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<?xml version='1.0' encoding='UTF-8'?>
<glyph name="view-comfy" format="2">
<advance width="1200"/>
<unicode hex="F0E6A"/>
<note>
view-comfy
</note>
<outline>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-comfy.glif/0
|
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| 1,333 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="view-grid" format="2">
<advance width="1200"/>
<unicode hex="F0570"/>
<note>
view-grid
</note>
<outline>
<contour>
<point x="20" y="1290" type="line"/>
<point x="20" y="773" type="line"/>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-grid.glif/0
|
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| 1,334 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="wall-sconce-flat-variant-outline" format="2">
<advance width="1200"/>
<unicode hex="F17CA"/>
<note>
wall-sconce-flat-variant-outline
</note>
<outline>
<contour>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/wall-sconce-flat-variant-outline.glif/0
|
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| 1,335 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="wallet" format="2">
<advance width="1200"/>
<unicode hex="F0584"/>
<note>
wallet
</note>
<outline>
<contour>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/wallet.glif/0
|
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| 1,336 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="weather-hurricane" format="2">
<advance width="1200"/>
<unicode hex="F0898"/>
<note>
weather-hurricane
</note>
<outline>
<contour>
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<point x="783" y="1177" type="qcurve" smooth="yes"/>
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/weather-hurricane.glif/0
|
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| 1,337 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="window-minimize" format="2">
<advance width="1200"/>
<unicode hex="F05B0"/>
<note>
window-minimize
</note>
<outline>
<contour>
<point x="1180" y="856" type="line"/>
<point x="20" y="856" type="line"/>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/window-minimize.glif/0
|
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| 1,338 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="zodiac-scorpio" format="2">
<advance width="1200"/>
<unicode hex="F0A86"/>
<note>
zodiac-scorpio
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<outline>
<contour>
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/zodiac-scorpio.glif/0
|
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| 1,339 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="POMODORO_TICKING" format="2">
<advance width="1200"/>
<unicode hex="E003"/>
<note>
POMODORO_TICKING
</note>
<outline>
<contour>
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cascadia-code/sources/nerdfonts/full/processed/Pomicons.ufo/glyphs/P_O_M_O_D_O_R_O__T_I_C_K_I_N_G_.glif/0
|
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| 1,340 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="uniE0B2" format="2">
<advance width="1200"/>
<unicode hex="E0B2"/>
<note>
uniE0B2
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</outline>
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cascadia-code/sources/nerdfonts/full/processed/PowerlineExtraSymbols.ufo/glyphs/uniE_0B_2.glif/0
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| 1,341 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="activate-breakpoints" format="2">
<advance width="1200"/>
<unicode hex="EA97"/>
<note>
activate-breakpoints
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cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/activate-breakpoints.glif/0
|
{
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| 1,342 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="check-all" format="2">
<advance width="1200"/>
<unicode hex="EBB1"/>
<note>
check-all
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<point x="321" y="472" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/check-all.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 552
}
| 1,343 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="debug-breakpoint-log-unverified" format="2">
<advance width="1200"/>
<unicode hex="EAAA"/>
<note>
debug-breakpoint-log-unverified
</note>
<outline>
<contour>
<point x="600" y="127" type="line"/>
<point x="1180" y="707" type="line"/>
<point x="600" y="1293" type="line"/>
<point x="20" y="707" type="line"/>
</contour>
<contour>
<point x="276" y="707" type="line"/>
<point x="600" y="1037" type="line"/>
<point x="924" y="707" type="line"/>
<point x="600" y="383" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/debug-breakpoint-log-unverified.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/debug-breakpoint-log-unverified.glif",
"repo_id": "cascadia-code",
"token_count": 287
}
| 1,344 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="debug-stackframe" format="2">
<advance width="1200"/>
<unicode hex="EB8B"/>
<note>
debug-stackframe
</note>
<outline>
<contour>
<point x="1180" y="627" type="line"/>
<point x="1180" y="793" type="line"/>
<point x="750" y="1271" type="line"/>
<point x="659" y="1314" type="line"/>
<point x="149" y="1314" type="line"/>
<point x="20" y="1185" type="line"/>
<point x="20" y="229" type="line"/>
<point x="149" y="106" type="line"/>
<point x="659" y="106" type="line"/>
<point x="750" y="149" type="line"/>
</contour>
<contour>
<point x="1083" y="707" type="line"/>
<point x="659" y="229" type="line"/>
<point x="149" y="229" type="line"/>
<point x="149" y="1185" type="line"/>
<point x="659" y="1185" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/debug-stackframe.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/debug-stackframe.glif",
"repo_id": "cascadia-code",
"token_count": 423
}
| 1,345 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="diff-renamed" format="2">
<advance width="1200"/>
<unicode hex="EAE0"/>
<note>
diff-renamed
</note>
<outline>
<contour>
<point x="20" y="1250" type="line"/>
<point x="20" y="170" type="line"/>
<point x="60" y="130" type="line"/>
<point x="1140" y="130" type="line"/>
<point x="1180" y="170" type="line"/>
<point x="1180" y="1250" type="line"/>
<point x="1140" y="1290" type="line"/>
<point x="60" y="1290" type="line"/>
</contour>
<contour>
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<point x="1100" y="1210" type="line"/>
<point x="1100" y="214" type="line"/>
<point x="104" y="214" type="line"/>
</contour>
<contour>
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<point x="268" y="626" type="line"/>
<point x="516" y="626" type="line"/>
<point x="516" y="378" type="line"/>
<point x="932" y="710" type="line"/>
<point x="516" y="1042" type="line"/>
<point x="516" y="794" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/diff-renamed.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/diff-renamed.glif",
"repo_id": "cascadia-code",
"token_count": 522
}
| 1,346 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="file-code" format="2">
<advance width="1200"/>
<unicode hex="EAE9"/>
<unicode hex="EAFB"/>
<note>
file-code
</note>
<outline>
<contour>
<point x="814" y="1385" type="line"/>
<point x="66" y="1385" type="line"/>
<point x="20" y="1339" type="line"/>
<point x="20" y="81" type="line"/>
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</contour>
<contour>
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<point x="1082" y="999" type="line"/>
<point x="793" y="999" type="line"/>
</contour>
<contour>
<point x="695" y="1293" type="line"/>
<point x="695" y="952" type="line"/>
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<point x="1082" y="906" type="line"/>
<point x="1082" y="133" type="line"/>
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<point x="113" y="1293" type="line"/>
</contour>
<contour>
<point x="489" y="385" type="line"/>
<point x="314" y="560" type="line"/>
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<point x="211" y="530" type="line"/>
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</contour>
<contour>
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<point x="675" y="385" type="line"/>
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<point x="953" y="597" type="line"/>
<point x="742" y="808" type="line"/>
<point x="675" y="736" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/file-code.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/file-code.glif",
"repo_id": "cascadia-code",
"token_count": 875
}
| 1,347 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="folder-library" format="2">
<advance width="1200"/>
<unicode hex="EBDF"/>
<note>
folder-library
</note>
<outline>
<contour>
<point x="507" y="1195" type="line"/>
<point x="476" y="1208" type="line"/>
<point x="60" y="1208" type="line"/>
<point x="20" y="1164" type="line"/>
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<point x="100" y="792" type="line"/>
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<point x="573" y="876" type="line"/>
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</contour>
<contour>
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<point x="1096" y="956" type="line"/>
<point x="556" y="956" type="line"/>
</contour>
<contour>
<point x="600" y="212" type="line"/>
<point x="684" y="212" type="line"/>
<point x="684" y="708" type="line"/>
<point x="600" y="708" type="line"/>
</contour>
<contour>
<point x="768" y="212" type="line"/>
<point x="848" y="212" type="line"/>
<point x="848" y="708" type="line"/>
<point x="768" y="708" type="line"/>
</contour>
<contour>
<point x="1105" y="212" type="line"/>
<point x="1180" y="238" type="line"/>
<point x="1012" y="708" type="line"/>
<point x="932" y="677" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/folder-library.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/folder-library.glif",
"repo_id": "cascadia-code",
"token_count": 1005
}
| 1,348 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="mail-read" format="2">
<advance width="1200"/>
<unicode hex="EB1B"/>
<note>
mail-read
</note>
<outline>
<contour>
<point x="1180" y="894" type="line"/>
<point x="622" y="1226" type="line"/>
<point x="578" y="1226" type="line"/>
<point x="20" y="894" type="line"/>
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<point x="1180" y="234" type="line"/>
</contour>
<contour>
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<point x="1069" y="863" type="line"/>
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<point x="246" y="734" type="line"/>
</contour>
<contour>
<point x="1100" y="779" type="line"/>
<point x="1100" y="274" type="line"/>
<point x="104" y="274" type="line"/>
<point x="104" y="779" type="line"/>
<point x="193" y="664" type="line"/>
<point x="228" y="650" type="line"/>
<point x="972" y="650" type="line"/>
<point x="1007" y="664" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/mail-read.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/mail-read.glif",
"repo_id": "cascadia-code",
"token_count": 565
}
| 1,349 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="redo" format="2">
<advance width="1200"/>
<unicode hex="EBB0"/>
<note>
redo
</note>
<outline>
<contour>
<point x="1013" y="1420" type="line"/>
<point x="1013" y="1193" type="line"/>
<point x="938" y="1269" type="line" smooth="yes"/>
<point x="839" y="1368"/>
<point x="583" y="1432"/>
<point x="326" y="1368"/>
<point x="134" y="1176"/>
<point x="65" y="920"/>
<point x="134" y="663"/>
<point x="228" y="565" type="qcurve" smooth="yes"/>
<point x="798" y="0" type="line"/>
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<point x="309" y="646" type="line" smooth="yes"/>
<point x="233" y="716"/>
<point x="175" y="920"/>
<point x="233" y="1123"/>
<point x="379" y="1269"/>
<point x="583" y="1321"/>
<point x="780" y="1269"/>
<point x="856" y="1193" type="qcurve" smooth="yes"/>
<point x="961" y="1088" type="line"/>
<point x="681" y="1088" type="line"/>
<point x="681" y="984" type="line"/>
<point x="1066" y="984" type="line"/>
<point x="1118" y="1036" type="line"/>
<point x="1118" y="1420" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/redo.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 613
}
| 1,350 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="report" format="2">
<advance width="1200"/>
<unicode hex="EB42"/>
<note>
report
</note>
<outline>
<contour>
<point x="20" y="1246" type="line"/>
<point x="20" y="413" type="line"/>
<point x="60" y="374" type="line"/>
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<point x="1180" y="1246" type="line"/>
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<point x="60" y="1286" type="line"/>
</contour>
<contour>
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<point x="1100" y="458" type="line"/>
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</contour>
<contour>
<point x="560" y="706" type="line"/>
<point x="640" y="706" type="line"/>
<point x="640" y="1122" type="line"/>
<point x="560" y="1122" type="line"/>
</contour>
<contour>
<point x="560" y="621" type="line"/>
<point x="560" y="542" type="line"/>
<point x="640" y="542" type="line"/>
<point x="640" y="621" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/report.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 735
}
| 1,351 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="star-empty" format="2">
<advance width="1200"/>
<unicode hex="EA6A"/>
<note>
star-empty
</note>
<outline>
<contour>
<point x="1180" y="856" type="line"/>
<point x="733" y="856" type="line"/>
<point x="600" y="1290" type="line"/>
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</contour>
<contour>
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</contour>
<contour>
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<point x="423" y="586" type="line"/>
</contour>
<contour>
<point x="706" y="816" type="line"/>
<point x="1069" y="816" type="line"/>
</contour>
<contour>
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<point x="759" y="418" type="line"/>
<point x="600" y="542" type="line"/>
<point x="441" y="418" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/star-empty.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/star-empty.glif",
"repo_id": "cascadia-code",
"token_count": 740
}
| 1,352 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="symbol-keyword" format="2">
<advance width="1200"/>
<unicode hex="EB62"/>
<note>
symbol-keyword
</note>
<outline>
<contour>
<point x="1180" y="1124" type="line"/>
<point x="768" y="1124" type="line"/>
<point x="768" y="1040" type="line"/>
<point x="1180" y="1040" type="line"/>
</contour>
<contour>
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<point x="1100" y="792" type="line"/>
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<point x="932" y="708" type="line"/>
</contour>
<contour>
<point x="768" y="708" type="line"/>
<point x="768" y="792" type="line"/>
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<contour>
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<point x="20" y="292" type="line"/>
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<contour>
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<point x="20" y="544" type="line"/>
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</contour>
<contour>
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<point x="1180" y="544" type="line"/>
<point x="768" y="544" type="line"/>
<point x="768" y="460" type="line"/>
</contour>
<contour>
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<point x="20" y="956" type="line"/>
<point x="600" y="956" type="line"/>
<point x="600" y="1208" type="line"/>
</contour>
<contour>
<point x="516" y="1040" type="line"/>
<point x="104" y="1040" type="line"/>
<point x="104" y="1124" type="line"/>
<point x="516" y="1124" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/symbol-keyword.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 869
}
| 1,353 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="verified" format="2">
<advance width="1200"/>
<unicode hex="EB77"/>
<note>
verified
</note>
<outline>
<contour>
<point x="427" y="276" type="line"/>
<point x="570" y="128" type="line"/>
<point x="630" y="128" type="line"/>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/verified.glif/0
|
{
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"repo_id": "cascadia-code",
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}
| 1,354 |
<?xml version='1.0' encoding='UTF-8'?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>ascender</key>
<integer>410</integer>
<key>capHeight</key>
<integer>0</integer>
<key>copyright</key>
<string>Copyright (c) , Lukas W</string>
<key>descender</key>
<integer>-102</integer>
<key>familyName</key>
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cascadia-code/sources/nerdfonts/full/processed/font-logos.ufo/glyphs/contents.plist/0
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{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/font-logos.ufo/glyphs/contents.plist",
"repo_id": "cascadia-code",
"token_count": 3778
}
| 1,358 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="i_seti_csv" format="2">
<advance width="1200"/>
<unicode hex="E64A"/>
<note>
i_seti_csv
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</outline>
<lib>
<dict>
<key>com.schriftgestaltung.Glyphs.lastChange</key>
<string>2024-02-27 18:42:08 +0000</string>
</dict>
</lib>
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|
cascadia-code/sources/nerdfonts/full/processed/original-source.ufo/glyphs/i_seti_csv.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/original-source.ufo/glyphs/i_seti_csv.glif",
"repo_id": "cascadia-code",
"token_count": 1042
}
| 1,359 |
{
"name": "Causica Devcontainer",
"build": {
"context": "..",
"dockerfile": "../Dockerfile",
"target": "dev"
},
"runArgs": [
// give headspace for pytorch dataloaders passing tensors across processes
"--shm-size=1gb"
],
"containerEnv": {
"DOCKER_BUILDKIT": "1"
},
"mounts": [
"source=command_history,target=/root/command_history,type=volume", // Persist command history across instances
"target=/root/.vscode-server,type=volume" // Persist vscode server across instances
],
"hostRequirements": {
"gpu": "optional" // Mount GPU(s) if available
},
"postCreateCommand": "pre-commit install",
"postStartCommand": "git config --global core.editor \"code --wait\" && poetry install",
"shutdownAction": "none",
"customizations": {
"vscode": {
"settings": {
// General
"editor.rulers": [120],
"terminal.integrated.profiles.linux": {
"zsh": {"path": "/usr/bin/zsh"}
},
"terminal.integrated.defaultProfile.linux": "zsh",
// Python
"[python]": {
"editor.defaultFormatter": "ms-python.black-formatter"
},
"python.defaultInterpreterPath": "/usr/bin/python",
"python.testing.pytestEnabled": true,
"python.testing.pytestArgs": [
"--continue-on-collection-errors",
"test"
],
"python.testing.unittestEnabled": false,
// Extensions
"isort.args": ["--resolve-all-configs"],
"black-formatter.args": ["--config=./pyproject.toml"],
"vim.textwidth": 120
},
"extensions": [
"bungcip.better-toml",
"github.copilot",
"github.copilot-chat",
"github.copilot-labs",
"ms-azuretools.vscode-docker",
"ms-python.black-formatter",
"ms-python.isort",
"ms-python.pylint",
"ms-python.python",
"ms-python.vscode-pylance",
"ms-toolsai.jupyter",
"redhat.vscode-yaml",
"vscodevim.vim"
]
}
}
}
|
causica/.devcontainer/devcontainer.json/0
|
{
"file_path": "causica/.devcontainer/devcontainer.json",
"repo_id": "causica",
"token_count": 806
}
| 1,360 |
# Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
Resources:
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [[email protected]](mailto:[email protected]) with questions or concerns
|
causica/CODE_OF_CONDUCT.md/0
|
{
"file_path": "causica/CODE_OF_CONDUCT.md",
"repo_id": "causica",
"token_count": 115
}
| 1,361 |
import torch
from tensordict import TensorDict
def check_temporal_tensordict_shapes(num_timepoints: int, tds: TensorDict) -> bool:
"""Check that the shapes within the TensorDict are consistent with the number of time points."""
return all(val.shape[len(tds.batch_size)] == num_timepoints for val in tds.values())
def temporal_tensordict_shapes(tds: TensorDict) -> dict[str, torch.Size]:
"""Return the shapes within the TensorDict without batch and time dimensions."""
return {key: val.shape[len(tds.batch_size) + 1 :] for key, val in tds.items()}
|
causica/research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py/0
|
{
"file_path": "causica/research_experiments/scotch/src/scotch/scotch_utils/scotch_utils.py",
"repo_id": "causica",
"token_count": 192
}
| 1,362 |
import math
import numpy as np
import torch
import torch.distributions as td
from causica.distributions.adjacency.adjacency_distributions import AdjacencyDistribution
from causica.triangular_transformations import fill_triangular
class ErdosRenyiDAGDistribution(AdjacencyDistribution):
"""
An adjacency distribution for sampling Directed Acyclic Graphs using Erdos Renyi.
"""
support = td.constraints.independent(td.constraints.boolean, 1)
arg_constraints = {"probs": td.constraints.unit_interval}
def __init__(
self,
num_nodes: int,
probs: torch.Tensor | None = None,
num_edges: torch.Tensor | None = None,
validate_args: bool | None = None,
):
"""
Args:
num_nodes: the number of nodes in the DAGs to be sampled
probs: A tensor of the probability that an edge exists between 2 nodes of shape batch_shape. If None, calculate p from num_edges
num_edges: The number of edges to sample. If None, sample from a binomial distribution with p=probs
"""
assert num_nodes > 0, "Number of nodes in the graph must be greater than 0"
if num_edges is not None:
if probs is not None:
raise ValueError("Only one of probs or num_edges must be provided")
# Calculate the probability of an edge existing based on the number of edges and the number of possible edges
probs = torch.minimum(num_edges / math.comb(num_nodes, 2), torch.ones_like(num_edges))
elif probs is None:
raise ValueError("Either probs or num_edges must be provided")
self.probs = probs
super().__init__(num_nodes=num_nodes, validate_args=validate_args)
self.num_low_tri = num_nodes * (num_nodes - 1) // 2
expanded_probs = self.probs[..., None].expand(
*self.probs.shape + (self.num_low_tri,)
) # shape batch_shape + (num_low_tri)
self.bern_dist = td.Independent(td.Bernoulli(probs=expanded_probs), reinterpreted_batch_ndims=1)
self.np_rng = np.random.default_rng()
def relaxed_sample(self, sample_shape: torch.Size = torch.Size(), temperature: float = 0.0) -> torch.Tensor:
raise NotImplementedError
def sample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
"""
Sample a binary adjacency matrix from the underlying distribution.
Args:
sample_shape: the shape of the samples to return
Returns:
A tensor of shape sample_shape + batch_shape + (num_nodes, num_nodes)
"""
# generate the lower triangle shape [..., n, n]
low_tri = fill_triangular(self.bern_dist.sample(sample_shape=sample_shape))
# generate a random permutation matrix
aranges = np.tile(np.arange(self.num_nodes), sample_shape + self.probs.shape + (1,)) # shape [..., n]
np_perms = torch.tensor(self.np_rng.permuted(aranges, axis=-1)) # a batch of rearranged [0, 1, 2... n]
# one hot the last dimension to create a tensor of shape [..., n, n]
perms = torch.nn.functional.one_hot(np_perms, num_classes=self.num_nodes).to(
dtype=low_tri.dtype, non_blocking=True
)
return torch.einsum("...ij,...jk,...lk->...il", perms, low_tri, perms)
def entropy(self) -> torch.Tensor:
raise NotImplementedError
@property
def mean(self) -> torch.Tensor:
raise NotImplementedError
@property
def mode(self) -> torch.Tensor:
"""
Return the mode of the underlying distribution.
This will be an adjacency matrix.
For this distribution, there are many modes since each permutation of nodes is equally likely.
We return the mode corresponding to the "default" ordering.
There are 2 possibilities:
p > 0.5: A lower triangular matrix of ones
p <= 0.5: A matrix of zeros
Returns:
A tensor of shape batch_shape + (num_nodes, num_nodes)
"""
return fill_triangular(torch.nan_to_num(self.bern_dist.mode, nan=0.0))
def log_prob(self, value: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
|
causica/src/causica/distributions/adjacency/directed_acyclic.py/0
|
{
"file_path": "causica/src/causica/distributions/adjacency/directed_acyclic.py",
"repo_id": "causica",
"token_count": 1716
}
| 1,363 |
from typing import Optional
import torch
import torch.distributions as td
from torch import nn
from causica.distributions.noise.noise import IndependentNoise, Noise, NoiseModule
class UnivariateCauchyNoise(td.Cauchy, Noise[torch.Tensor]):
def sample_to_noise(self, samples: torch.Tensor) -> torch.Tensor:
"""
Transform from the sample observations to corresponding noise variables.
Args:
samples: Tensor of shape sample_shape + batch_shape + event_shape
Returns:
The generated samples with shape sample_shape + batch_shape + event_shape
"""
return samples - self.loc
def noise_to_sample(self, noise: torch.Tensor) -> torch.Tensor:
"""
Generate samples using the given exogenous noise.
Args:
noise: noise variable with shape sample_shape + batch_shape.
Returns:
The generated samples with shape sample_shape + batch_shape + event_shape
"""
return noise + self.loc
class UnivariateCauchyNoiseModule(NoiseModule[IndependentNoise[UnivariateCauchyNoise]]):
"""Represents a UnivariateCauchyNoise with learnable parameters for independent variables."""
def __init__(self, dim: int, init_log_scale: float | torch.Tensor = 0.0):
"""
Args:
dim: Number of dimensions for the NormalNoise.
"""
super().__init__()
if isinstance(init_log_scale, torch.Tensor):
if init_log_scale.squeeze().ndim == 0:
init_log_scale = torch.full(torch.Size([dim]), init_log_scale.item())
else:
assert init_log_scale.ndim == 1
assert init_log_scale.shape[0] == dim
else:
init_log_scale = torch.full(torch.Size([dim]), init_log_scale)
self.log_scale = nn.Parameter(init_log_scale)
def forward(self, x: Optional[torch.Tensor] = None) -> IndependentNoise[UnivariateCauchyNoise]:
if x is None:
x = torch.zeros_like(self.log_scale)
return IndependentNoise(UnivariateCauchyNoise(loc=x, scale=torch.exp(self.log_scale)), 1)
|
causica/src/causica/distributions/noise/univariate_cauchy.py/0
|
{
"file_path": "causica/src/causica/distributions/noise/univariate_cauchy.py",
"repo_id": "causica",
"token_count": 878
}
| 1,364 |
import pytest
import torch
import torch.distributions as td
from causica.data_generation.samplers.functional_relationships_sampler import LinearRelationshipsSampler
from causica.data_generation.samplers.noise_dist_sampler import (
BernoulliNoiseModuleSampler,
CategoricalNoiseModuleSampler,
JointNoiseModuleSampler,
UnivariateNormalNoiseModuleSampler,
)
from causica.data_generation.samplers.sem_sampler import SEMSampler
from causica.datasets.interventional_data import InterventionData
from causica.datasets.synthetic_dataset import CausalMetaset
from causica.distributions import ErdosRenyiDAGDistribution
@pytest.fixture(name="mixture_sem_sampler_and_shapes")
def fixture_mixture_sem_sampler_and_shapes() -> tuple[SEMSampler, dict[str, torch.Size]]:
shapes_dict = {
"x_0": torch.Size([1]),
"x_1": torch.Size([5]),
"x_2": torch.Size([1]),
"x_3": torch.Size([3]),
"x_4": torch.Size([4]),
}
noise_dist_samplers = {
"x_0": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1),
"x_1": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=5),
"x_2": BernoulliNoiseModuleSampler(base_logits_dist=td.Uniform(low=0.2, high=2.0), dim=1),
"x_3": BernoulliNoiseModuleSampler(
base_logits_dist=td.Uniform(low=0.2 * torch.ones([3]), high=2.0 * torch.ones([3])), dim=3
),
"x_4": CategoricalNoiseModuleSampler(
base_logits_dist=td.Uniform(low=0.2 * torch.ones([4]), high=2.0 * torch.ones([4])), num_classes=4
),
}
# Create adjacency distribution, joint noise module sampler, and functional relationships sampler
adjacency_dist = ErdosRenyiDAGDistribution(num_nodes=5, num_edges=torch.tensor(10))
joint_noise_module_sampler = JointNoiseModuleSampler(noise_dist_samplers)
initial_linear_coefficient_matrix_shape = sum(shape[0] for shape in shapes_dict.values())
functional_relationships_sampler = LinearRelationshipsSampler(
td.Uniform(
low=torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
high=3.0 * torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
),
shapes_dict,
)
return SEMSampler(adjacency_dist, joint_noise_module_sampler, functional_relationships_sampler), shapes_dict
def test_mixed_type_causal_metaset(mixture_sem_sampler_and_shapes: tuple[SEMSampler, dict[str, torch.Size]]):
mixture_sem_sampler, shapes_dict = mixture_sem_sampler_and_shapes
metaset = CausalMetaset(mixture_sem_sampler, 5, 7, 1, 2, sample_interventions=True)
count = 0
for sample in metaset: # type: ignore
count += 1
assert sample.observations.batch_size == torch.Size([5])
assert sample.noise.batch_size == torch.Size([5])
for v, shape in shapes_dict.items():
assert sample.observations[v].shape == torch.Size([5]) + shape
assert sample.noise[v].shape == torch.Size([5]) + shape
assert sample.graph.shape == torch.Size([5, 5])
assert len(sample.interventions) == 1
assert isinstance(sample.interventions[0][0], InterventionData)
assert all((sample.observations["x_2"].unique()[:, None] == torch.tensor([0.0, 1.0])).any(dim=1))
assert count == len(metaset)
|
causica/test/datasets/test_synthetic_dataset.py/0
|
{
"file_path": "causica/test/datasets/test_synthetic_dataset.py",
"repo_id": "causica",
"token_count": 1393
}
| 1,365 |
import pytest
import torch
from causica.distributions.noise import UnivariateLaplaceNoise
@pytest.mark.parametrize(("batch", "dimension"), [(1, 10), (2, 5)])
def test_init(batch, dimension):
mean = torch.randn((batch, dimension))
scale = torch.ones((batch, dimension))
noise_model = UnivariateLaplaceNoise(loc=mean, scale=scale)
assert noise_model.loc.shape == (batch, dimension)
assert noise_model.scale.shape == (batch, dimension)
def test_sample_to_noise():
# batch size 1
obs = torch.Tensor([[10, 15]])
pred = torch.Tensor([[5, 10]])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.sample_to_noise(obs), torch.Tensor([[5, 5]]))
# no batch size
obs = torch.Tensor([10, 15])
pred = torch.Tensor([5, 10])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.sample_to_noise(obs), torch.Tensor([5, 5]))
# batch size 2
obs = torch.Tensor([[10, 15], [20, 25]])
pred = torch.Tensor([[5, 10], [18, 19]])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.sample_to_noise(obs), torch.Tensor([[5, 5], [2, 6]]))
def test_noise_to_sample():
# batch size 1
noise = torch.Tensor([[5, 5]])
pred = torch.Tensor([[5, 12]])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.noise_to_sample(noise), torch.Tensor([[10, 17]]))
# no batch size
noise = torch.Tensor([5, 5])
pred = torch.Tensor([5, 12])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.noise_to_sample(noise), torch.Tensor([10, 17]))
# batch size 2
noise = torch.Tensor([[5, 5], [2, 6]])
pred = torch.Tensor([[5, 12], [18, 19]])
noise_model = UnivariateLaplaceNoise(loc=pred, scale=torch.ones_like(pred))
assert torch.allclose(noise_model.noise_to_sample(noise), torch.Tensor([[10, 17], [20, 25]]))
|
causica/test/distributions/noise/test_univariate_laplace.py/0
|
{
"file_path": "causica/test/distributions/noise/test_univariate_laplace.py",
"repo_id": "causica",
"token_count": 843
}
| 1,366 |
import os
from pathlib import Path
import mlflow
import pytest
import yaml
from jsonargparse import Namespace
from mlflow import ActiveRun
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.cli import LightningArgumentParser
from pytorch_lightning.trainer.states import TrainerFn
from causica.lightning.callbacks import MLFlowSaveConfigCallback
@pytest.fixture(name="local_mlflow_run", scope="function")
def local_mlflow_run_fn(tmp_path: Path) -> ActiveRun:
"""Return an mlflow run tracking to a temporary directory."""
mlflow.set_tracking_uri(tmp_path)
experiment_id = mlflow.create_experiment("experiment")
return mlflow.start_run(experiment_id=experiment_id)
def test_mlflow_save_config_callback(local_mlflow_run: ActiveRun):
"""Test that the config is successfully saved to mlflow."""
namespace = Namespace(foo="bar", nested={"baz": "foobar"})
parser = LightningArgumentParser()
for key in namespace.keys():
parser.add_argument(f"--{key}")
with local_mlflow_run:
callback = MLFlowSaveConfigCallback(parser=parser, config=namespace, config_filename="config.yaml")
callback.setup(Trainer(), LightningModule(), stage=TrainerFn.FITTING)
base_artifact_uri = local_mlflow_run.info.artifact_uri
config_artifact_uri = os.path.join(base_artifact_uri, callback.config_filename)
assert yaml.safe_load(mlflow.artifacts.load_text(config_artifact_uri)) == namespace.as_dict()
|
causica/test/lightning/test_callbacks.py/0
|
{
"file_path": "causica/test/lightning/test_callbacks.py",
"repo_id": "causica",
"token_count": 503
}
| 1,367 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import sys
if sys.version_info >= (3, 8):
from importlib import metadata
else:
import importlib_metadata as metadata
__version__ = metadata.version("cliffordlayers")
|
cliffordlayers/cliffordlayers/__init__.py/0
|
{
"file_path": "cliffordlayers/cliffordlayers/__init__.py",
"repo_id": "cliffordlayers",
"token_count": 74
}
| 1,368 |
import torch
import torch.nn.functional as F
from torch.nn.modules.utils import _single, _pair, _triple
def get_clifford_g3_kernel(weights):
assert len(weights) == 6
scale = weights[4]
square_1 = weights[0] * weights[0]
square_2 = weights[1] * weights[1]
square_3 = weights[2] * weights[2]
square_4 = weights[3] * weights[3]
norm = torch.sqrt(square_1 + square_2 + square_3 + square_4 + 0.0001)
weight_1_n = weights[0] / norm
weight_2_n = weights[1] / norm
weight_3_n = weights[2] / norm
weight_4_n = weights[3] / norm
norm_factor = 2.0
square_2 = norm_factor * (weight_2_n * weight_2_n)
square_3 = norm_factor * (weight_3_n * weight_3_n)
square_4 = norm_factor * (weight_4_n * weight_4_n)
rot12 = norm_factor * weight_1_n * weight_2_n
rot13 = norm_factor * weight_1_n * weight_3_n
rot14 = norm_factor * weight_1_n * weight_4_n
rot23 = norm_factor * weight_2_n * weight_3_n
rot24 = norm_factor * weight_2_n * weight_4_n
rot34 = norm_factor * weight_3_n * weight_4_n
rot_kernel2 = torch.cat(
[
scale * (1.0 - (square_3 + square_4)),
scale * (rot23 - rot14),
scale * (rot24 + rot13),
],
dim=1,
)
rot_kernel3 = torch.cat(
[
scale * (rot23 + rot14),
scale * (1.0 - (square_2 + square_4)),
scale * (rot34 - rot12),
],
dim=1,
)
rot_kernel4 = torch.cat(
[
scale * (rot24 - rot13),
scale * (rot34 + rot12),
scale * (1.0 - (square_2 + square_3)),
],
dim=1,
)
kernel = torch.cat([rot_kernel2, rot_kernel3, rot_kernel4], dim=0)
return kernel
def clifford_g3convnd(
input,
weights,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
transposed=False,
):
kernel = get_clifford_g3_kernel(weights)
if bias is not None:
bias = torch.cat([bias[0], bias[1], bias[2]], dim=0)
if input.dim() == 3:
convfunc = F.conv_transpose1d if transposed else F.conv1d
padding = _single(padding)
dilation = _single(dilation)
stride = _single(stride)
elif input.dim() == 4:
convfunc = F.conv_transpose2d if transposed else F.conv2d
padding = padding if isinstance(padding, str) else _pair(padding)
dilation = _pair(dilation)
stride = _pair(stride)
elif input.dim() == 5:
convfunc = F.conv_transpose3d if transposed else F.conv3d
padding = _triple(padding)
dilation = _triple(dilation)
stride = _triple(stride)
else:
raise NotImplementedError("input must be 3D, 4D or 5D")
return convfunc(input, kernel, bias, stride, padding=padding, dilation=dilation, groups=groups)
|
cliffordlayers/cliffordlayers/nn/functional/cliffordg3conv.py/0
|
{
"file_path": "cliffordlayers/cliffordlayers/nn/functional/cliffordg3conv.py",
"repo_id": "cliffordlayers",
"token_count": 1316
}
| 1,369 |
[project]
name = "cliffordlayers"
dynamic = ["version"]
description = "A PyTorch library for Clifford layers"
readme = "README.md"
requires-python = ">=3.8"
license = {text = "MIT"}
authors = [{name = "Jayesh K. Gupta", email = "[email protected]"}, {name="Johannes Brandstetter", email="[email protected]"}, {name="David Ruhe", email="[email protected]"}]
dependencies = ["torch"]
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Development Status :: 4 - Beta",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
]
[project.optional-dependencies]
dev = ["pytest"]
[project.urls]
homepage = "https://microsoft.github.io/cliffordlayers"
repository = "https://github.com/microsoft/cliffordlayers"
[build-system]
build-backend = "setuptools.build_meta"
requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2", "wheel"]
[tool.setuptools]
packages = ["cliffordlayers"]
[tool.setuptools_scm]
[tool.ruff]
ignore = ['E501', 'E741']
[tool.black]
line-length = 120
|
cliffordlayers/pyproject.toml/0
|
{
"file_path": "cliffordlayers/pyproject.toml",
"repo_id": "cliffordlayers",
"token_count": 420
}
| 1,370 |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
|
cookiecutter-spacy-fastapi/{{cookiecutter.project_slug}}/app/tests/__init__.py/0
|
{
"file_path": "cookiecutter-spacy-fastapi/{{cookiecutter.project_slug}}/app/tests/__init__.py",
"repo_id": "cookiecutter-spacy-fastapi",
"token_count": 21
}
| 1,371 |
build/
node_modules/
dist/
coverage/
# Client
client/build/
client/node_modules/
client/dist/
client/coverage/
|
0xDeCA10B/demo/.eslintignore/0
|
{
"file_path": "0xDeCA10B/demo/.eslintignore",
"repo_id": "0xDeCA10B",
"token_count": 42
}
| 0 |
{
"name": "decai-demo-client",
"version": "1.4.5",
"license": "MIT",
"private": true,
"proxy": "http://localhost:5387/",
"dependencies": {
"@drizzle-utils/get-web3": "^0.2.2-alpha.0",
"@material-ui/core": "^4.7.1",
"@material-ui/icons": "^4.5.1",
"@tensorflow-models/mobilenet": "^2.0.4",
"@tensorflow-models/universal-sentence-encoder": "^1.2.0",
"@tensorflow/tfjs": "^1.3.2",
"@types/jest": "^24.0.23",
"@types/murmurhash-js": "^1.0.3",
"@types/node": "^12.12.14",
"@types/react": "^16.9.55",
"@types/react-dom": "^16.9.9",
"axios": "^0.21.2",
"blueimp-load-image": "^2.24.0",
"canvas": "^2.6.0",
"dotenv": "^8.2.0",
"immutability-helper": "^3.0.1",
"moment": "^2.29.4",
"murmurhash-js": "^1.0.0",
"notistack": "^0.9.7",
"react": "^16.14.0",
"react-dom": "^16.14.0",
"react-dropzone": "^10.2.1",
"react-router-dom": "^5.1.2",
"react-scripts": "^3.4.4",
"react-spinners": "^0.6.1",
"serve": "^11.2.0",
"truffle": "^5.1.50",
"typescript": "^3.7.3",
"web3": "^1.3.0",
"web3-eth-contract": "^1.3.0"
},
"scripts": {
"start": "bash deploy_client.sh",
"start-prod": "bash start_prod.sh",
"build": "react-scripts build --expose-gc --max_old_space_size=8192",
"test": "bash test.sh",
"train-vpa-classifier": "node src/ml-models/vpa/train-classifier.js",
"train-hot-dog-classifier": "node src/ml-models/hot_dog-not/train-classifier.js",
"eject": "react-scripts eject"
},
"browserslist": [
">0.2%",
"not dead",
"not ie <= 11",
"not op_mini all"
],
"devDependencies": {
"@tensorflow/tfjs-node": "^1.3.2",
"fake-indexeddb": "^3.0.0",
"node-fetch": "^2.6.7",
"ts-node": "^9.1.1"
}
}
|
0xDeCA10B/demo/client/package.json/0
|
{
"file_path": "0xDeCA10B/demo/client/package.json",
"repo_id": "0xDeCA10B",
"token_count": 933
}
| 1 |
pragma solidity ^0.6;
pragma experimental ABIEncoderV2;
import "../../../lib/SafeMath.sol";
import "../../../lib/SignedSafeMath.sol";
import {Ownable} from "../ownership/Ownable.sol";
/**
* A classifier that can take a data sample as input and return a predict classification/label for the data.
*/
contract Classifier is Ownable {
/**
* The possible classifications that can be predicted.
* Prediction results are indices to values in this array.
*/
string[] public classifications;
/**
* The indices of the features to use from some well-known shared encoder.
* If it is empty, then all features are used.
*/
uint32[] public featureIndices;
/**
* Initialize a classifier.
* @param _classifications The possible classifications that can be predicted.
*/
constructor(string[] memory _classifications)
Ownable()
public {
classifications = _classifications;
}
/**
* @return The number of possible classifications that can be predicted.
*/
function getNumClassifications() public view returns (uint) {
return classifications.length;
}
/**
* Add more feature indices to take from the encoded result.
* Made to be called just after the contract is created and never again.
* @param _featureIndices The feature indices to append to the array held by this contract.
*/
function addFeatureIndices(uint32[] memory _featureIndices) public onlyOwner {
for (uint32 i = 0; i < _featureIndices.length; ++i) {
featureIndices.push(_featureIndices[i]);
}
}
/**
* @return The number of feature indices to use. 0 means to use all feature from the encoding.
*/
function getNumFeatureIndices() public view returns (uint) {
return featureIndices.length;
}
}
/**
* A `Classifier` for data with 64-bit values.
*/
// Use an abstract contract instead of an interface so that we can enforce internal functions
// and not be forced to have some external.
abstract contract Classifier64 is Classifier {
using SafeMath for uint256;
using SignedSafeMath for int256;
/**
* Number of decimal places precision.
* Since Solidity does not support floating point numbers,
* we assume certain numbers have been multiplied by this value.
*/
uint32 constant public toFloat = 1E9;
constructor(string[] memory _classifications)
Classifier(_classifications)
internal {
// solium-disable-previous-line no-empty-blocks
}
/**
* @param data A single sample.
* @return The normalization scalar for `data`.
*/
function norm(int64[] memory data) public virtual pure returns (uint);
/**
* @param data A single sample.
* @return The predicted classification/label for `data`.
*/
function predict(int64[] memory data) public virtual view returns (uint64);
/**
* Train the classifier with one data sample.
*
* @param data The training data or features.
* @param classification The label for `data`.
*/
function update(int64[] memory data, uint64 classification) public virtual;
}
|
0xDeCA10B/demo/client/src/contracts/classification/Classifier.sol/0
|
{
"file_path": "0xDeCA10B/demo/client/src/contracts/classification/Classifier.sol",
"repo_id": "0xDeCA10B",
"token_count": 1033
}
| 2 |
import * as _getWeb3 from '@drizzle-utils/get-web3'
import Web3 from 'web3' // Only required for custom/fallback provider option.
export async function getWeb3() {
if (typeof window !== "undefined" && window.ethereum) {
// Get rid of a warning about network refreshing.
window.ethereum.autoRefreshOnNetworkChange = false
}
// Fallback to Ethereum mainnet.
// Address copied from MetaMask.
const fallbackProvider = new Web3.providers.HttpProvider("https://api.infura.io/v1/jsonrpc/mainnet")
const result = await _getWeb3({ fallbackProvider, requestPermission: true })
return result
}
export async function getNetworkType() {
if (typeof window !== "undefined" && window.ethereum) {
// Get rid of a warning about network refreshing.
window.ethereum.autoRefreshOnNetworkChange = false
}
return _getWeb3().then(web3 => {
return web3.eth.net.getNetworkType()
}).catch(err => {
console.warn("Error getting the network type.")
console.warn(err)
alert("Could not find an Ethereum wallet provider so mainnet will be used")
// Assume mainnet
return 'main'
})
}
|
0xDeCA10B/demo/client/src/getWeb3.js/0
|
{
"file_path": "0xDeCA10B/demo/client/src/getWeb3.js",
"repo_id": "0xDeCA10B",
"token_count": 350
}
| 3 |
// Basically the same as tensor-utils.js but made for back-end.
// There are fancier ways to share code for front-end and back-end
// but they all seemed too complicated and unreliable in all cases.
const tf = require('@tensorflow/tfjs-node')
exports.normalize1d = function (x) {
return tf.tidy(_ => {
if (!(x instanceof tf.Tensor)) {
x = tf.tensor(x)
}
const ord = 2
const norm = tf.norm(x, ord)
if (norm.dataSync()[0] === 0) {
return x
}
return x.div(norm)
})
}
exports.normalize2d = function (x) {
return tf.tidy(_ => {
const ord = 2
const axis = 1
const norms = tf.norm(x, ord, axis).expandDims(1)
return x.div(norms)
})
}
exports.normalizeArray = function (data) {
return tf.tidy(_ => {
return exports.normalize1d(data).arraySync()
})
}
|
0xDeCA10B/demo/client/src/ml-models/tensor-utils-node.js/0
|
{
"file_path": "0xDeCA10B/demo/client/src/ml-models/tensor-utils-node.js",
"repo_id": "0xDeCA10B",
"token_count": 311
}
| 4 |
const util = require('util')
exports.setTimeoutPromise = util.promisify(setTimeout)
|
0xDeCA10B/demo/client/src/utils.js/0
|
{
"file_path": "0xDeCA10B/demo/client/src/utils.js",
"repo_id": "0xDeCA10B",
"token_count": 27
}
| 5 |
{
"compilerOptions": {
"target": "es2020",
"lib": [
"dom",
"dom.iterable",
"esnext"
],
"allowJs": true,
"skipLibCheck": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true,
"strict": true,
"forceConsistentCasingInFileNames": true,
"module": "esnext",
"moduleResolution": "node",
"resolveJsonModule": true,
"isolatedModules": true,
"noEmit": true,
"jsx": "preserve"
},
"include": [
"src"
]
}
|
0xDeCA10B/demo/client/tsconfig.json/0
|
{
"file_path": "0xDeCA10B/demo/client/tsconfig.json",
"repo_id": "0xDeCA10B",
"token_count": 231
}
| 6 |
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict
import numpy as np
from injector import inject, singleton
from decai.simulation.contract.objects import Address, RejectException, SmartContract, TimeMock
@dataclass
class StoredData:
# Storing the data is not necessary. data: object
classification: object
time: int
sender: Address
# Need to use float since the numbers might be large. They should still actually be integers.
initial_deposit: float
"""
The amount that was initially given to deposit this data.
"""
claimable_amount: float
"""
The amount of the deposit that can still be claimed.
"""
claimed_by: Dict[Address, bool] = field(default_factory=lambda: defaultdict(bool))
@inject
@singleton
@dataclass
class DataHandler(SmartContract):
"""
Stores added training data and corresponding meta-data.
"""
_time: TimeMock
_added_data: Dict[tuple, StoredData] = field(default_factory=dict, init=False)
def __iter__(self):
return iter(self._added_data.items())
def _get_key(self, data, classification, added_time: int, original_author: Address):
if isinstance(data, np.ndarray):
# The `.tolist()` isn't necessary but is faster.
data = tuple(data.tolist())
else:
data = tuple(data)
return (data, classification, added_time, original_author)
def get_data(self, data, classification, added_time: int, original_author: Address) -> StoredData:
"""
:param data: The originally submitted features.
:param classification: The label originally submitted for `data`.
:param added_time: The time in seconds for which the data was added.
:param original_author: The address that originally added the data.
:return: The stored information for the data.
"""
key = self._get_key(data, classification, added_time, original_author)
result = self._added_data.get(key)
return result
def handle_add_data(self, contributor_address: Address, cost, data, classification):
"""
Log an attempt to add data
:param sender: The address of the one attempting to add data
:param cost: The cost required to add new data.
:param data: A single sample of training data for the model.
:param classification: The label for `data`.
"""
current_time_s = self._time()
key = self._get_key(data, classification, current_time_s, contributor_address)
if key in self._added_data:
raise RejectException("Data has already been added.")
d = StoredData(classification, current_time_s, contributor_address, cost, cost)
self._added_data[key] = d
def handle_refund(self, submitter: Address, data, classification, added_time: int) -> (float, bool, StoredData):
"""
Log a refund attempt.
:param submitter: The address of the one attempting a refund.
:param data: The data for which to attempt a refund.
:param classification: The label originally submitted for `data`.
:param added_time: The time in seconds for which the data was added.
:return:
The amount that can be claimed for the refund.
True if the data has already been claimed by `submitter`, otherwise false.
The stored data.
"""
stored_data = self.get_data(data, classification, added_time, submitter)
assert stored_data is not None, "Data not found."
assert stored_data.sender == submitter, "Data isn't from the sender."
claimable_amount = stored_data.claimable_amount
claimed_by_submitter = stored_data.claimed_by[submitter]
return (claimable_amount, claimed_by_submitter, stored_data)
def handle_report(self, reporter: Address, data, classification, added_time: int, original_author: Address) \
-> (bool, StoredData):
"""
Retrieve information about the data to report.
:param reporter: The address of the one reporting the data.
:param data: The data to report.
:param classification: The label originally submitted for `data`.
:param added_time: The time in seconds for which the data was added.
:param original_author: The address that originally added the data.
:return:
True if the data has already been claimed by `submitter`, otherwise false.
The stored data.
"""
stored_data = self.get_data(data, classification, added_time, original_author)
assert stored_data is not None, "Data not found."
claimed_by_reporter = stored_data.claimed_by[reporter]
# The Solidity implementation updates `stored_data.claimed_by` here which is fine.
# We do not update it here because if an error occurs while attempting a refund,
# then the change would have to be undone.
# Instead, `stored_data.claimed_by` is updated in `update_claimable_amount`.
return (claimed_by_reporter, stored_data)
def update_claimable_amount(self, receiver: Address, stored_data: StoredData, reward_amount: float):
# The Solidity implementation does the update in another place which is fine for it.
# Here we only update it once we're sure the refund can be completed successfully.
if reward_amount > 0:
stored_data.claimed_by[receiver] = True
stored_data.claimable_amount -= reward_amount
|
0xDeCA10B/simulation/decai/simulation/contract/data/data_handler.py/0
|
{
"file_path": "0xDeCA10B/simulation/decai/simulation/contract/data/data_handler.py",
"repo_id": "0xDeCA10B",
"token_count": 1965
}
| 7 |
import unittest
from decai.simulation.data.featuremapping.hashing.murmurhash3 import MurmurHash3
class TestMurmurHash3(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.h = MurmurHash3()
def test_classifications(self):
h = self.h.hash("hey")
assert type(h) == int
assert h == 318325784
assert self.h.hash("blockchain") == 3905957473
|
0xDeCA10B/simulation/decai/simulation/data/featuremapping/hashing/tests/test_murmurhash3.py/0
|
{
"file_path": "0xDeCA10B/simulation/decai/simulation/data/featuremapping/hashing/tests/test_murmurhash3.py",
"repo_id": "0xDeCA10B",
"token_count": 168
}
| 8 |
import logging
from dataclasses import dataclass, field
from logging import Logger
from injector import Module, provider, singleton
@dataclass
class LoggingModule(Module):
_log_level: int = field(default=logging.INFO)
@provider
@singleton
def provide_logger(self) -> Logger:
result = logging.Logger('decai')
result.setLevel(self._log_level)
f = logging.Formatter('%(asctime)s [%(levelname)s] - %(name)s:%(filename)s:%(funcName)s\n%(message)s')
h = logging.StreamHandler()
h.setFormatter(f)
result.addHandler(h)
return result
|
0xDeCA10B/simulation/decai/simulation/logging_module.py/0
|
{
"file_path": "0xDeCA10B/simulation/decai/simulation/logging_module.py",
"repo_id": "0xDeCA10B",
"token_count": 246
}
| 9 |
# Lab 3 - CUDA实现和优化
## 实验目的
1. 理解DNN框架中的张量运算在GPU加速器上加速原理。
2. 通过CUDA实现和优化一个定制化张量运算。
## 实验环境
* PyTorch==1.5.0
* CUDA 10.0
## 实验原理
1. 矩阵运算与计算机体系结构
2. GPU加速器的加速原理
## 实验内容
### 实验流程图
### 具体步骤
1. 理解PyTorch中Linear张量运算的计算过程，推导计算公式
2. 了解GPU端加速的原理，CUDA内核编程和实现一个kernel的原理
3. 实现CUDA版本的定制化张量运算
1. 编写.cu文件，实现矩阵相乘的kernel
2. 在上述.cu文件中，编写使用cuda进行前向计算和反向传播的函数
3. 基于C++ API，编写.cpp文件，调用上述函数，实现Linear张量运算的前向计算和反向传播。
4. 将代码生成python的C++扩展
5. 使用基于C++的函数扩展，实现自定义Linear类模块的前向计算和反向传播函数
6. 运行程序，验证网络正确性
4. 使用profiler比较网络性能：基于C++API，比较有无CUDA对张量运算性能的影响
5. 【可选实验，加分】实现基于CUDA的卷积层（Convolutional）自定义张量运算
## 实验报告
### 实验环境
||||
|--------|--------------|--------------------------|
|硬件环境|CPU（vCPU数目）| |
||GPU(型号，数目)||
|软件环境|OS版本||
||深度学习框架<br>python包名称及版本||
||CUDA版本||
||||
### 实验结果
|||
|---------------|---------------------------|
| 实现方式（Linear层为例）| 性能评测 |
|<br/> <br/>CPU only<br/> <br/> | |
|<br/> <br/>With CUDA<br/> <br/> ||
||||
## 参考代码
代码位置：`Lab3/mnist_custom_linear_cuda.py`
运行命令：
```
cd mylinear_cuda_extension
python setup.py install --user
cd .. && python mnist_custom_linear_cuda.py
```
## 参考资料
* CUDA Programming model: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
* An Even Easier Introduction to CUDA: https://devblogs.nvidia.com/even-easier-introduction-cuda/
* CUSTOM C++ AND CUDA EXTENSIONS: https://pytorch.org/tutorials/advanced/cpp_extension.html
|
AI-System/Labs/BasicLabs/Lab3/README.md/0
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{
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"repo_id": "AI-System",
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| 10 |
# What image do you want to start building on?
# Make a folder in your image where your app's source code can live
# Tell your container where your app's source code will live
# What source code do you what to copy, and where to put it?
# Does your app have any dependencies that should be installed?
# What port will the container talk to the outside world with once created?
# How do you start your app?
|
AI-System/Labs/BasicLabs/Lab5/Dockerfile.template/0
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{
"file_path": "AI-System/Labs/BasicLabs/Lab5/Dockerfile.template",
"repo_id": "AI-System",
"token_count": 102
}
| 11 |
from urllib.request import urlretrieve
import time
import tarfile
# TODO
URL_path = r""
filename = r"Data.tar.gz"
print("Please input the local folder path:")
local = input()
from_path = URL_path + "/" + filename
to_path = local + "/" + filename
print("Downloading...")
try:
urlretrieve(from_path, to_path)
print("Done.")
print("Extracting...")
try:
with tarfile.open(to_path) as file:
file.extractall(path = local)
print("Done.")
print("All Work Finished!")
except:
print("Failed Extraction!")
except:
print("Invalid Path!")
print("Exit in 3 seconds...")
time.sleep(3)
|
AI-System/Textbook/src/DataDownload.py/0
|
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"file_path": "AI-System/Textbook/src/DataDownload.py",
"repo_id": "AI-System",
"token_count": 259
}
| 12 |
<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 人工智能系统概述 (System Perspective of System for AI)
# 简介
目前应用最为广泛，工程实践最丰富，研究最为火热的人工智能（Artificial Intelligence）方向就是深度学习（Deep Learning）。深度学习算法目前在计算机视觉，自然语言处理，语音识别等典型场景取得了超越传统算法的效果与突破性进展。应用与算法层面成功的背后离不开系统本身作为基础设施提供容易编程的接口，高效率的执行，大规模的训练，跨平台的部署和工具链的支持。
我们在后面章节介绍的人工智能系统（Artificial Intelligence System）主要是指深度学习系统（Deep Learning System），但是这些系统设计原则大部分也适合于机器学习系统（Machine Learning System）。深度学习算法本身无论从模型设计，训练方式也借鉴了很多传统机器学习算法的经典理论与实践方式，但是深度学习系统本身相比机器学习系统从硬件到软件层有更多新的挑战和演化，数据与问题规模变得更大，应用场景与部署也更加广泛。综合起来深度学习系统会综合考虑和借鉴机器学习系统（Machine Learning System），大数据系统（Big Data System）和高性能计算（High Performance Computing）领域和社区中的经典的系统设计，优化与问题解决方法，并演化出针对深度学习算法特点的新的系统设计。
本章希望在开篇让读者能够了解深度学习是什么，以及作为人工智能系统的上层工作负载(Workload)，深度学习的发展与现状是如何驱动人工智能系统本身发展的。本章首先将展开介绍深度学习的历史，现状与发展，之后介绍深度学习发展的驱动力：算法，框架与体系结构的发展，让读者感受到除了上层应用与算法，系统底层抽象与管理的硬件资源同样是重要的驱动因素。系统常常处于中间层对上管理任务，对下抽象与管理硬件。之后我们将介绍深度学习系统的组成与生态，让读者形成系统的（Systematic）知识框架，为未来开展人工智能系统的学习奠定基础。最后我们通过简单实例启发读者了解算法背后的系统问题，以及回顾经典的计算机系统设计理论并指导之后的深度学习系统的学习。
本章将围绕 1) 深度学习的历史，现状与发展。2）算法，框架，体系结构与算力的进步。 3）深度学习系统组成与生态。4）深度学习样例背后的系统问题。5）影响深度学习系统设计的理论，原则与假设。这五方面的内容展开介绍，以期让读者在开篇了解人工智能系统的来龙去脉，形成人工智能系统的系统化与层次化的初步感受，为后续展开具体的人工智能系统其他章节内容打好初步基础。
# 内容概览
本章包含以下内容：
- [深度学习的历史，现状与发展](1.1-深度学习的历史，现状与发展.md)
- [算法，框架，体系结构与算力的进步](1.2-算法，框架，体系结构与算力的进步.md)
- [深度学习系统组成与生态](1.3-深度学习系统组成与生态.md)
- [深度学习样例背后的系统问题](1.4-深度学习样例背后的系统问题.md)
- [影响深度学习系统设计的理论，原则与假设](1.5-影响深度学习系统设计的理论，原则与假设.md)
|
AI-System/Textbook/第1章-人工智能系统概述/1-前言.md/0
|
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| 13 |
<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
## 2.6 梯度下降优化算法
本小节主要围绕梯度下降优化算法，随机梯度下降 SGD，动量算法 Momentum，梯度加速算法 NAG，AdaGrad，AdaDelta，均方根反向传播 RMSProp，Adam展开介绍。
- [2.6 梯度下降优化算法](#26-梯度下降优化算法)
- [随机梯度下降 SGD](#随机梯度下降-sgd)
- [动量算法 Momentum](#动量算法-momentum)
- [梯度加速算法 NAG](#梯度加速算法-nag)
- [AdaGrad](#adagrad)
- [AdaDelta](#adadelta)
- [均方根反向传播 RMSProp](#均方根反向传播-rmsprop)
- [Adam - Adaptive Moment Estimation](#adam---adaptive-moment-estimation)
- [小结与讨论](#小结与讨论)
- [参考文献](#参考文献)
#### 随机梯度下降 SGD
先回忆一下随机梯度下降的基本算法，便于和后面的各种算法比较。图 2.6.1 中的梯度搜索轨迹为示意图。
<img src="./img/sgd_algorithm.png" width="500"/>
图 2.6.1 随机梯度下降算法的梯度搜索轨迹示意图
***输入和参数***
- $\eta$ - 全局学习率
***算法***
---
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
更新参数：$\theta_t = \theta_{t-1} - \eta \cdot g_t$
---
随机梯度下降算法，在当前点计算梯度，根据学习率前进到下一点。到中点附近时，由于样本误差或者学习率问题，会发生来回徘徊的现象，很可能会错过最优解。
#### 动量算法 Momentum
SGD方法的一个缺点是其更新方向完全依赖于当前batch计算出的梯度，因而十分不稳定，因为数据有噪音。
Momentum算法借用了物理中的动量概念，它模拟的是物体运动时的惯性，即更新的时候在一定程度上保留之前更新的方向，同时利用当前batch的梯度微调最终的更新方向。这样一来，可以在一定程度上增加稳定性，从而学习地更快，并且还有一定摆脱局部最优的能力。Momentum算法会观察历史梯度，若当前梯度的方向与历史梯度一致（表明当前样本不太可能为异常点），则会增强这个方向的梯度。若当前梯度与历史梯度方向不一致，则梯度会衰减。
<img src="./img/momentum_algorithm.png" width="500"/>
图 2.6.2 动量算法的前进方向
图 2.6.2 中，第一次的梯度更新完毕后，会记录$v_1$的动量值。在“求梯度点”进行第二次梯度检查时，得到2号方向，与$v_1$的动量组合后，最终的更新为2'方向。这样一来，由于有$v_1$的存在，会迫使梯度更新方向具备“惯性”，从而可以减小随机样本造成的震荡。
- 输入和参数
- $\eta$ - 全局学习率
- $\alpha$ - 动量参数，一般取值为0.5, 0.9, 0.99
- $v_t$ - 当前时刻的动量，初值为0
- 算法
---
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
计算速度更新：$v_t = \alpha \cdot v_{t-1} + \eta \cdot g_t$ (公式1)
更新参数：$\theta_t = \theta_{t-1} - v_t$ (公式2)
---
#### 梯度加速算法 NAG
其核心思想是：注意到 momentum 方法，如果只看 $\alpha \cdot v_{t-1}$ 项，那么当前的θ经过momentum的作用会变成 $\theta - \alpha \cdot v_{t-1}$。既然我们已经知道了下一步的走向，我们不妨先走一步，到达新的位置”展望”未来，然后在新位置上求梯度, 而不是原始的位置。
所以，同Momentum相比，梯度不是根据当前位置θ计算出来的，而是在移动之后的位置$\theta - \alpha \cdot v_{t-1}$计算梯度。理由是，既然已经确定会移动$\theta - \alpha \cdot v_{t-1}$，那不如之前去看移动后的梯度。
图 2.6.3 是NAG的前进方向。
<img src="./img/nag_algorithm.png" width="500" />
图 2.6.3 梯度加速算法的前进方向
这个改进的目的就是为了提前看到前方的梯度。如果前方的梯度和当前梯度目标一致，那我直接大步迈过去； 如果前方梯度同当前梯度不一致，那我就小心点更新。
***输入和参数***
- $\eta$ - 全局学习率
- $\alpha$ - 动量参数，缺省取值0.9
- $v$ - 动量，初始值为0
***算法***
---
临时更新：$\hat \theta = \theta_{t-1} - \alpha \cdot v_{t-1}$
前向计算：$f(\hat \theta)$
计算梯度：$g_t = \nabla_{\hat\theta} J(\hat \theta)$
计算速度更新：$v_t = \alpha \cdot v_{t-1} + \eta \cdot g_t$
更新参数：$\theta_t = \theta_{t-1} - v_t$
---
#### AdaGrad
AdaGrad是一个基于梯度的优化算法，它的主要功能是：它对不同的参数调整学习率，具体而言，对低频出现的参数进行大的更新，对高频出现的参数进行小的更新。因此，他很适合于处理稀疏数据。
在这之前，我们对于所有的参数使用相同的学习率进行更新。但 Adagrad 则不然，对不同的训练迭代次数t，AdaGrad 对每个参数都有一个不同的学习率。这里开方、除法和乘法的运算都是按元素运算的。这些按元素运算使得目标函数自变量中每个元素都分别拥有自己的学习率。
***输入和参数***
- $\eta$ - 全局学习率
- $\epsilon$ - 用于数值稳定的小常数，建议缺省值为`1e-6`
- $r=0$ 初始值
***算法***
---
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
累计平方梯度：$r_t = r_{t-1} + g_t \odot g_t$
计算梯度更新：$\Delta \theta = {\eta \over \epsilon + \sqrt{r_t}} \odot g_t$
更新参数：$\theta_t=\theta_{t-1} - \Delta \theta$
---
#### AdaDelta
Adaptive Learning Rate Method. $^{[2]}$
AdaDelta法是AdaGrad 法的一个延伸，它旨在解决它学习率不断单调下降的问题。相比计算之前所有梯度值的平方和，AdaDelta法仅计算在一个大小为w的时间区间内梯度值的累积和。
但该方法并不会存储之前梯度的平方值，而是将梯度值累积值按如下的方式递归地定义：关于过去梯度值的衰减均值，当前时间的梯度均值是基于过去梯度均值和当前梯度值平方的加权平均，其中是类似上述动量项的权值。
- 输入和参数
- $\epsilon$ - 用于数值稳定的小常数，建议缺省值为1e-5
- $\alpha \in [0,1)$ - 衰减速率，建议0.9
- $s$ - 累积变量，初始值0
- $r$ - 累积变量变化量，初始为0
- 算法
---
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
累积平方梯度：$s_t = \alpha \cdot s_{t-1} + (1-\alpha) \cdot g_t \odot g_t$
计算梯度更新：$\Delta \theta = \sqrt{r_{t-1} + \epsilon \over s_t + \epsilon} \odot g_t$
更新梯度：$\theta_t = \theta_{t-1} - \Delta \theta$
更新变化量：$r = \alpha \cdot r_{t-1} + (1-\alpha) \cdot \Delta \theta \odot \Delta \theta$
---
#### 均方根反向传播 RMSProp
RMSprop 是由 Geoff Hinton 在他 Coursera 课程中提出的一种适应性学习率方法，至今仍未被公开发表。RMSprop法要解决AdaGrad的学习率缩减问题。
- 输入和参数
- $\eta$ - 全局学习率，建议设置为0.001
- $\epsilon$ - 用于数值稳定的小常数，建议缺省值为1e-8
- $\alpha$ - 衰减速率，建议缺省取值0.9
- $r$ - 累积变量矩阵，与$\theta$尺寸相同，初始化为0
- 算法
---
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
累计平方梯度：$r = \alpha \cdot r + (1-\alpha)(g_t \odot g_t)$
计算梯度更新：$\Delta \theta = {\eta \over \sqrt{r + \epsilon}} \odot g_t$
更新参数：$\theta_{t}=\theta_{t-1} - \Delta \theta$
---
#### Adam - Adaptive Moment Estimation
计算每个参数的自适应学习率，相当于RMSProp + Momentum的效果，Adam$^{[4]}$算法在RMSProp算法基础上对小批量随机梯度也做了指数加权移动平均。和AdaGrad算法、RMSProp算法以及AdaDelta算法一样，目标函数自变量中每个元素都分别拥有自己的学习率。
- 输入和参数
- $t$ - 当前迭代次数
- $\eta$ - 全局学习率，建议缺省值为0.001
- $\epsilon$ - 用于数值稳定的小常数，建议缺省值为1e-8
- $\beta_1, \beta_2$ - 矩估计的指数衰减速率，$\in[0,1)$，建议缺省值分别为0.9和0.999
- 算法
***
计算梯度：$g_t = \nabla_\theta J(\theta_{t-1})$
计数器加一：$t=t+1$
更新有偏一阶矩估计：$m_t = \beta_1 \cdot m_{t-1} + (1-\beta_1) \cdot g_t$
更新有偏二阶矩估计：$v_t = \beta_2 \cdot v_{t-1} + (1-\beta_2)(g_t \odot g_t)$
修正一阶矩的偏差：$\hat m_t = m_t / (1-\beta_1^t)$
修正二阶矩的偏差：$\hat v_t = v_t / (1-\beta_2^t)$
计算梯度更新：$\Delta \theta = \eta \cdot \hat m_t /(\epsilon + \sqrt{\hat v_t})$
更新参数：$\theta_t=\theta_{t-1} - \Delta \theta$
***
## 小结与讨论
本小节主要介绍了梯度下降优化算法，随机梯度下降 SGD，动量算法 Momentum，梯度加速算法 NAG，AdaGrad，AdaDelta，均方根反向传播 RMSProp，Adam。
读者思考，如果当前的训练过程无法在单机单卡完成，需要扩展到多卡和多机分布式训练，如何实现以上训练算法？
## 参考文献
1. 《智能之门》，胡晓武等著，高等教育出版社
2. Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul), 2121-2159.
3. Zeiler, M. D. (2012). ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701.
4. Tieleman, T., & Hinton, G. (2012). Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2), 26-31.
5. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
6. 周志华老师的西瓜书《机器学习》
7. Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1):321-357.
8. Inoue H. Data Augmentation by Pairing Samples for Images Classification[J]. 2018.
9. Zhang H, Cisse M, Dauphin Y N, et al. mixup: Beyond Empirical Risk Minimization[J]. 2017.
10. 《深度学习》- 伊恩·古德费洛
11. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Link: https://arxiv.org/pdf/1506.01497v3.pdf
|
AI-System/Textbook/第2章-神经网络基础/2.6-梯度下降优化算法.md/0
|
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| 14 |
<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 8.1 推理系统简介
本章将围绕推理系统的应用场景，对比推理和训练场景的不同点，进而介绍推理系统的设计目标与约束，为后面章节内容的展开做好铺垫。
- [8.1 推理系统简介](#81-推理系统简介)
- [8.1.1 对比推理与训练过程](#811-对比推理与训练过程)
- [8.1.2 推理系统的优化目标与约束](#812-推理系统的优化目标与约束)
- [小结与讨论](#小结与讨论)
- [参考文献](#参考文献)
## 8.1.1 对比推理与训练过程
深度学习模型已经广泛的部署在各类应用中，充斥在我们生活的周边。例如，对话机器人，新闻推荐系统，物体检测器等。这些应用中，部署深度学习模型。以对话机器人为例，其输入是一句话作为问题，输出也是一句话作为回答，其中可以通过 Seq2Seq，BERT 等语言深度学习模型进行输入到输出的映射与处理。物体检测器的输入为一张图片，输出为图片中可以被检测的物体类别和位置，中间可以通过 Faster R-CNN，YOLO 等模型进行输入到输出的映射与转换，图中的展示结果是通过其他工具（OpenCV）等绘制在图像上，而绘制的坐标及类别是深度学习模型的输出信息。
我们生活中所接触到的应用人工智能模型的应用，其深度学习模型可以部署在数据中心，接受用户的 APP 或者 网页服务的请求，也可以部署在边缘侧移动端的 APP 或者 IOT 设备中实时响应请求。
在后面介绍的推理系统中，我们以数据中心的服务端推理系统为主，兼顾边缘侧移动端推理的场景，但是这些策略本身大部分是数据中心与边缘侧都适用的。
<center> <img src="./img/1/8-1-1-app.png" /></center>
<center>图 8.1.1 典型深度学习推理应用</center>
互联网公司的数据中心中（以 Facebook 2018 年[论文中披露的数据为例](https://research.facebook.com/publications/deep-learning-inference-in-facebook-data-centers-characterization-performance-optimizations-and-hardware-implications/)）[<sup>[1]</sup>](#facebook)常常通过推理系统部署视觉模型（图像分类，物体检测，视频理解等应用），语言模型（翻译，内容理解等应用），排序与推荐模型（广告，信息流，搜索等应用）等深度学习模型。从当时的数据来看，模型尺寸有大有小，范围大致为 1M 到 10B 参数，推理批尺寸为 1 到 100（相比训练批尺寸小很多），延迟要求保证一般为 10 毫秒量级（其中对视觉任务并没有严格延迟要求），同时其大量使用量化方式加速推理。
如图 8.1.1 所示，对话机器人接收一句问题作为输入，通过深度学习模型（例如，Seq2Seq 模型）预测出回答并返回给用户。在这个过程中，推理系统需要考虑和提供以下的功能：
- 提供可以被用户调用的接口
- 能够完成一定的数据处理将输入处理为输出
- 能够在指定低延迟要求下返回用户响应
- 能够利用多样的加速器进行一定的加速
- 能够随着用户的增长保持高吞吐的服务响应和动态进行扩容
- 能够可靠的提供服务，应对软硬件的失效
- 能够支持算法工程师不断更新迭代模型，应对不断变化的新框架
- ...
我们通过上面可以看到，单纯复用原有的 Web 服务器或者移动端应用软件，只能解决其中一部分问题，在深度学习模型推理的场景下，产生了新的系统设计需求与挑战。
接下来，我们先从深度学习训练过程和推理过程对比两者的相同点和不同点，以及在生命周期所处的环节，进而便于理解深度学习推理系统所侧重的目标。
如图 8.1.2，深度学习模型的生命周期（Life Cycle）包含以下几个阶段：首先，需要从存储系统或其他数据源收集和准备训练数据。之后进入训练阶段，开始训练模型，一旦模型满足一定的学习性能（例如，准确度超过 $K\%$，或者错误率降低到 $M\%$ 以下），则终止训练，并保存模型文件。完成训练之后，模型文件会被优化（例如，压缩，量化等），编译（例如，内核调优与代码生成），加载部署在推理系统中。推理系统对外暴露接口（例如，Http 或 gRPC 等），接收用户请求或系统调用，模型通过推理（Inference）处理完请求后，返回给用户相应的响应结果，完成推理任务。
一般训练任务在数据中心中更像是传统的批处理任务，需要执行数小时，数天才能完成，其一般配置较大的批尺寸追求较大的吞吐，将模型训练达到指定的准确度或错误率。而推理任务更像是执行 $7 \times 24$ 的服务，其常常受到响应延迟的约束，配置的批尺寸更小，模型已经稳定一般不再被训练。
<center> <img src="./img/1/8-1-2-lifecycle.png" /></center>
<center>图 8.1.2 深度学习模型的生命周期</center>
如图 8.1.3，如果我们将训练阶段和推理阶段模型的执行过程进行抽象，我们可以总结出两者的共性和不同。
训练阶段，深度学习模型常常采用梯度下降算法或类似的优化算法进行模型训练，我们可以将其拆解为三个阶段:
- 前向传播（Forward Propagation）过程将输入样本（Sample）计算为输出标签（Label）。
- 反向传播（Back Propagation）过程求解权重的梯度。
- 梯度更新（Weight Update）阶段将模型权重通过一定的步长和梯度进行更新。
在推理阶段，则只需要执行前向传播过程，将输入样本计算为输出标签。如图 8.1.3，相比训练阶段，推理阶段则只需要执行前向传播（Forward Propagation）一个过程，将输入样本通过深度学习模型计算输出标签（Label）。
<center> <img src="./img/1/8-1-3-traininfer.png" /></center>
<center>图 8.1.3 模型训练与推理阶段</center>
训练作业常常在之前章节中所介绍的异构集群管理系统中进行执行，通常执行，几个小时，天，或周，是类似传统的批处理作业（Batch Job）。而推理作业需要 $ 7 \times 24 $ 小时运行，类似传统的在线服务（Online Service）。具体来说，深度学习模型推理相比训练的新特点与挑战主要有以下几点：
- 模型被部署为长期运行的服务
- 服务有明确对请求的低延迟（Low Latency）和高吞吐（High Throughput）需求。例如，互联网服务一般都有明确的响应时间延迟约束，保证用户体验，同时需要应对爆发增长的用户产生的高吞吐请求的响应需求。
- 推理有更苛刻的资源约束
- 更小的内存，更低的功耗等。例如，手机的资源要远小于数据中心的商用服务器。
- 推理不需要反向传播梯度下降
- 可以牺牲一定的数据精度。例如，模型本身不再被更新，可以通过量化，稀疏性等手段牺牲一定精度换取一定的性能提升。
- 部署的设备型号更加多样
- 需要多样的定制化的优化。例如，相比服务器端可以通过 Docker 等手段解决环境问题。移动端显得更为棘手，手机有多种多样的平台与操作系统，IOT 设备有不同的芯片和上层软件栈，需要工具与系统提供编译以减少用户适配代价。
如图 8.1.3，深度学习训练与推理相比。在训练中，需要许多输入，通常是更大的批尺寸（Batch Size），用于训练深度学习模型。在推理中，数据经过训练的网络用于发现和预测新输入中的信息，这些输入通过更小的批尺寸输入网络，且由于有延迟的约束，大批尺寸需要所有批内样本都处理完才能响应，容易造成延迟超出约束（例如，每个用户请求要求 100ms 内进行响应）。
对于推理，相比训练的性能目标是不同的。 为了最大限度地减少网络的端到端响应时间（End to End Response Time），推理通常比训练批量输入更少的输入样本，也就是更小的批尺寸，因为依赖推理工作的服务（例如，基于云的图像处理管道）需要尽可能的更快响应，因此用户不必要让系统累积样本成更大批尺寸，进而不必等待几秒钟的响应时间。高吞吐量是训练期间追求的性能指标，但低延迟对于推理更加重要。
本章后面展开的内容中，大部分就是围绕以上总结的新问题和挑战展开。
接下来我们通过 MMdnn[<sup>[2]</sup>](#mmdnn) 中自带的一个模型在 TensorRT 的推理过程实例来了解模型推理的常见步骤。
```python
# (1) 将框架(本实例中为 TensorFlow 模型文件)的模型文件转换为推理系统的模型格式(本实例中为UFF格式).
…
uff_model = uff.from_tensorflow(tf_model, OUTPUT_NAMES)
# (2) 部署模型文件到推理系统TensorRT.
…
engine = trt.utils.uff_to_trt_engine(G_LOGGER, uff_model, parser, 1, 1 << 20)
# (3) 读取测试输入.
…
img = Image.open(path)
…
# (4) 初始化推理引擎上下文(Context).
context = engine.create_execution_context()
...
# (5) 将输入数据拷贝到设备(例如，GPU)并执行模型进行推理
…
bindings = [int(d_input), int(d_output)]
…
cuda.memcpy_htod_async(d_input, img, stream)
# (6) 模型推理
context.enqueue(1, bindings, stream.handle, None)
…
# (7) 最后，获取推理结果，并将结果拷贝到主存
cuda.memcpy_dtoh_async(output, d_output, stream)
…
print("Prediction: ", LABELS[np.argmax(output)])
# 打印结果
>>> Prediction: n01608432 kite
```
类似其他的推理系统也是有类似的步骤。也有很多场景下是通过 CPU 进行推理，这样就不需要进行主存与设备内存之前的数据拷贝，同时操作系统对不同模型有更成熟的隔离与任务调度管理支持。通过以上代码实例我们可以了解主干流程，那么当把模型部署之后，我们可以通过下图观察到常见推理系统的模块，与推理系统交互的系统和推理任务的流水线。
从下图 8.1.4，我们可以看到，模型首先经过训练后会保存在文件系统中，随着训练的模型效果不断提升，可能会产生新版本的模型，并存储在文件系统中并由一定的模型版本管理协议进行管理。之后模型会通过服务系统部署上线，推理系统首先会加载模型到内存，同时会对模型进行一定的版本管理，支持新版本上线和旧版本回滚，对输入数据进行批尺寸（Batch Size）动态优化，并提供服务接口（例如，HTTP，gRPC等），供客户端调用。用户不断向推理服务系统发起请求并接受响应。除了被用户直接访问，推理系统也可以作为一个微服务，被数据中心中其他微服务所调用，完成整个请求处理中一个环节的功能与职责。
<center> <img src="./img/1/8-1-4-servingsys.png" /></center>
<center>图 8.1.4 推理服务系统 </center>
## 8.1.2 推理系统的优化目标与约束
我们以在线推荐系统的服务需求为例，去了解应用场景层面对推理系统产生的需求。例如，某在线新闻 APP 公司希望部署内容个性化推荐服务并期望该服务能满足以下需求：
- 低延迟：
- 互联网上响应请求延迟常常期望小于 100 毫秒
- 高吞吐：
- 突发事件驱动的暴增人群的吞吐量需求
- 扩展性：
- 扩展到不断增长的庞大的用户群体
- 准确度：
- 随着新闻和读者兴趣的变化提供准确的预测
<center> <img src="./img/1/8-1-5-recommendsys.png" width="400" height="500" /></center>
<center>图 8.1.5 推荐系统</center>
如图 8.1.6 所示，除了应用场景的需求，推理系统也需要应对不同训练框架和多样性的推理硬件所产生的部署环境多样性，部署优化和维护困难且容易出错的挑战：
- 框架多样：
- 大多数框架都是为训练设计和优化：例如，批尺寸（Batch Size），批处理作业对延迟不敏感，分布式训练的支持，需要更高的数据精度支持等。
- 开发人员需要将必要的软件组件拼凑在一起：需要兼容数据读取，客户端请求，模型推理等多个阶段的处理。
- 跨多个不断发展的框架集成和推理需求：PyTorch 和 TensorFlow 等框架，以及不同的框架版本的支持。
- 硬件多样：
- 多种部署硬件的支持：移动端部署场景和约束多样（例如，自动驾驶，智能家居等），造成多样化的空间限制，功耗约束。同时孕育了大量专有场景的芯片厂，提供的芯片体系结构多样，软件栈多样。
<center> <img src="./img/1/8-1-6-flexibility.png" /></center>
<center>图 8.1.6 推理系统部署需要支持多种框架和硬件</center>
综上所述，我们可以总结出设计推理系统的主要的优化目标：
- 低延迟（Low Latency）:
- 满足服务等级协议的延迟。更低的延迟会有更好的用户体验和商业竞争力。
- 高吞吐量（High Throughputs）:
- 能够支持高吞吐量的服务请求。更高的吞吐量能服务更多的请求和更多的用户。
- 高效率（High Efficiency）:
- 高效率，低功耗使用 GPU，CPU。高效率执行能够降低推理服务的成本，让推理系统本身降本增效。
- 灵活性（Flexibility）:
- 支持多种框架, 提供构建不同应用的灵活性。提升深度学习模型的部署覆盖的场景，提升部署的生产力。
- 可靠性（Reliability）:
- 需要对不一致的数据，软件、用户配置和底层执行环境故障等造成的中断有弹性（Resilient）。保障推理服务的用户体验与服务等级协议。
- 可扩展性（Scalability）:
- 扩展支持不断增长的用户或设备。更好的能应对突发和不断增长的用户请求。
除了以上的优化目标，设计推理系统时，相比训练系统，需要满足更多的约束：
<center> <img src="./img/1/8-1-7-constraint.png" /></center>
<center>图 8.1.7 推理系统部署需要支持多种框架和硬件</center>
- 服务等级协议（SLA）对延迟的约束
- 模型返回请求在指定时间以内。
- ...
- 资源约束
- 设备端功耗约束：IOT 设备一般为电池供电，提供的电池电量有限（例如，手机，汽车等），无法承受计算量过大产生过大能耗的模型部署。
- 设备与服务端内存约束：移动端设备的内存相比服务端的服务器内存要小很多。
- 云端资源的预算约束：云端部署应用是资源和使用时长付费，一般公司有指定的预算限制。
- ...
- 准确度（Accuracy）约束
- 使用近似模型产生的一些误差可以接受：可以通过模型压缩，量化，低精度推理等手段进行推理加速，以精度换速度。
- ...
如图 8.1.8 所示，推理系统中常常涉及相应模块并完成相应功能，我们将在后面章节中逐步展开。
通过下图我们可以看到推理系统的全貌。首先从流程上来看，推理系统完成以下处理并涉及以下系统设计问题:
<center> <img src="./img/1/8-1-8-infersys.png" /></center>
<center>图 8.1.8 推理系统组件与架构</a></center>
1. 请求与响应处理：系统需要序列化与反序列化请求，并通过后端高效执行，满足一定的响应延迟。相比传统的 Web 服务，推理系统常常需要接受图像，文本，音频等非结构化数据，单请求或响应数据量一般更大，这就需要对这类数据有高效的传输，序列化，压缩与解压缩机制。
2. 请求调度：系统可以根据后端资源利用率，动态调整批尺寸，模型的资源分配，进而提升资源利用率，吞吐量。同时如果是通过加速器进行的加速推理，还要考虑主存与加速器内存之间的数据拷贝，通过调度或预取等策略在计算的间歇做好数据的准备。
3. 后端框架执行：框架将请求映射到模型作为输入，并在运行时调度深度学习模型的内核进行多阶段的处理。如果是部署在异构硬件或多样化的环境，还可以利用编译器进行代码生成与内核算子优化，让模型自动化转换为高效的特定平台的可执行的机器码。
4. 模型版本管理：在云端算法工程师不断验证和开发新的版本模型，需要有一定的协议保证版本更新与回滚。定期或满足一定条件的新模型不断上线替换线上模型，以提升推理服务的效果，但是由于有些指标只能线上测试，有可能线上测试效果较差还需要支持回滚机制，让模型能回滚到稳定的旧版本模型。
5. 健康汇报：云端的服务系统应该是可观测的，才能让服务端工程师监控，报警和修复服务，保证服务的稳定性和 SLA。例如，一段时间内响应变慢，通过可观测的日志，运维工程师能诊断是哪个环节成为瓶颈，进而可以快速定位，应用策略，防止整个服务突发性无法响应（例如，OOM 造成服务程序崩溃）。
6. 推理芯片与代码编译：在边缘端等场景会面对更多样的硬件，驱动和开发库，需要通过编译器进行一定代码生成让模型可以跨设备高效运行，并通过编译器实现性能优化。
7. 推理系统和训练系统间可以通过模型库与上线的协议建立起联系，一般训练系统有一套完整的 DevOps 流水线，也被称作 MLOps，在 8.5 章节我们将进行介绍。算法工程师不断在训练流水线中提交模型设计与算法调优的实验，待满足一定的精度需求后，模型进入模型库并触发上线动作。之后就是模型被压缩优化或加载进推理系统了。
推理系统一般可以部署在云或者边缘。云端部署的推理系统更像传统 Web 服务，在边缘侧部署的模型更像手机应用和 IOT 应用系统。两者有以下特点：
1. 云（Cloud）端：云端有更大的算力，内存，且电更加充足满足模型的功耗需求，同时与训练平台连接更加紧密，更容易使用最新版本模型，同时安全和隐私更容易保证。相比边缘侧可以达到更高的推理吞吐量。但是用户的请求需要经过网络传输到数据中心并进行返回，同时使用的是服务提供商的软硬件资源。
2. 边缘（Edge）端：边缘侧设备资源更紧张（例如，手机和 IOT 设备），且功耗受电池约束，需要更加在意资源的使用和执行的效率。用户的响应只需要在自身设备完成，且不需消耗服务提供商的资源。
推理系统中应用了大量的策略与工具进而让模型可以部署在多样的环境中达到更好的性能和满足资源约束。图 8.1.9 中所示，一般我们可以将引入的策略分为在线和离线策略。
离线策略是在模型还没有加载到推理系统内存部署的阶段，对模型本身进行的转换，优化与打包，核心是让模型变得更“小”（运算量小，内存消耗小等），进而精简模型，更像传统编译器所解决的静态分析与优化的问题。
在线部分是推理系统加载模型后，运行时对请求和模型的执行进行的管理和优化。其更多与运行时资源分配与回收，任务调度，扩容与恢复，模型管理协议等相关，更像传统操作系统与 Web 服务系统中所解决的运行时动态管理与优化的问题。
我们将在后面的章节中介绍相应的策略，让读者对推理系统也形成系统化（Systematic）的感知。
<center> <img src="./img/1/8-1-9-policyinfer.png" width="1000" height="500" /></center>
<center>图 8.1.9 推理系统中涉及到的策略与优化</center>
## 小结与讨论
本小节主要围绕深度学习推理系统设计需要考虑的目标和约束展开介绍，希望读者在深入了解推理系统之前，能对推理系统本身有一个更加全面的认识，之后的各种优化策略也是围绕这些需求进行权衡和设计。
最后大家可以进而思考以下问题，巩固之前的内容：
推理系统相比传统服务系统有哪些新的挑战？
云和端的服务系统有何不同的侧重和挑战？
## 参考文献
<div id="facebook"></div>
1. [Park, Jongsoo et al. “Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.” ArXiv abs/1811.09886 (2018): n. pag.](https://arxiv.org/abs/1811.09886)
<div id="mmdnn"></div>
2. [Yu Liu, Cheng Chen, Ru Zhang, Tingting Qin, Xiang Ji, Haoxiang Lin, and Mao Yang. 2020. Enhancing the interoperability between deep learning frameworks by model conversion. Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery, New York, NY, USA, 1320–1330. https://doi.org/10.1145/3368089.3417051](https://github.com/microsoft/MMdnn)
<div id=""></div>
3. [CSE 599W: System for ML - Model Serving](https://dlsys.cs.washington.edu/)
<div id="clipper"></div>
4. [Crankshaw, Daniel et al. “Clipper: A Low-Latency Online Prediction Serving System.” NSDI (2017).](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-crankshaw.pdf)
<div id="tfx"></div>
1. [Denis Baylor, et al. 2017. TFX: A TensorFlow-Based Production-Scale Machine Learning Platform. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, New York, NY, USA, 1387–1395.](https://research.google/pubs/pub46484/)
<div id="tfserve"></div>
6. [Olston, Christopher et al. “TensorFlow-Serving: Flexible, High-Performance ML Serving.” ArXiv abs/1712.06139 (2017): n. pag.](https://arxiv.org/abs/1712.06139)
<div id="tensorrt"></div>
7. [DEEP LEARNING DEPLOYMENT WITH NVIDIA TENSORRT](https://developer.nvidia.com/blog/deploying-deep-learning-nvidia-tensorrt/)
|
AI-System/Textbook/第8章-深度学习推理系统/8.1-推理系统简介.md/0
|
{
"file_path": "AI-System/Textbook/第8章-深度学习推理系统/8.1-推理系统简介.md",
"repo_id": "AI-System",
"token_count": 15350
}
| 15 |
name: master
channels:
- conda-forge
dependencies:
- python=3.6
- pip
- ipykernel
- ipywidgets==7.3.0
- pandas>=0.23.3
- nb_conda_kernels==2.1.0
- scikit-learn>=0.19.1
- tensorflow-gpu==1.14.0
- tornado<6
- urllib3==1.23
- pip:
- aiohttp>=3.3.2
- azure-cli==2.0.77
- azureml-sdk[notebooks]==1.0.76
- azure-storage-blob==12.0.0
- keras==2.2.0
- lightgbm>=2.1.2
- locustio==0.13.2
- matplotlib==3.1.1
- nest_asyncio>=0.9.10
- nbconvert==5.4.1
- nest_asyncio>=0.9.10
- papermill==0.19.1
- Pillow==6.1.0
- prompt-toolkit>=2.0.7
- PyOpenSSL
- python-dotenv==0.10.3
- toolz==0.9.0
- tqdm==4.32.2
- wget==3.2
- git+https://github.com/microsoft/AI-Utilities.git
|
AI/.ci/environment.yml/0
|
{
"file_path": "AI/.ci/environment.yml",
"repo_id": "AI",
"token_count": 445
}
| 16 |
parameters:
Agent: Hosted Ubuntu 1604
Demands: "python3"
stageName: 'defaultStageName'
jobDisplayName: 'defaultDisplayName'
jobTimeoutInMinutes: 180
TridentWorkloadTypeShort: #
DeployLocation: #
TestPostfix: # "" | "-release" | "-preview"
Deploy_Location_Short: #
DefaultWorkingDirectory: #
Template: #
aksimagename: 'myimage'
ProjectLocation: #
PythonPath: #
cluster_name: #
flighting_release: false
flighting_preview: false
flighting_master: false
doCleanup: True
sub_vars: ../vars/agce_devops_sub_vars.yml
workload_vars: #
sql_server_name: "x"
sql_database_name: "x"
sql_username: "x"
sql_password: "x"
data_prep: true
train: true
post_cleanup: true
container_name: "x"
account_name: "x"
account_key: "x"
datastore_rg: "x"
stages:
- stage: ${{parameters.stageName}}
dependsOn: []
jobs:
- job: deploy_notebook_steps
displayName: ${{parameters.jobDisplayName}}
pool:
name: ${{parameters.Agent}}
demands: ${{parameters.Demands}}
timeoutInMinutes: ${{parameters.jobTimeoutInMinutes}}
continueOnError: ${{or(or(eq(parameters.flighting_release,'true'), eq(parameters.flighting_preview,'true')), eq(parameters.flighting_master,'true'))}}
workspace:
clean: all
variables:
- template: ${{parameters.sub_vars}}
- template: ${{parameters.workload_vars}}
steps:
- template: ../steps/deploy_notebook_steps_v2.yml
parameters:
template: ${{variables.Template}}
azureSubscription: ${{variables.azureSubscription}}
azure_subscription: ${{variables.azure_subscription}}
azureresourcegroup: ${{variables.TridentWorkloadTypeShort}}-${{variables.DeployLocation}}${{parameters.TestPostfix}}
workspacename: ${{variables.TridentWorkloadTypeShort}}-${{variables.DeployLocation}}
azureregion: ${{variables.DeployLocation}}
aksimagename: ${{parameters.aksimagename}}
aks_name: ${{variables.TridentWorkloadTypeShort}}${{parameters.TestPostfix}}
location: ${{variables.ProjectLocation}}
python_path: ${{parameters.DefaultWorkingDirectory}}${{variables.PythonPath}}
cluster_name: ${{variables.TridentWorkloadTypeShort}}${{parameters.TestPostfix}}
flighting_release: ${{parameters.flighting_release}}
flighting_preview: ${{parameters.flighting_preview}}
flighting_master: ${{parameters.flighting_master}}
doCleanup: ${{parameters.doCleanup}}
sql_server_name: ${{parameters.sql_server_name}}
sql_database_name: ${{parameters.sql_database_name}}
sql_username: ${{parameters.sql_username}}
sql_password: ${{parameters.sql_password}}
data_prep: ${{parameters.data_prep}}
train: ${{parameters.train}}
post_cleanup: ${{parameters.post_cleanup}}
container_name: ${{parameters.container_name}}
account_name: ${{parameters.account_name}}
account_key: ${{parameters.account_key}}
datastore_rg: ${{parameters.datastore_rg}}
|
AI/.ci/stage/deploy_notebooks_stage_v2.yml/0
|
{
"file_path": "AI/.ci/stage/deploy_notebooks_stage_v2.yml",
"repo_id": "AI",
"token_count": 1219
}
| 17 |
parameters:
azureSubscription: ''
azure_subscription: ''
location: submodules/DeployMLModelPipelines
azureresourcegroup: dcibhpdl
workspacename: dcibhpwsdl
azureregion: westus2
aksimagename: dcibhpaksdl
aks_name: dcibhpaksdl
aks_service_name: dcibhpaksdlapi
conda: batchscoringdl_aml
doCleanup: true
python_path: "$(System.DefaultWorkingDirectory)/submodules/DeployMLModelPipelines"
cluster_name: "x"
acr_name: "x"
flighting_release: false
flighting_preview: false
flighting_master: false
steps:
- template: config_conda.yml
parameters:
conda_location: ${{parameters.python_path}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
flighting_release: ${{parameters.flighting_release}}
flighting_preview: ${{parameters.flighting_preview}}
flighting_master: ${{parameters.flighting_master}}
- template: azpapermill.yml
parameters:
notebook: 02_setup_aml.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
azure_subscription: ${{parameters.azure_subscription}}
azureresourcegroup: ${{parameters.azureresourcegroup}}
workspacename: ${{parameters.workspacename}}
azureregion: ${{parameters.azureregion}}
storage_account_name: ${{parameters.workspacename}}store
- template: azpapermill.yml
parameters:
notebook: 03_develop_pipeline.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
num_epochs: 1
timeoutInMinutes: 240
- template: azpapermill.yml
parameters:
notebook: 04_deploy_logic_apps.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
image_name: 'torchdet'
acr_resource_group: ${{parameters.azureresourcegroup}}
acr_location: ${{parameters.azureregion}}
- template: cleanuptask.yml
parameters:
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
azureresourcegroup: ${{parameters.azureresourcegroup}}
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/steps/DLBatchDeployAMLJob.yml/0
|
{
"file_path": "AI/.ci/steps/DLBatchDeployAMLJob.yml",
"repo_id": "AI",
"token_count": 814
}
| 18 |
# AI Architecture
#
# A Github Service Connection must also be created with the name "AIArchitecturesAndPractices-GitHub"
# https://docs.microsoft.com/en-us/azure/devops/pipelines/process/demands?view=azure-devops&tabs=yaml
#
# An Agent_Name Variable must be creating in the Azure DevOps UI.
# https://docs.microsoft.com/en-us/azure/devops/pipelines/process/variables?view=azure-devops&tabs=yaml%2Cbatch#secret-variables
#
# This must point to an Agent Pool, with a Self-Hosted Linux VM with a Docker.
# https://docs.microsoft.com/en-us/azure/devops/pipelines/agents/v2-linux?view=azure-devops
parameters:
azureSubscription: ''
azure_subscription: ''
azureresourcegroup: ''
workspacename: ''
azureregion: westus2
aksimagename: ''
aks_name: ''
aks_service_name: myimage
conda: ''
doCleanup: true
flighting_release: false
flighting_preview: false
flighting_master: false
fresh_install: false
steps:
- template: config_conda.yml
parameters:
conda_location: .
azureSubscription: ${{parameters.azureSubscription}}
conda: ai-architecture-template
flighting_release: ${{parameters.flighting_release}}
flighting_preview: ${{parameters.flighting_preview}}
flighting_master: ${{parameters.flighting_master}}
fresh_install: ${{parameters.fresh_install}}
- template: pytest_steps.yml
parameters:
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
azureresourcegroup: ${{parameters.azureresourcegroup}}
workspacename: aiarchtemplate
conda: ai-architecture-template
azureregion: ${{parameters.azureregion}}
- template: cleanuptask.yml
parameters:
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
conda: ai-architecture-template
azureresourcegroup: ${{parameters.azureresourcegroup}}
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/steps/conda_pytest_pylint.yml/0
|
{
"file_path": "AI/.ci/steps/conda_pytest_pylint.yml",
"repo_id": "AI",
"token_count": 693
}
| 19 |
steps:
- script: |
docker stop $(docker ps -a -q)
docker rm $(docker ps -a -q)
docker system prune -a -f
displayName: 'Docker Clean'
|
AI/.ci/steps/docker_clean.yml/0
|
{
"file_path": "AI/.ci/steps/docker_clean.yml",
"repo_id": "AI",
"token_count": 67
}
| 20 |
variables:
TridentWorkloadTypeShort: aidlscore
DeployLocation: eastus2
ProjectLocation: "."
PythonPath: "."
Template: DLTrainDeployAMLJob.yml
|
AI/.ci/vars/dlhyperparametertuning_vars.yml/0
|
{
"file_path": "AI/.ci/vars/dlhyperparametertuning_vars.yml",
"repo_id": "AI",
"token_count": 52
}
| 21 |
#!/bin/bash
#
# This script is for generate ann data for a model in training
#
# For the overall design of the ann driver, check run_train.sh
#
# This script continuously generate ann data using latest model from model_dir
# For training, run this script after initial ann data is created from run_train.sh
# Make sure parameter used here is consistent with the training script
# # Passage ANCE(FirstP)
# gpu_no=4
# seq_length=512
# model_type=rdot_nll
# tokenizer_type="roberta-base"
# base_data_dir="../data/raw_data/"
# preprocessed_data_dir="${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/"
# job_name="OSPass512"
# # Document ANCE(FirstP)
# gpu_no=4
# seq_length=512
# model_type=rdot_nll
# tokenizer_type="roberta-base"
# base_data_dir="../data/raw_data/"
# preprocessed_data_dir="${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/"
# job_name="OSDoc512"
# # Document ANCE(MaxP)
gpu_no=4
seq_length=2048
model_type=rdot_nll_multi_chunk
tokenizer_type="roberta-base"
base_data_dir="../data/raw_data/"
preprocessed_data_dir="${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/"
job_name="OSDoc2048"
##################################### Inital ANN Data generation ################################
model_dir="${base_data_dir}${job_name}/"
model_ann_data_dir="${model_dir}ann_data/"
pretrained_checkpoint_dir="warmup checkpoint path"
initial_data_gen_cmd="\
python -m torch.distributed.launch --nproc_per_node=$gpu_no ../drivers/run_ann_data_gen.py --training_dir $model_dir \
--init_model_dir $pretrained_checkpoint_dir --model_type $model_type --output_dir $model_ann_data_dir \
--cache_dir "${model_ann_data_dir}cache/" --data_dir $preprocessed_data_dir --max_seq_length $seq_length \
--per_gpu_eval_batch_size 16 --topk_training 200 --negative_sample 20 \
"
echo $initial_data_gen_cmd
eval $initial_data_gen_cmd
|
ANCE/commands/run_ann_data_gen.sh/0
|
{
"file_path": "ANCE/commands/run_ann_data_gen.sh",
"repo_id": "ANCE",
"token_count": 661
}
| 22 |
from .tokenization_seed_encoder import *
from .configuration_seed_encoder import *
from .modeling_seed_encoder import *
|
ANCE/model/SEED_Encoder/__init__.py/0
|
{
"file_path": "ANCE/model/SEED_Encoder/__init__.py",
"repo_id": "ANCE",
"token_count": 35
}
| 23 |
import sys
sys.path += ['../']
import pandas as pd
from sklearn.metrics import roc_curve, auc
import gzip
import copy
import torch
from torch import nn
import torch.distributed as dist
from tqdm import tqdm, trange
import os
from os import listdir
from os.path import isfile, join
import json
import logging
import random
import pytrec_eval
import pickle
import numpy as np
import torch
torch.multiprocessing.set_sharing_strategy('file_system')
from multiprocessing import Process
from torch.utils.data import DataLoader, Dataset, TensorDataset, IterableDataset
import re
from model.models import MSMarcoConfigDict, ALL_MODELS
from typing import List, Set, Dict, Tuple, Callable, Iterable, Any
logger = logging.getLogger(__name__)
class InputFeaturesPair(object):
"""
A single set of features of data.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
Usually ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
label: Label corresponding to the input
"""
def __init__(
self,
input_ids_a,
attention_mask_a=None,
token_type_ids_a=None,
input_ids_b=None,
attention_mask_b=None,
token_type_ids_b=None,
label=None):
self.input_ids_a = input_ids_a
self.attention_mask_a = attention_mask_a
self.token_type_ids_a = token_type_ids_a
self.input_ids_b = input_ids_b
self.attention_mask_b = attention_mask_b
self.token_type_ids_b = token_type_ids_b
self.label = label
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
def getattr_recursive(obj, name):
for layer in name.split("."):
if hasattr(obj, layer):
obj = getattr(obj, layer)
else:
return None
return obj
def barrier_array_merge(
args,
data_array,
merge_axis=0,
prefix="",
load_cache=False,
only_load_in_master=False):
# data array: [B, any dimension]
# merge alone one axis
if args.local_rank == -1:
return data_array
if not load_cache:
rank = args.rank
if is_first_worker():
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dist.barrier() # directory created
pickle_path = os.path.join(
args.output_dir,
"{1}_data_obj_{0}.pb".format(
str(rank),
prefix))
with open(pickle_path, 'wb') as handle:
pickle.dump(data_array, handle, protocol=4)
# make sure all processes wrote their data before first process
# collects it
dist.barrier()
data_array = None
data_list = []
# return empty data
if only_load_in_master:
if not is_first_worker():
dist.barrier()
return None
for i in range(
args.world_size): # TODO: dynamically find the max instead of HardCode
pickle_path = os.path.join(
args.output_dir,
"{1}_data_obj_{0}.pb".format(
str(i),
prefix))
try:
with open(pickle_path, 'rb') as handle:
b = pickle.load(handle)
data_list.append(b)
except BaseException:
continue
data_array_agg = np.concatenate(data_list, axis=merge_axis)
dist.barrier()
return data_array_agg
def pad_input_ids(input_ids, max_length,
pad_on_left=False,
pad_token=0):
padding_length = max_length - len(input_ids)
padding_id = [pad_token] * padding_length
if padding_length <= 0:
input_ids = input_ids[:max_length]
else:
if pad_on_left:
input_ids = padding_id + input_ids
else:
input_ids = input_ids + padding_id
return input_ids
def pad_ids(input_ids, attention_mask, token_type_ids, max_length,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
mask_padding_with_zero=True):
padding_length = max_length - len(input_ids)
padding_id = [pad_token] * padding_length
padding_type = [pad_token_segment_id] * padding_length
padding_attention = [0 if mask_padding_with_zero else 1] * padding_length
if padding_length <= 0:
input_ids = input_ids[:max_length]
attention_mask = attention_mask[:max_length]
token_type_ids = token_type_ids[:max_length]
else:
if pad_on_left:
input_ids = padding_id + input_ids
attention_mask = padding_attention + attention_mask
token_type_ids = padding_type + token_type_ids
else:
input_ids = input_ids + padding_id
attention_mask = attention_mask + padding_attention
token_type_ids = token_type_ids + padding_type
return input_ids, attention_mask, token_type_ids
# to reuse pytrec_eval, id must be string
def convert_to_string_id(result_dict):
string_id_dict = {}
# format [string, dict[string, val]]
for k, v in result_dict.items():
_temp_v = {}
for inner_k, inner_v in v.items():
_temp_v[str(inner_k)] = inner_v
string_id_dict[str(k)] = _temp_v
return string_id_dict
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def is_first_worker():
return not dist.is_available() or not dist.is_initialized() or dist.get_rank() == 0
def concat_key(all_list, key, axis=0):
return np.concatenate([ele[key] for ele in all_list], axis=axis)
def get_checkpoint_no(checkpoint_path):
nums = re.findall(r'\d+', checkpoint_path)
return int(nums[-1]) if len(nums) > 0 else 0
def get_latest_ann_data(ann_data_path):
ANN_PREFIX = "ann_ndcg_"
if not os.path.exists(ann_data_path):
return -1, None, None
files = list(next(os.walk(ann_data_path))[2])
num_start_pos = len(ANN_PREFIX)
data_no_list = [int(s[num_start_pos:])
for s in files if s[:num_start_pos] == ANN_PREFIX]
if len(data_no_list) > 0:
data_no = max(data_no_list)
with open(os.path.join(ann_data_path, ANN_PREFIX + str(data_no)), 'r') as f:
ndcg_json = json.load(f)
return data_no, os.path.join(
ann_data_path, "ann_training_data_" + str(data_no)), ndcg_json
return -1, None, None
def numbered_byte_file_generator(base_path, file_no, record_size):
for i in range(file_no):
with open('{}_split{}'.format(base_path, i), 'rb') as f:
while True:
b = f.read(record_size)
if not b:
# eof
break
yield b
class EmbeddingCache:
def __init__(self, base_path, seed=-1):
self.base_path = base_path
with open(base_path + '_meta', 'r') as f:
meta = json.load(f)
self.dtype = np.dtype(meta['type'])
self.total_number = meta['total_number']
self.record_size = int(
meta['embedding_size']) * self.dtype.itemsize + 4
if seed >= 0:
self.ix_array = np.random.RandomState(
seed).permutation(self.total_number)
else:
self.ix_array = np.arange(self.total_number)
self.f = None
def open(self):
self.f = open(self.base_path, 'rb')
def close(self):
self.f.close()
def read_single_record(self):
record_bytes = self.f.read(self.record_size)
passage_len = int.from_bytes(record_bytes[:4], 'big')
passage = np.frombuffer(record_bytes[4:], dtype=self.dtype)
return passage_len, passage
def __enter__(self):
self.open()
return self
def __exit__(self, type, value, traceback):
self.close()
def __getitem__(self, key):
if key < 0 or key > self.total_number:
raise IndexError(
"Index {} is out of bound for cached embeddings of size {}".format(
key, self.total_number))
self.f.seek(key * self.record_size)
return self.read_single_record()
def __iter__(self):
self.f.seek(0)
for i in range(self.total_number):
new_ix = self.ix_array[i]
yield self.__getitem__(new_ix)
def __len__(self):
return self.total_number
class StreamingDataset(IterableDataset):
def __init__(self, elements, fn, distributed=True):
super().__init__()
self.elements = elements
self.fn = fn
self.num_replicas=-1
self.distributed = distributed
def __iter__(self):
if dist.is_initialized():
self.num_replicas = dist.get_world_size()
self.rank = dist.get_rank()
else:
print("Not running in distributed mode")
for i, element in enumerate(self.elements):
if self.distributed and self.num_replicas != -1 and i % self.num_replicas != self.rank:
continue
records = self.fn(element, i)
for rec in records:
yield rec
def tokenize_to_file(args, i, num_process, in_path, out_path, line_fn):
configObj = MSMarcoConfigDict[args.model_type]
tokenizer = configObj.tokenizer_class.from_pretrained(
args.model_name_or_path,
do_lower_case=True,
cache_dir=None,
)
with open(in_path, 'r', encoding='utf-8') if in_path[-2:] != "gz" else gzip.open(in_path, 'rt', encoding='utf8') as in_f,\
open('{}_split{}'.format(out_path, i), 'wb') as out_f:
for idx, line in enumerate(in_f):
if idx % num_process != i:
continue
out_f.write(line_fn(args, line, tokenizer))
def multi_file_process(args, num_process, in_path, out_path, line_fn):
processes = []
for i in range(num_process):
p = Process(
target=tokenize_to_file,
args=(
args,
i,
num_process,
in_path,
out_path,
line_fn,
))
processes.append(p)
p.start()
for p in processes:
p.join()
def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank
"""
if not dist.is_initialized() or dist.get_world_size() == 1:
return [data]
world_size = dist.get_world_size()
# serialized to a Tensor
buffer = pickle.dumps(data)
storage = torch.ByteStorage.from_buffer(buffer)
tensor = torch.ByteTensor(storage).to("cuda")
# obtain Tensor size of each rank
local_size = torch.LongTensor([tensor.numel()]).to("cuda")
size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
dist.all_gather(size_list, local_size)
size_list = [int(size.item()) for size in size_list]
max_size = max(size_list)
# receiving Tensor from all ranks
# we pad the tensor because torch all_gather does not support
# gathering tensors of different shapes
tensor_list = []
for _ in size_list:
tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
if local_size != max_size:
padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
tensor = torch.cat((tensor, padding), dim=0)
dist.all_gather(tensor_list, tensor)
data_list = []
for size, tensor in zip(size_list, tensor_list):
buffer = tensor.cpu().numpy().tobytes()[:size]
data_list.append(pickle.loads(buffer))
return data_list
|
ANCE/utils/util.py/0
|
{
"file_path": "ANCE/utils/util.py",
"repo_id": "ANCE",
"token_count": 5747
}
| 24 |
"""
Code for self-training with weak supervision.
Author: Giannis Karamanolakis ([email protected])
"""
import numpy as np
# Weak Source Classes
# Here is the place to define heuristic rules (labeling functions)
# Note: most rules are already provided in benchmarks as pre-processed files (for efficiency).
class YoutubeRules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 2
self.num_rules = 10
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
return preds
class SMSRules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 2
self.num_rules = 73
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
return preds
class TRECRules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 6
self.num_rules = 68
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
return preds
class CENSUSRules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 2
# self.rules = self.load_rules(self.rule_fpath)
self.num_rules = 83
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
return preds
class MITRRules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 9
# self.rules = self.load_rules(self.rule_fpath)
#self.num_rules = 15
self.add_other_rule = True
self.num_rules = 15 if not self.add_other_rule else 16
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
if not self.add_other_rule:
return preds
# "other" rule predicts 0 if all the other rules predict -1.
pred_mat = np.array(preds)
other_rule = ((pred_mat!=-1).sum(axis=1) != 0).astype(int) * (-1)
new_preds = np.append(pred_mat, np.expand_dims(other_rule, 1), axis=1)
return new_preds.tolist()
class SPOUSERules:
# Weak Source Class
# has to implement apply function that applied to a dataset
# predict() function that applies to a single text.
def __init__(self, datapath="../data"):
self.num_labels = 2
self.num_rules = 9
self.preprocess = None
def apply(self, dataset):
preds = dataset.data['weak_labels']
return preds
|
ASTRA/astra/weaksource/ReGeXRules.py/0
|
{
"file_path": "ASTRA/astra/weaksource/ReGeXRules.py",
"repo_id": "ASTRA",
"token_count": 1248
}
| 25 |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
[email protected].
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
|
AdaMix/CODE_OF_CONDUCT.md/0
|
{
"file_path": "AdaMix/CODE_OF_CONDUCT.md",
"repo_id": "AdaMix",
"token_count": 1107
}
| 26 |
{
"entries": [
{
"1": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abilene, Texas is served by the Abilene regional airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abilene Regional Airport serves the city of Abilene in Texas.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Abilene,_Texas",
"property": "cityServed",
"subject": "Abilene_Regional_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Abilene,_Texas",
"property": "cityServed",
"subject": "Abilene_Regional_Airport"
}
],
[
{
"object": "Abilene,_Texas",
"property": "city",
"subject": "Abilene_Regional_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
}
},
{
"2": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport can be found in Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas airport is located at Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"@en",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id2"
}
},
{
"3": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway name of Adolfo Suárez Madrid–Barajas Airport is 18L/36R.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name at Adolfo Suarez Madrid-Barajas airport is 18L/36R.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name of Adolfo Suarez Madrid-Barajas Airport is 18L/36R.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"18L/36R\"",
"property": "runwayName",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"18L/36R\"",
"property": "runwayDesignation",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
}
},
{
"4": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Afonso Pena International Airport ICAO Location Idenitifier is SBCT.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "SBCT is the ICAO location identifier of Afonso Pena International Airport.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"SBCT\"",
"property": "ICAO_Location_Identifier",
"subject": "Afonso_Pena_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"SBCT\"",
"property": "icaoLocationIdentifier",
"subject": "Afonso_Pena_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
}
},
{
"5": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Afonso Pena International Airport serves the city of Curitiba.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Afonso Pena International Airport serves Curitiba.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Curitiba",
"property": "cityServed",
"subject": "Afonso_Pena_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Curitiba",
"property": "cityServed",
"subject": "Afonso_Pena_International_Airport"
}
],
[
{
"object": "Curitiba",
"property": "city",
"subject": "Afonso_Pena_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id5"
}
},
{
"6": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Al Taqaddum Air Base serves the city of Fallujah.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al-Taqaddum Air Base serves the city of Fallujah.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Fallujah",
"property": "cityServed",
"subject": "Al-Taqaddum_Air_Base"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Fallujah",
"property": "city",
"subject": "Al-Taqaddum_Air_Base"
}
],
[
{
"object": "Fallujah",
"property": "cityServed",
"subject": "Al-Taqaddum_Air_Base"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id6"
}
},
{
"7": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway length of Al-Taqaddum Air Base is 3684.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The length of the runway at Al-Taqaddum Air Base is 3684.0.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3684.0",
"property": "runwayLength",
"subject": "Al-Taqaddum_Air_Base"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3684.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Al-Taqaddum_Air_Base"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id7"
}
},
{
"8": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alderney Airport runway name is 14/32.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "14/32 is the runway name of Alderney Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name of Alderney Airport is 14/32.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"14/32\"",
"property": "runwayName",
"subject": "Alderney_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"14/32\"",
"property": "runwayDesignation",
"subject": "Alderney_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id8"
}
},
{
"9": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway length at Allama Iqbal International Airport is 3,360.12.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway at Allama Iqbal International Airport has a length of 3360.12.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway at Allama Iqbal International Airport is 3360.12 long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3360.12",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3360.12\"^^xsd:double",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id9"
}
},
{
"10": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The first runway at Amsterdam's Schiphol Airport is known as Number 18.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Amsterdam Airport Schiphol's 1st runway number is 18.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The number of the 1st runway at Amsterdam Airport Schiphol is 18.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "18",
"property": "1st_runway_Number",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "18",
"property": "r1Number",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id10"
}
},
{
"11": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 5th runway at Amsterdam airport Schiphol has an asphalt surfacing.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The fifth runway of Amsterdam Airport Schiphol has an asphalt surface.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 5th runway of the Amsterdam Airport Schiphol is asphalt.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Asphalt\"",
"property": "5th_runway_SurfaceType",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Asphalt\"@en",
"property": "r5Surface",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id11"
}
},
{
"12": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol runway name is 06/24 Kaagbaan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "06/24, Kaagbaan, is the runway name of Amsterdam Airport Schiphol.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name of Amsterdam Airport Schiphol is 06/24 Kaagbaan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"06/24 'Kaagbaan'\"",
"property": "runwayName",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"06/24 'Kaagbaan'\"",
"property": "runwayDesignation",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"13": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is 973 metres above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews airport's is 973.0 above sea level.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "973.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"967.435\"^^xsd:double",
"property": "elevation",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"14": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Andrews County Airport is owned by Andrews County, Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The owner of Andrews County airport is Andrews County, Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is owned by Andrews County, Texas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Andrews_County,_Texas",
"property": "owner",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Andrews_County,_Texas",
"property": "owner",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id14"
}
},
{
"15": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway length of Andrews County Airport is 896.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "896.0",
"property": "runwayLength",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"895.807\"^^xsd:double",
"property": "runwayLength",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id15"
}
},
{
"16": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is the first runway made of asphalt.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asphalt is the surface of the first runway of Angola International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Angola International Airport has an asphalt surface.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Asphalt\"",
"property": "1st_runway_SurfaceType",
"subject": "Angola_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"ASPHALT\"@en",
"property": "r1Surface",
"subject": "Angola_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id16"
}
},
{
"17": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport is 12 metres above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport has an elevation of 12.0 metres above sea level.e.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "12.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Antwerp_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"12.0\"^^xsd:double",
"property": "elevation",
"subject": "Antwerp_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
}
},
{
"18": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Antwerp International Airport is operated by the Flemish Government.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The operating organisation of Antwerp International airport is the Flemish government.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport is operated by the Flemish government.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Flemish_Government",
"property": "operatingOrganisation",
"subject": "Antwerp_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Flemish_Government",
"property": "operator",
"subject": "Antwerp_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id18"
}
},
{
"19": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway length of Appleton International Airport is 2,439.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of the Appleton International airport is 2439 units.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Appleton International Airport is 2439.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2439.0",
"property": "runwayLength",
"subject": "Appleton_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2439.01\"^^xsd:double",
"property": "runwayLength",
"subject": "Appleton_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id19"
}
},
{
"20": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport (New Zealand) runway length is 518.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Ardmore Airport, New Zealand, is 518.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Ardmore Airport (New Zealand) is 518.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "518.0",
"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"518.16\"^^xsd:double",
"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id20"
}
},
{
"21": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "UTAA is the ICAO Location Identifier of Ashgabat International Airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ICAO Location Identifier of Ashgabat International Airport is UTAA.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"UTAA\"",
"property": "ICAO_Location_Identifier",
"subject": "Ashgabat_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"(Location of airport in Turkmenistan)\"",
"property": "icaoLocationIdentifier",
"subject": "Ashgabat_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
}
},
{
"22": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway name of Ashgabat International Airport is 11/29.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name at Ashgabat International airport is 11/29.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"11/29\"",
"property": "runwayName",
"subject": "Ashgabat_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"11/29\"",
"property": "runwayDesignation",
"subject": "Ashgabat_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
}
},
{
"23": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport owner is South Jersey Transportation Authority.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "South Jersey Transportation Authority is the owner of the Atlantic City International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is owned by the South Jersey Transportation Authority.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "South_Jersey_Transportation_Authority",
"property": "owner",
"subject": "Atlantic_City_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "South_Jersey_Transportation_Authority",
"property": "owner",
"subject": "Atlantic_City_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id23"
}
},
{
"24": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The name of the runway at Atlantic City International Airport is \"13/31\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "13/31 is the runway name for Atlantic City International airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport's runway name is \"13/31\".",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"13/31\"",
"property": "runwayName",
"subject": "Atlantic_City_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"13/31\"",
"property": "runwayDesignation",
"subject": "Atlantic_City_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id24"
}
},
{
"25": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Dutch language is spoken in Belgium.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Dutch is the language spoken in Belgium.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Belgium's language is Dutch.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dutch_language",
"property": "language",
"subject": "Belgium"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dutch_language",
"property": "language",
"subject": "Belgium"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id25"
}
},
{
"26": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Curitiba is part of the South Region, Brazil.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "South_Region,_Brazil",
"property": "isPartOf",
"subject": "Curitiba"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "South_Region,_Brazil",
"property": "subdivisionName",
"subject": "Curitiba"
}
],
[
{
"object": "South_Region,_Brazil",
"property": "isPartOf",
"subject": "Curitiba"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id26"
}
},
{
"27": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Denmark demonym is Danes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The inhabitants of Denmark have the demonym of Danes.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The people of Denmark are called Danes.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Danes",
"property": "demonym",
"subject": "Denmark"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Danes",
"property": "demonym",
"subject": "Denmark"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id27"
}
},
{
"28": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Fallujah is in the country of Iraq.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Fallujah is located in the country of Iraq.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Fallujah is in Iraq.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Iraq",
"property": "country",
"subject": "Fallujah"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Iraq",
"property": "country",
"subject": "Fallujah"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id28"
}
},
{
"29": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin is in the U.S.A.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin is located in the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin is in the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Greenville,_Wisconsin"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Greenville,_Wisconsin"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id29"
}
},
{
"30": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin is part of Grand Chute, Wisconsin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin is in Grand Chute, Wisconsin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Greenville, Wisconsin, is part of Grand Chute, Wisconsin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Grand_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Greenville,_Wisconsin"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Grand_Chute,_Wisconsin",
"property": "subdivisionName",
"subject": "Greenville,_Wisconsin"
}
],
[
{
"object": "Grand_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Greenville,_Wisconsin"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id30"
}
},
{
"31": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Iraq language is Arabic.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arabic is the language in Iraq.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arabic",
"property": "language",
"subject": "Iraq"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Arabic",
"property": "language",
"subject": "Iraq"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id31"
}
},
{
"32": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Madrid is in the country of Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The city of Madrid is found in Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Madrid is located in Spain.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Madrid"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Madrid"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id32"
}
},
{
"33": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Maple Ridge Township in Alpena County, Michigan is part of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Maple Ridge Township, Alpena County, Michigan is part of the United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "isPartOf",
"subject": "Maple_Ridge_Township,_Alpena_County,_Michigan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "subdivisionName",
"subject": "Maple_Ridge_Township,_Alpena_County,_Michigan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id33"
}
},
{
"34": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Pakistan is Sardar Ayaz Sadiq.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sardar_Ayaz_Sadiq",
"property": "leaderName",
"subject": "Pakistan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sardar_Ayaz_Sadiq",
"property": "leader",
"subject": "Pakistan"
}
],
[
{
"object": "Sardar_Ayaz_Sadiq",
"property": "leaderName",
"subject": "Pakistan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id34"
}
},
{
"35": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "New Jersey is a region served by the Port Authority of New York and New Jersey.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Port Authority of N.Y. and N.J. serves New Jersey.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Port Authority of New York and New Jersey serves the region of New Jersey.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "New_Jersey",
"property": "regionServed",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "New_Jersey",
"property": "regionServed",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id35"
}
},
{
"36": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Houston is the largest city in Texas.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Houston",
"property": "largestCity",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Houston",
"property": "largestcity",
"subject": "Texas"
}
],
[
{
"object": "Houston",
"property": "largestCity",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id36"
}
},
{
"37": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The USAF was involved in the 1986 bombing of Libya.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force was fighting in the United States bombing of Libya in 1986.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1986 United States bombing of Libya was carried out by the United States Air Force.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1986_United_States_bombing_of_Libya",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "1986_United_States_bombing_of_Libya",
"property": "battle",
"subject": "United_States_Air_Force"
}
],
[
{
"object": "1986_United_States_bombing_of_Libya",
"property": "battles",
"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id37"
}
},
{
"38": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force has a Boeing C-17 Globemaster III transport aircraft.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Boeing C-17 Globemaster III is a transport aircraft of the United States Air Force.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Boeing_C-17_Globemaster_III",
"property": "transportAircraft",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Boeing_C-17_Globemaster_III",
"property": "aircraftTransport",
"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id38"
}
},
{
"39": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument was established in the year 2000.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument was established in 2000.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A monument to the 11th Mississippi Infantry was erected in 2000.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "11th Mississippi Infantry Monument was established in 2000.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "11th Mississippi Infantry Monument has been established in 2000.",
"xml_id": "Id5"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument was completed in 2000.",
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}
],
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"object": "2000",
"property": "established",
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},
"shape": "NA",
"shape_type": "NA",
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}
},
{
"40": {
"category": "Monument",
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{
"comment": "good",
"lang": "",
"lex": "To the southeast of Adams County, Pennsylvania lies Carroll County, Maryland.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adams County, Pennsylvania has Carroll County, Maryland to its southeast.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Carroll County Maryland is southeast of Adams County Pennsylvania.",
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}
],
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"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
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{
"object": "Carroll_County,_Maryland",
"property": "southeast",
"subject": "Adams_County,_Pennsylvania"
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]
]
},
"shape": "NA",
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},
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"41": {
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{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade was an Azerbaijan leader.",
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},
{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade is an Azerbaijan leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade is the leader of Azerbaijan.",
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}
],
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{
"object": "Artur_Rasizade",
"property": "leader",
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},
"shape": "NA",
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}
},
{
"42": {
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{
"comment": "good",
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"lex": "Prime Minister of Azerbaijan is the official title of the leader of Azerbaijan.",
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},
{
"comment": "good",
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"lex": "The title of the leader of Azerbaijan is Prime Minister of Azerbaijan.",
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},
{
"comment": "good",
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"lex": "The title of the leader of Azerbaijan is The Prime Minister of Azerbaijan.",
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},
{
"comment": "good",
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"lex": "Azerbaijan's leader is the Prime Minister of Azerbaijan.",
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}
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{
"object": "Prime_Minister_of_Azerbaijan",
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"object": "Prime_Minister_of_Azerbaijan",
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},
{
"43": {
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{
"comment": "good",
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"lex": "The Native name of the Baku Turkish Martyrs' Memorial is \"Türk Sehitleri Aniti\".",
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},
{
"comment": "good",
"lang": "",
"lex": "The native name of the Baku Turkish Martyrs' Memorial is Turk Sehitleri Aniti.",
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},
{
"comment": "good",
"lang": "",
"lex": "The native name for the Baku Turkish Martyrs' Memorial is Türk Şehitleri Anıtı.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is known locally as Türk Şehitleri Anıtı.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial is called Turk Sehitleri Aniti.",
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"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is known in Turkish as Turk Sehitleri Aniti.",
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"object": "\"Türk Şehitleri Anıtı\"",
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]
},
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},
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"comment": "good",
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"lex": "11 Diagonal Street is located in South Africa.",
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"comment": "good",
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"lex": "The address, 11 Diagonal Street is located in South Africa.",
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"subject": "11_Diagonal_Street"
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},
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},
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"45": {
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{
"comment": "good",
"lang": "",
"lex": "250 Delaware Avenue has the Postmodern style of architecture.",
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"comment": "good",
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"lex": "The architectural style of 250 Delaware Avenue is Postmodern.",
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"object": "Postmodern_architecture",
"property": "architecturalStyle",
"subject": "250_Delaware_Avenue"
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],
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"subject": "250_Delaware_Avenue"
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},
"shape": "NA",
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},
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"comment": "good",
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"lex": "300 North LaSalle is located in Chicago.",
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}
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"subject": "300_North_LaSalle"
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},
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"comment": "good",
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"lex": "George Winkler was the architect of 320 South Boston Building.",
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"comment": "good",
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"lex": "George Winkler is the architect of the 320 South Boston Building.",
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}
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"comment": "good",
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{
"object": "1910",
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],
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"object": "\"1910\"",
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},
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},
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{
"comment": "good",
"lang": "",
"lex": "The AC Hotel Bella Sky Copenhagen has 814 rooms.",
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},
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"comment": "good",
"lang": "",
"lex": "The number of rooms in the AC Hotel Bella Sky Copenhagen is 814.",
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"object": "814",
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"subject": "AC_Hotel_Bella_Sky_Copenhagen"
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},
"shape": "NA",
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},
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"comment": "good",
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"lex": "The architects James Pain and George Richard Pain designed Adare Manor,.",
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},
{
"comment": "good",
"lang": "",
"lex": "James Pain and George Richard Pain are the architects of the Adare Manor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "James Pain and George Richard Pain were the Adare Manor architects.",
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}
],
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"property": "architect",
"subject": "Adare_Manor"
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],
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"object": "\"James Pain and George Richard Pain,\"@en",
"property": "architect",
"subject": "Adare_Manor"
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id50"
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},
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"51": {
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{
"comment": "good",
"lang": "",
"lex": "Adisham Hall is located in Haputale.",
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}
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"subject": "Adisham_Hall"
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},
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{
"comment": "good",
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"lex": "Akita Museum of Art is located in Japan.",
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}
],
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"object": "Japan",
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"subject": "Akita_Museum_of_Art"
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"subject": "Akita_Museum_of_Art"
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],
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{
"object": "Japan",
"property": "locationCountry",
"subject": "Akita_Museum_of_Art"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id52"
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},
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"53": {
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{
"comment": "good",
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"lex": "The College of William and Mary is the owner of the Alan B. Miller Hall.",
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"object": "College_of_William_&_Mary",
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"subject": "Alan_B._Miller_Hall"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
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},
{
"54": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The address for Amdavad ni Gufa is Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road.",
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{
"comment": "good",
"lang": "",
"lex": "The address of Amdavad ni Gufa is Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road.",
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],
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"object": "\"Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road\"",
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],
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"object": "\"Lalbhai Dalpatbhai Campus, near CEPT University, opp. Gujarat University, University Road\"@en",
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"subject": "Amdavad_ni_Gufa"
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]
]
},
"shape": "NA",
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},
{
"55": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asher and Mary Isabelle Richardson House has the reference number 88002539 in the National Register of Historic Places.",
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{
"comment": "good",
"lang": "",
"lex": "The Asher and Mary Isabelle Richardson House has a reference number of 88002539 in the National Register of Historic Places.",
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{
"comment": "good",
"lang": "",
"lex": "The Reference Number in the National Register of Historic Places for Asher and Mary Isabelle Richardson House is 88002539.",
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],
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"modifiedtripleset": [
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"object": "\"88002539\"",
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"subject": "Asher_and_Mary_Isabelle_Richardson_House"
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"object": "\"88002539\"",
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]
},
"shape": "NA",
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"xml_id": "Id55"
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},
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"56": {
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"comment": "good",
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"lex": "Julia Morgan was the architect of the grounds of Asilomar Conference.",
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{
"comment": "good",
"lang": "",
"lex": "The architect of Asilomar Conference Grounds was Julia Morgan.",
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"object": "Julia_Morgan",
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"shape": "NA",
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},
{
"57": {
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"comment": "good",
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"lex": "Asilomar Conference Grounds is designed based on the architecture style of \"Arts and Crafts Movement and American craftsman Bungalows\".",
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},
{
"comment": "good",
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"lex": "The architecture of Asilomar Conference Grounds is of the \"Arts and Crafts Movement and American Craftsman Bungalows\".",
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],
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"object": "\"Arts and Crafts Movement and American craftsman Bungalows\"",
"property": "architecture",
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]
},
"shape": "NA",
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},
{
"58": {
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{
"comment": "good",
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"lex": "The Asilomar Conference Grounds were constructed in 1913.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Asilomar Conference Grounds was constructed in 1913.",
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}
],
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],
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{
"object": "1913",
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"object": "\"1913\"^^xsd:gYear",
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]
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id58"
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},
{
"59": {
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{
"comment": "good",
"lang": "",
"lex": "Asser Levy Public Baths are located in New York City.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Asser Levy Public Baths are located in New York City.",
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],
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"modifiedtripleset": [
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"object": "New_York_City",
"property": "location",
"subject": "Asser_Levy_Public_Baths"
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"object": "New_York_City",
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"subject": "Asser_Levy_Public_Baths"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id59"
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},
{
"60": {
"category": "Building",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Buffalo New York is part of Erie County New York.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Buffalo, New York is part of Erie County in New York.",
"xml_id": "Id2"
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],
"links": [],
"modifiedtripleset": [
{
"object": "Erie_County,_New_York",
"property": "isPartOf",
"subject": "Buffalo,_New_York"
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],
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{
"object": "Erie_County,_New_York",
"property": "subdivisionName",
"subject": "Buffalo,_New_York"
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],
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{
"object": "Erie_County,_New_York",
"property": "isPartOf",
"subject": "Buffalo,_New_York"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id60"
}
},
{
"61": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Buffalo, New York is Byron Brown.",
"xml_id": "Id1"
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{
"comment": "good",
"lang": "",
"lex": "Byron Brown is the leader of Buffalo, New York.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of Buffalo, New York, is Byron Brown.",
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}
],
"links": [],
"modifiedtripleset": [
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id61"
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},
{
"62": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "County Limerick is a part of Munster.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "County Limerick is part of Munster.",
"xml_id": "Id2"
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"lex": "AZ Alkmaar played in the Eredivisie in 2014-15.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Eredivisie",
"property": "season",
"subject": "AZ_Alkmaar"
}
],
"originaltriplesets": {
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{
"object": "2014–15_Eredivisie",
"property": "season",
"subject": "AZ_Alkmaar"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id111"
}
},
{
"112": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is in the U.S.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is in the USA.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
}
],
"originaltriplesets": {
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{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id112"
}
},
{
"113": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akron is located in Summit County, Ohio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is located in Summit County, Ohio,.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is in Summit County, Ohio.",
"xml_id": "Id3"
}
],
"links": [],
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{
"object": "Summit_County,_Ohio",
"property": "location",
"subject": "Akron,_Ohio"
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],
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{
"object": "Summit_County,_Ohio",
"property": "subdivisionName",
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]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id113"
}
},
{
"114": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The full name of Akron Summit Assault is ''Akron Metro Futbol Club Summit Assault.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault fullname is \"Akron Metro Futbol Club Summit Assault\".",
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},
{
"comment": "good",
"lang": "",
"lex": "The fullname of Akron Summit is Akron Metro Futbol Club Summit Assault.",
"xml_id": "Id3"
}
],
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"object": "\"Akron Metro Futbol Club Summit Assault\"",
"property": "fullname",
"subject": "Akron_Summit_Assault"
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],
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{
"object": "\"Akron Metro Futbol Club Summit Assault\"@en",
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]
]
},
"shape": "NA",
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"xml_id": "Id114"
}
},
{
"115": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Amsterdam's leader is Eberhard van der Laan.",
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},
{
"comment": "good",
"lang": "",
"lex": "Eberhard van der Laan is a leader in Amsterdam.",
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}
],
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"object": "Eberhard_van_der_Laan",
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"subject": "Amsterdam"
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],
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{
"object": "Eberhard_van_der_Laan",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id115"
}
},
{
"116": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Christian Panucci has played for Chelsea FC.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chelsea_F.C.",
"property": "club",
"subject": "Christian_Panucci"
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],
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{
"object": "Chelsea_F.C.",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id116"
}
},
{
"117": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Christian Panucci plays for Inter Milan.",
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},
{
"comment": "good",
"lang": "",
"lex": "Christian Panucci played for Inter Milan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Inter_Milan",
"property": "club",
"subject": "Christian_Panucci"
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],
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{
"object": "Inter_Milan",
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[
{
"object": "Inter_Milan",
"property": "clubs",
"subject": "Christian_Panucci"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id117"
}
},
{
"118": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Frank de Boer once played football for FC Barcelona.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Frank de Boer is attached to the club FC Barcelona.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Barcelona",
"property": "club",
"subject": "Frank_de_Boer"
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],
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{
"object": "FC_Barcelona",
"property": "clubs",
"subject": "Frank_de_Boer"
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],
[
{
"object": "FC_Barcelona",
"property": "team",
"subject": "Frank_de_Boer"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id118"
}
},
{
"119": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gubbio is in the region of Umbria.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Gubbio is a region of Umbria.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Umbria",
"property": "region",
"subject": "Gubbio"
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],
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{
"object": "Umbria",
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"subject": "Gubbio"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id119"
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},
{
"120": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Italy's leader is Sergio Mattarella.",
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{
"comment": "good",
"lang": "",
"lex": "Sergio Mattarella is the leader of Italy.",
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},
{
"comment": "good",
"lang": "",
"lex": "The leader of Italy is Sergio Mattarella.",
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}
],
"links": [],
"modifiedtripleset": [
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"object": "Sergio_Mattarella",
"property": "leader",
"subject": "Italy"
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],
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"originaltripleset": [
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{
"object": "Sergio_Mattarella",
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]
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id120"
}
},
{
"121": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Jens Härtel has represented the club FC Sachsen Leipzig.",
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{
"comment": "good",
"lang": "",
"lex": "Jens Härtel played for FC Sachsen Leipzig.",
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{
"object": "FC_Sachsen_Leipzig",
"property": "club",
"subject": "Jens_Härtel"
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{
"object": "FC_Sachsen_Leipzig",
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],
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{
"object": "FC_Sachsen_Leipzig",
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]
]
},
"shape": "NA",
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"xml_id": "Id121"
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},
{
"122": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodríguez is a member of the club FC Dallas.",
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},
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodríguez is in the FC Dallas club.",
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},
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodriguez plays for FC Dallas.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Dallas",
"property": "club",
"subject": "Jorge_Humberto_Rodríguez"
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],
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{
"object": "FC_Dallas",
"property": "clubs",
"subject": "Jorge_Humberto_Rodríguez"
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],
[
{
"object": "FC_Dallas",
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"subject": "Jorge_Humberto_Rodríguez"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id122"
}
},
{
"123": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Luciano Spalletti played for Udinese Calcio.",
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},
{
"comment": "good",
"lang": "",
"lex": "Luciano Spalletti has been associated with Udinese Calcio.",
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},
{
"comment": "good",
"lang": "",
"lex": "Luciano Spalletti plays for Udinese Calcio.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Udinese_Calcio",
"property": "club",
"subject": "Luciano_Spalletti"
}
],
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"originaltripleset": [
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{
"object": "Udinese_Calcio",
"property": "managerClub",
"subject": "Luciano_Spalletti"
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],
[
{
"object": "Udinese_Calcio",
"property": "managerclubs",
"subject": "Luciano_Spalletti"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id123"
}
},
{
"124": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago played for A.S.D. S.S. Nola 1925.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago plays for A.S.D. S.S. Nola 1925.",
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}
],
"links": [],
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{
"object": "A.S.D._S.S._Nola_1925",
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"subject": "Massimo_Drago"
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],
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"originaltripleset": [
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{
"object": "A.S.D._S.S._Nola_1925",
"property": "team",
"subject": "Massimo_Drago"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id124"
}
},
{
"125": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini has played for Atalanta BC.",
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},
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini is part of the Atalanta B.C. club.",
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{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini plays for Atalanta B.C.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Atalanta_B.C.",
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"subject": "Michele_Marcolini"
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],
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"originaltripleset": [
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{
"object": "Atalanta_B.C.",
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],
[
{
"object": "Atalanta_B.C.",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id125"
}
},
{
"126": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini played for Vicenza Calcio.",
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},
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini is in the Vicenza Calcio club.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini plays for Vicenza Calcio.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vicenza_Calcio",
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],
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"originaltripleset": [
[
{
"object": "Vicenza_Calcio",
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],
[
{
"object": "Vicenza_Calcio",
"property": "clubs",
"subject": "Michele_Marcolini"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id126"
}
},
{
"127": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Peter Stöger was once a player for 1. FC Köln.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Peter Stoger is attached to the club 1 FC Koln.",
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},
{
"comment": "good",
"lang": "",
"lex": "Peter Stoger attends the 1. FC Koln club.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "1._FC_Köln",
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"subject": "Peter_Stöger"
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],
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"originaltripleset": [
[
{
"object": "1._FC_Köln",
"property": "team",
"subject": "Peter_Stöger"
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],
[
{
"object": "1._FC_Köln",
"property": "managerClub",
"subject": "Peter_Stöger"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id127"
}
},
{
"128": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran has worked at FC Bari 1908.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran plays for F.C. Bari 1908.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "F.C._Bari_1908",
"property": "club",
"subject": "Rolando_Maran"
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],
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"originaltripleset": [
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{
"object": "F.C._Bari_1908",
"property": "managerClub",
"subject": "Rolando_Maran"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id128"
}
},
{
"129": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jong Ajax are the tenants of the De Toekomst Sportpark.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jong Ajax is the tenant of Sportpark De Toekomst.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The tenant of the Sportpark De Toekomst is Jong Ajax.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jong_Ajax",
"property": "tenant",
"subject": "Sportpark_De_Toekomst"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jong_Ajax",
"property": "tenants",
"subject": "Sportpark_De_Toekomst"
}
],
[
{
"object": "Jong_Ajax",
"property": "tenant",
"subject": "Sportpark_De_Toekomst"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id129"
}
},
{
"130": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "St. Vincent-St.Mary High School is in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "St. Vincent St.Mary High School is located in United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "St._Vincent–St._Mary_High_School"
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],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "United_States",
"property": "country",
"subject": "St._Vincent–St._Mary_High_School"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id130"
}
},
{
"131": {
"category": "University",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1 Decembrie 1918 University is located in the state of Alba.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alba",
"property": "state",
"subject": "1_Decembrie_1918_University"
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],
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"originaltripleset": [
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{
"object": "Alba",
"property": "state",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id131"
}
},
{
"132": {
"category": "University",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The President of Acharya Institute of Technology is B. M. Reddy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Acharya Institute of Technology's president is B.M. Reddy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"B.M. Reddy\"",
"property": "president",
"subject": "Acharya_Institute_of_Technology"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"B.M. Reddy\"@en",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id132"
}
},
{
"133": {
"category": "University",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The northwest border to Kerala is Mahe in India.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Kerala has to its northwest Mahé in India.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Mahé in India, is to the northwest of Kerala.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mahé,_India",
"property": "has to its northwest",
"subject": "Kerala"
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],
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"originaltripleset": [
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{
"object": "Mahé,_India",
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"subject": "Kerala"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id133"
}
},
{
"134": {
"category": "University",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Mendrisio and Meride are neighboring Municipalities.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Mendrisio is a neighbouring municipality of Meride.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Meride",
"property": "neighboringMunicipality",
"subject": "Mendrisio"
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],
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"originaltripleset": [
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{
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"comment": "good",
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"comment": "good",
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"lex": "925 is the area code of Antioch, in California.",
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"lex": "The area code for Antioch, California is 925.",
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"lex": "There are 1472 people per square kilometer in Arlington, Texas.",
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"comment": "good",
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"lex": "Kasim Reed is the leader in Atlanta.",
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"lex": "Atlantic City, New Jersey has a total area of 44.125 (square kilometres).",
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"lex": "Attica, in Indiana is 166.0 above sea level.",
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"lex": "Auburn is part of Lee County in Alabama.",
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"lex": "Auburn, Alabama is part of Lee County.",
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"lex": "The total area of Auburn, Washington is 77.41 sq kms.",
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},
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"177": {
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"comment": "good",
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"lex": "Austin is located in Texas.",
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"comment": "good",
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"lex": "Austin is a part of Texas.",
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"comment": "good",
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"lex": "Austin is part of the state of Texas.",
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"178": {
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"comment": "good",
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"lex": "Spanish is spoken in California.",
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"lex": "The Spanish language is spoken in California.",
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"179": {
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"comment": "good",
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"lex": "Dunwoody is the largest city in DeKalb County, Georgia.",
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"comment": "good",
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"lex": "Fulton County is in Georgia, in the United States.",
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"object": "United_States",
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"subject": "Fulton_County,_Georgia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id180"
}
},
{
"181": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anderson is the county seat of Madison County, which is in Indiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The county seat of Madison County, Indiana is Anderson.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Anderson,_Indiana",
"property": "countySeat",
"subject": "Madison_County,_Indiana"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Anderson,_Indiana",
"property": "countySeat",
"subject": "Madison_County,_Indiana"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id181"
}
},
{
"182": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Michigan is in the United states.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Michigan is in the U.S.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Michigan is found within the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Michigan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Michigan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id182"
}
},
{
"183": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Detroit is the largest city in Michigan.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Detroit",
"property": "largestCity",
"subject": "Michigan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Detroit",
"property": "largestCity",
"subject": "Michigan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id183"
}
},
{
"184": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Orange County is in California, in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Orange County, California is located in the United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Orange_County,_California"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Orange_County,_California"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id184"
}
},
{
"185": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Portland is the largest city in Oregon.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Oregon 's largest city is Portland.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Portland,_Oregon",
"property": "largestCity",
"subject": "Oregon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Portland,_Oregon",
"property": "largestCity",
"subject": "Oregon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id185"
}
},
{
"186": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Fort Worth, Tarrant County is the largest city in Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The largest city in Tarrant County, Texas, is Fort Worth.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Fort_Worth,_Texas",
"property": "largestCity",
"subject": "Tarrant_County,_Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Fort_Worth,_Texas",
"property": "largestCity",
"subject": "Tarrant_County,_Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id186"
}
},
{
"187": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barack Obama is the leader of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of the United States is Barack Obama.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barack Obama is a leader of the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
}
],
[
{
"object": "Barack_Obama",
"property": "leaderName",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id187"
}
},
{
"188": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States House of representatives, is located in the United States Capitol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The U.S. House of Representatives is in the U.S. Capitol.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States House of Representatives is located at the United States Capitol.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Capitol",
"property": "location",
"subject": "United_States_House_of_Representatives"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States_Capitol",
"property": "location",
"subject": "United_States_House_of_Representatives"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id188"
}
},
{
"189": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient of amatriciana sauce is pecorino romano.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Pecorino Romano is an ingredient used in Amatriciana sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Pecorino Romano is an ingredient of Amatriciana sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pecorino_Romano",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pecorino_Romano",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id189"
}
},
{
"190": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An important ingredient in arrabbiata sauce is chili pepper.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The chili pepper serves as an ingredient in Arrabbiata sauce.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id190"
}
},
{
"191": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre contains white rice as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "white rice is an ingredient in Arròs negre.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "White_rice",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "White_rice",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id191"
}
},
{
"192": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre is from the region of Catalonia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is from the Catalonia region.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id192"
}
},
{
"193": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from the region of the Valencian Community.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id193"
}
},
{
"194": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from the region of Singapore.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from Singapore.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is from the Singapore region.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id194"
}
},
{
"195": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomato is an ingredient in a BLT.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "one of the ingredients of the BLT Sandwich is Tomato.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tomato",
"property": "ingredient",
"subject": "BLT"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tomato",
"property": "ingredient",
"subject": "BLT"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id195"
}
},
{
"196": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is served in the region of Hong Kong.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska hails from Hong Kong.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id196"
}
},
{
"197": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding can be served either warm or cold.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding can be served warm or cold.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Warm or cold\"",
"property": "served",
"subject": "Bakewell_pudding"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Warm or cold\"@en",
"property": "served",
"subject": "Bakewell_pudding"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id197"
}
},
{
"198": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a dish from Colombian cuisine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is typical Colombian cuisine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is part of Colombian cuisine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id198"
}
},
{
"199": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Chicharrón is one of the ingredients in Bandeja paisa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient found in Bandeja paisa is Chicharrón.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chicharrón",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chicharrón",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id199"
}
},
{
"200": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa originates from the Paisa region.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a traditional dish from the Paisa region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is from the Paisa region.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id200"
}
},
{
"201": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny cakes can be served in 30 gram sizes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Serving size for the Barny cakes is 30.0g.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The serving size of Barny cakes is 30.0g.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "30.0 g",
"property": "servingSize",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"30.0\"^^xsd:double",
"property": "servingSize",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id201"
}
},
{
"202": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor and shumai are variations on the same dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Shumai is a variation of Batagor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor is a variation of Shumai.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Shumai",
"property": "hasVariant",
"subject": "Batagor"
}
],
[
{
"object": "Shumai",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id202"
}
},
{
"203": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A batagor consists of a fried fish dumpling with tofu and vegetables in peanut sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor has these main ingredients - fried fish dumplings, tofu and vegetables in peanut sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor's main ingredients are fried fish dumpling with tofu and vegetables in peanut sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"fried fish dumpling with tofu and vegetables in peanut sauce\"",
"property": "mainIngredients",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"fried fish dumpling with tofu and vegetables inpeanut sauce\"",
"property": "ingredientName",
"subject": "Batagor"
}
],
[
{
"object": "\"fried fish dumpling with tofu and vegetables in peanut sauce\"@en",
"property": "mainIngredient",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id203"
}
},
{
"204": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Shrimp is an ingredient in Batchoy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "one of the ingredients for Batchoy is Shrimp.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Shrimp",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Shrimp",
"property": "ingredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id204"
}
},
{
"205": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a food found in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow comes from the region of Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is served in the region of Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id205"
}
},
{
"206": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhaji and bajji are alternative names for Bhajji.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji is also known as Bhaji or bajji.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Bhaji, bajji\"",
"property": "alternativeName",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Bhaji, bajji\"@en",
"property": "alias",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id206"
}
},
{
"207": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Binignit is an ingredient found in taro.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Taro is an ingredient in Binignit.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Taro is an ingredient of Binignit.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Taro",
"property": "ingredient",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Taro",
"property": "ingredient",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id207"
}
},
{
"208": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico can be varied by using cottage cheese.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cottage_cheese",
"property": "dishVariation",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cottage_cheese",
"property": "hasVariant",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id208"
}
},
{
"209": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is found in the region of Jalisco.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico comes from the region Jalisco.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id209"
}
},
{
"210": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Chicken is a type of bird.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "the class of chickens is bird.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bird",
"property": "class",
"subject": "Chicken"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bird",
"property": "class",
"subject": "Chicken"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id210"
}
},
{
"211": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The currency of India is the Indian rupee.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Indian rupee is the currency in India.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "the Indian rupee is the currency in India.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id211"
}
},
{
"212": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sumitra Mahajan is a leader in India.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sumitra Mahajan is the leader of India.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sumitra_Mahajan",
"property": "leader",
"subject": "India"
}
],
[
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id212"
}
},
{
"213": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Indonesian language is the language spoken in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Indonesian is the language of Indonesia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id213"
}
},
{
"214": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aristoteles Sandoval is a leader of Jalisco.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aristoteles Sandoval is a leader in Jalisco.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aristóteles Sandoval is the leader of Jalisco.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Aristóteles_Sandoval",
"property": "leaderName",
"subject": "Jalisco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Aristóteles_Sandoval",
"property": "leaderName",
"subject": "Jalisco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id214"
}
},
{
"215": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Banyumasan people are an ethnic group from Java.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Banyumasan people are an ethnic group in Java.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Banyumasan people is one of the ethnic groups in Java.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banyumasan_people",
"property": "ethnicGroup",
"subject": "Java"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Banyumasan_people",
"property": "ethnicGroup",
"subject": "Java"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id215"
}
},
{
"216": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Malay is the ethnic group of Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Malaysian Malay is an ethnic group in Malaysia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Malay is an ethnic group in Malaysia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
}
],
[
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id216"
}
},
{
"217": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Mexico is Silvano Aureoles Conejo.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The name of the leader of Mexico is Silvano Aureoles Conejo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The name of the leader in Mexico is Silvano Aureoles Conejo.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Silvano_Aureoles_Conejo",
"property": "leader",
"subject": "Mexico"
}
],
[
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id217"
}
},
{
"218": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Spanish language is one of the languages spoken in New York.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "one of the languages of New York is Spanish.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spanish_language",
"property": "language",
"subject": "New_York"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spanish_language",
"property": "language",
"subject": "New_York"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id218"
}
},
{
"219": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "China is the country Shumai comes from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Shumai is a dish from China.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "China",
"property": "country",
"subject": "Shumai"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "China",
"property": "country",
"subject": "Shumai"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id219"
}
},
{
"220": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Peanut sauce is an ingredient in Siomay.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Peanut sauce is an ingredient of Siomay.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id220"
}
},
{
"221": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Malays are an ethnic group of Sumatra.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Malays are an ethnic group in Sumatra.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malays_(ethnic_group)",
"property": "ethnicGroup",
"subject": "Sumatra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malays_(ethnic_group)",
"property": "ethnicGroups",
"subject": "Sumatra"
}
],
[
{
"object": "Malays_(ethnic_group)",
"property": "ethnicGroup",
"subject": "Sumatra"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id221"
}
},
{
"222": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "David Cameron is the leader of the United Kingdom.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "David Cameron is a leader in the United Kingdom.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "David Cameron is the leader of the United Kingdon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "David_Cameron",
"property": "leaderName",
"subject": "United_Kingdom"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "David_Cameron",
"property": "leaderName",
"subject": "United_Kingdom"
}
],
[
{
"object": "David_Cameron",
"property": "leader",
"subject": "United_Kingdom"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id222"
}
},
{
"223": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is categorized as a Contributing Property and was established in 2000.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument was established in the year 2000 and is categorised as a contributing property.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2000",
"property": "established",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Contributing_property",
"property": "category",
"subject": "11th_Mississippi_Infantry_Monument"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2000",
"property": "established",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Contributing_property",
"property": "category",
"subject": "11th_Mississippi_Infantry_Monument"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id223"
}
},
{
"224": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 14th New Jersey Volunteer Infantry Monument is near Frederick, Maryland in the district of the Monocacy National Battlefield.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 14th New Jersey Volunteer Infantry Monument is located in the Monocacy National Battlefield which nearest city is Frederick, Maryland.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Monocacy_National_Battlefield",
"property": "district",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Frederick,_Maryland",
"property": "nearestCity",
"subject": "Monocacy_National_Battlefield"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Monocacy_National_Battlefield",
"property": "district",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Frederick,_Maryland",
"property": "nearestCity",
"subject": "Monocacy_National_Battlefield"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id224"
}
},
{
"225": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial, located in Azerbaijan, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku and located in Azerbaijan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"@en",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id225"
}
},
{
"226": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial, which native name is Türk Şehitleri Anıtı, has been dedicated to Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is known locally as Türk Şehitleri Anıtı and is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial known in Turkish as Turk Sehitleri Aniti, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial's native name is Türk Şehitleri Anıtı and is dedicated to Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial (native name: Türk Şehitleri Anıtı) is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id5"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"@en",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"@en",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id226"
}
},
{
"227": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "African Americans are an ethnic group in the US, where Akron, Ohio is located.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "An ethnic group in the United States are African Americans. Akron, Ohio is located in the U.S.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is located within the United States, of which African Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
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"lex": "Albuquerque, New Mexico is located in the U.S. and has the United States House of Representatives as a leader.",
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{
"object": "United_States",
"property": "country",
"subject": "Pierce_County,_Washington"
}
],
[
{
"object": "Pierce_County,_Washington",
"property": "subdivisionName",
"subject": "Auburn,_Washington"
},
{
"object": "United_States",
"property": "country",
"subject": "Pierce_County,_Washington"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id242"
}
},
{
"243": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Oregon is part of the United States, where Washington, D.C. is the capital.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Washington DC is the capital of the United States, where Albany, Oregon is located.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albany Oregon is part of the United States where Washington D.C. is the capital.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "isPartOf",
"subject": "Albany,_Oregon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "subdivisionName",
"subject": "Albany,_Oregon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id243"
}
},
{
"244": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amarillo is in Texas, in the United States, the capital of which is Washington, D.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Washington, D.C. is the capital of the United States, which is the home country to Amarillo,Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo, Texas is located within the United States, where the capital is Washington, D.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Amarillo,_Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Amarillo,_Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id244"
}
},
{
"245": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is in the United States, where African Americans are one of the ethnic groups.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico, is part of the United States, where the African Americans are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States includes the ethnic group of African Americans and is the location of Albuquerque, New Mexico.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id245"
}
},
{
"246": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Native Americans are an ethnic group within the United States, the country where Albany, Georgia is located.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States is the location of Albany, Georgia and includes Native Americans among its ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Native Americans are one of the ethnic groups in the United States, where Albany, Georgia is located.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "isPartOf",
"subject": "Albany,_Georgia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "subdivisionName",
"subject": "Albany,_Georgia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id246"
}
},
{
"247": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Auburn, Alabama is located in the United States, a country where one of the ethnic groups are Native Americans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Native Americans are an ethnic group in the United States, where Auburn, Alabama can be found.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States is the location of Auburn, Alabama, and the country includes the ethnic group of Native Americans.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Auburn,_Alabama"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Auburn,_Alabama"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id247"
}
},
{
"248": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The country of Akron, Ohio, is United States, where English is the language spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is located within the United States, where English is the language spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akron,Ohio is located within the United States where English is the official language.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id248"
}
},
{
"249": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is in the United States, where the leader of the country is called the President of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico, is within the United States; the leader of which, is called the President of the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque is in New Mexico, in the United States, where the leader is called the President of the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id249"
}
},
{
"250": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce is made with tomatoes. It is a traditional Italian sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana is a sauce containing tomato that comes from Italy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id250"
}
},
{
"251": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is a food found in Indonesia where Jusuf Kalla is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is commonly served in Indonesia where Jusuf Kalla is leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is a cuisine found in Jusuf Kalla led, Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leader",
"subject": "Indonesia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id251"
}
},
{
"252": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce can be found in Italy where the Italian language is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is from Italy where the Italian language is spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce can be found in Italy, where surprisingly Italian is spoken.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id252"
}
},
{
"253": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sergio Mattarella is a leader in Italy, where Arrabbiata sauce is a traditional dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce can be found in Italy where Sergio Mattarella is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is from Italy, where Sergio Mattarella is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
],
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leader",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id253"
}
},
{
"254": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata is an Italian sauce that contains chili pepper.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce can be found in Italy and one of the ingredients in it is chili pepper.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce, containing chili pepper, can be found in Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id254"
}
},
{
"255": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre is a traditional Spanish dish and comes from the region of the Valencian Community.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from the region of the Valencian Community in Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from the Spanish region of the Valencian Community.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id255"
}
},
{
"256": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A dish popular in Indonesia is asam pedas which is made from fish cooked in hot and sour sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Fish is cooked in sour and hot sauce to create an Asam Pedas which originates from the country of Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish of fish cooked in a sour and hot sauce that comes from Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "mainIngredients",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "ingredientName",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id256"
}
},
{
"257": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is from Indonesia and is a popular dish in Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet comes from both Malaysia and Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam Penyet is a dish from Malaysia and Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id257"
}
},
{
"258": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A bacon sandwich can be called a BLT which is a variation of a Club Sandwich.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "BLT is a variation of the club sandwich and the bacon sandwich.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwich is a variation of BLT which is a variation of a club sandwich.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Club_sandwich",
"property": "dishVariation",
"subject": "BLT"
},
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Club_sandwich",
"property": "variations",
"subject": "BLT"
},
{
"object": "BLT",
"property": "hasVariant",
"subject": "Bacon_sandwich"
}
],
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "hasVariant",
"subject": "BLT"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id258"
}
},
{
"259": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sausage is an ingredient of a bacon explosion but the main ingredient is bacon.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aside from the main ingredient bacon, sausage is included in the Bacon Explosion.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "mainIngredients",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "mainIngredient",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id259"
}
},
{
"260": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A bacon sandwich - which can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin - has the ingredient bacon.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich, which always includes bacon, has different names including: Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"",
"property": "alternativeName",
"subject": "Bacon_sandwich"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"@en",
"property": "alias",
"subject": "Bacon_sandwich"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id260"
}
},
{
"261": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A bacon sandwich can be transformed into a BLT which has an ingredient widely found called bread.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient in a bacon sandwich is bread and a variation is the BLT.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich, made from bread, is a variation of BLT.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id261"
}
},
{
"262": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from the country of France and one of the ingredients is sponge cake.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska (France) uses sponge cake as an ingredient.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "France's Baked Alaska includes the ingredient, sponge cake.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id262"
}
},
{
"263": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from the country of France where Manuel Valls is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "France (led by Manuel Valls) is the origin of Baked Alaska.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Manuel Valls is the leader of France, where baked alaska comes from.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leader",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id263"
}
},
{
"264": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Meringue is one of the main ingredients of a Baked Alaska, Baked Alaska originates from the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska, a United States dish, includes the ingredient meringue.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Meringue is an ingredient of a Baked Alaska which is a dish found in the U.S.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id264"
}
},
{
"265": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Fruit preservatives is an ingredient in bakewell tarts which are a variant of bakewell pudding.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bakewell tart, containing fruit preserves, is a variation of Bakewell pudding.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Fruit preserves are an ingredient in Bakewell tart which is a variation of Bakewell pudding.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Fruit_preserves",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Fruit_preserves",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id265"
}
},
{
"266": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is an ingredient of the dish Bakso which comes from Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is included in the Indonesian dish of bakso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is an ingredient in Bakso which is from Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "Vermicelli",
"property": "ingredient",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "Vermicelli",
"property": "ingredient",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id266"
}
},
{
"267": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Celery is a member of the Apiaceae family and is an ingredient of Bakso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Celery, which is part of the Apiaceae family, is an ingredient of Bakso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Celery is an ingredient of Bakso and is a member of the family Apiaceae.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Apiaceae",
"property": "family",
"subject": "Celery"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Apiaceae",
"property": "family",
"subject": "Celery"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id267"
}
},
{
"268": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient found in Bandeja paisa (typical Colombian cuisine) is Chicharrón.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Chicharrón is one of the ingredients in Colombian cuisine's Bandeja paisa.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Traditional Colombian cuisine, Bandeja paisa, includes the ingredient, chicharron.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chicharrón",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chicharrón",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id268"
}
},
{
"269": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The country Barny Cakes come from is France where the leader is Claude Bartolone.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes can be found in France where the leader is Claude Bartolone.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes come from France, whose leader is Claude Bartolone.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leader",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id269"
}
},
{
"270": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Peanut sauce is an ingredient of batagor which is found in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, which has peanut sauce as an ingredient, is found in Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Peanut sauce is an ingredient of batagor which comes from the country of Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
}
],
[
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "\"Indonesia\"@en",
"property": "country",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id270"
}
},
{
"271": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor has peanut sauce as an ingredient and is a variation of the Siomay dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The dishes Batagor and Siomay both contain the ingredient peanut sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor is a variation of the Siomay dish and one of the ingredients is peanut sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id271"
}
},
{
"272": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish from the country of Singapore that had palm sugar as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Palm sugar is an ingredient in beef kway teow which comes from Singapore.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore, it includes palm sugar.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Palm_sugar",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Palm_sugar",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id272"
}
},
{
"273": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish from Singapore and Halimah Yacob is it's leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish from Singapore where Halimah Yacob is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore whose leader is Halimah Yacob.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Halimah_Yacob",
"property": "leader",
"subject": "Singapore"
}
],
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id273"
}
},
{
"274": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in Beef Kway Teow made in Singapore and Indonesia are kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Kway teow, beef tender loin, gula Melaka, sliced dried black beans, garlic, soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil are the main ingredients of Beef Kway Teow which is a dish found in Indonesia and Singapore.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish commonly found in Singapore and Indonesia and the main ingredients are Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil\"",
"property": "mainIngredients",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Kway teow , beef tenderloin, gula Melaka ,sliced, dried black beans, garlic, dark soy sauce, lengkuas , oyster sauce, soya sauce, chilli and sesame oil\"@en",
"property": "mainIngredient",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id274"
}
},
{
"275": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country India, where Indians live.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India which is where Indian people are from.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji is a dish originating from Indian inhabited, India.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id275"
}
},
{
"276": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji's come from India where T.S. Thakur is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji and T.S. Thakur are from India and Thakkur is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India where the leader is T.S. Thakur.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id276"
}
},
{
"277": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Igorot people are one of the ethnic groups in the Philippines where the binignit dish can be found.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Igorot people are an ethnic group found in the Philippines which is also home to the binignit dish.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Binignit is a dish from the Philippines, where the ethnic group called the Igorot people are found.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id277"
}
},
{
"278": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Binignit comes from the Philippines and has banana as the main ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "One of the main ingredients in binignit is banana and it can be found in the Philippines.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The binignit dish, containing banana, can be found in the Philippines.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banana",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Banana",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id278"
}
},
{
"279": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of binignit are sweet potatoes and banana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "One of the main ingredients of Binignit is the sweet potato, and it also contains banana.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of binignit are sweet potatotes and bananas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id279"
}
},
{
"280": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Mexico (led by Enrique Pena Nieto) boasts the dish Bionico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Enrique Pena Nieto is the leader of Mexico, where the food bionico is found.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a food found in Mexico where Enrique Pena Nieto is a leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leaderName",
"subject": "Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leader",
"subject": "Mexico"
}
],
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leaderName",
"subject": "Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id280"
}
},
{
"281": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico requires granola as one of its ingredients and can be found in Guadalajara.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Granola is a required ingredient of the Guadalajara regional dish, Bionico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico, which contains granola, can be found in Guadalajara.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id281"
}
},
{
"282": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is served at the dessert course, as would cake be as a variation.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dish served for dessert as is cake.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A variation of cake is dessert and Bionico is one example of this.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cake",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cake",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id282"
}
},
{
"283": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Indonesia, where Joko Widodo is the leader, is also where Bakso comes from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Indonesia is the country Bakso comes from and the leader is Joko Widodo.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Joko_Widodo",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Joko_Widodo",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id283"
}
},
{
"284": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A variation of Shumai includes Batagor which features the ingredient Shiitake.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Shiitake is an ingredient of Shumai which has a variation known as Batagor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Shiitake is an ingredient of Shumai which is a variation of Batagor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Shumai",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id284"
}
},
{
"285": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aarhus Airport, operated by Aarhus Lufthavn A/S, has a runway length of 2777.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aarhus Lufthavn A/S is the operation organisation of Aarhus Airport which has a runway length of 2777.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2777.0",
"property": "runwayLength",
"subject": "Aarhus_Airport"
},
{
"object": "\"Aarhus Lufthavn A/S\"",
"property": "operatingOrganisation",
"subject": "Aarhus_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2777.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Aarhus_Airport"
},
{
"object": "\"Aarhus Lufthavn A/S\"@en",
"property": "operator",
"subject": "Aarhus_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id285"
}
},
{
"286": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport at Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas is 610 metres above sea level..",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas and lies 610 metres above sea level.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas airport is located at Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas and is elevated 610 metres above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "610.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"609.6\"^^xsd:double",
"property": "elevation",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"@en",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id286"
}
},
{
"287": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas and has a runway length of 3500.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length at Adolfo Suarez Madrid- Barajas airport is 3500. It is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas airport is located at Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas and the runway length is 3500.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3500.0",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3499.71\"^^xsd:double",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"@en",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id287"
}
},
{
"288": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is in Uttar Pradesh, Awadh.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is located in Uttar Pradesh, which is part of Awadh.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Agra airport is located in Uttar Pradesh, Awadh.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Uttar_Pradesh",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "Awadh",
"property": "isPartOf",
"subject": "Uttar_Pradesh"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Uttar_Pradesh",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "Awadh",
"property": "isPartOf",
"subject": "Uttar_Pradesh"
}
],
[
{
"object": "Uttar_Pradesh",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "Awadh",
"property": "subdivisionName",
"subject": "Uttar_Pradesh"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id288"
}
},
{
"289": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Al Asad Airbase is situated in the Al Anbar Province, Iraq and has a runway length of 3090.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is located in Al Anbar Province, Iraq and has a runway length of 3090.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad airbase is located at Al Anbar Province, Iraq and has a runway length is 3090.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Al Anbar Province, Iraq\"",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
"object": "3090.0",
"property": "runwayLength",
"subject": "Al_Asad_Airbase"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Al Anbar Province, Iraq\"@en",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
"object": "\"3090.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Al_Asad_Airbase"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id289"
}
},
{
"290": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is in Iraq, where they speak Arabic.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is found in Iraq where the language is Arabic.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is in Iraq where Arabic is the language.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Iraq",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
"object": "Arabic",
"property": "language",
"subject": "Iraq"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Iraq",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
"object": "Arabic",
"property": "language",
"subject": "Iraq"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id290"
}
},
{
"291": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The US Air force operates the Al Asad airbase and has previously fought in the Invasion of Grenada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is operated by the United States Air Force who were involved in battles at the Invasion of Grenada.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "Invasion_of_Grenada",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States_Air_Force",
"property": "operator",
"subject": "Al_Asad_Airbase"
},
{
"object": "Invasion_of_Grenada",
"property": "battle",
"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id291"
}
},
{
"292": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alderney airport's first runway is made from Poaceae which is from the Monocotyledon class.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Alderney Airport is made from Poaceae, which is in the Monocotyledon class.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Alderney Airport is made from Poaceae, classed as a Monocotyledon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Poaceae",
"property": "1st_runway_SurfaceType",
"subject": "Alderney_Airport"
},
{
"object": "Monocotyledon",
"property": "class",
"subject": "Poaceae"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Poaceae",
"property": "r1Surface",
"subject": "Alderney_Airport"
},
{
"object": "Monocotyledon",
"property": "class",
"subject": "Poaceae"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id292"
}
},
{
"293": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Alderney airport has an asphalt surface and a length of 877.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The first runway at Alderney Airport is made of asphalt, and its length is 877.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "877.0",
"property": "runwayLength",
"subject": "Alderney_Airport"
},
{
"object": "Asphalt",
"property": "1st_runway_SurfaceType",
"subject": "Alderney_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"877.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Alderney_Airport"
},
{
"object": "Asphalt",
"property": "r1Surface",
"subject": "Alderney_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id293"
}
},
{
"294": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International airport is located in Pakistan where Anwar Zaheer Jamali is a leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International airport is located in Pakistan who's leader is Anwar Zaheer Jamali.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Pakistan is home to the Allama Iqbal International airport and is led by Anwar Zaheer Jamali.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Anwar_Zaheer_Jamali",
"property": "leaderName",
"subject": "Pakistan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Anwar_Zaheer_Jamali",
"property": "leaderName",
"subject": "Pakistan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id294"
}
},
{
"295": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Located in Punjab Pakistan, Allama Iqbal International Airport has a runway length of 2900.0 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Allama Iqbal International Airport is found in Punjab, Pakistan and has a runway length of 2900.0.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Punjab,_Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "2900.0",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Punjab,_Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "\"2900.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
}
],
[
{
"object": "\"2900.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "\"Punjab, Pakistan\"@en",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
}
],
[
{
"object": "\"Punjab, Pakistan\"@en",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "\"2899.87\"^^xsd:double",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id295"
}
},
{
"296": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is -3.3528 metres above sea level and its first runway has a length of 3800 meters.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is -3.3528 from sea level and the length of its first runway is 3800 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is -3.3528 metres above sea level, the runway is 3800m long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3800",
"property": "1st_runway_LengthMetre",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "3800",
"property": "r1LengthM",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id296"
}
},
{
"297": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is 3.3528 below sea level and its first runway is made of asphalt..",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam airport Schiphol os -3.3528 from sea level and the 1st runway is made from Asphalt.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is -3.3528 metres above sea level and the first runway is made of asphalt.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asphalt",
"property": "1st_runway_SurfaceType",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Asphalt",
"property": "r1Surface",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id297"
}
},
{
"298": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol serves the city of Amsterdam and has a runway length of 2014.0.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "2014.0",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "city",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
}
],
[
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id298"
}
},
{
"299": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol serves Amsterdam and the runway length is 3300.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "3300.0",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
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"subject": "Amsterdam_Airport_Schiphol"
}
],
[
{
"object": "Amsterdam",
"property": "city",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"3300.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id299"
}
},
{
"300": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas where Houston is the largest city.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas where the largest city is Houston.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Houston",
"property": "largestCity",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Houston",
"property": "largestcity",
"subject": "Texas"
}
],
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Houston",
"property": "largestCity",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id300"
}
},
{
"301": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The city of Luanda is served by Angola International Airport which has a runway that is 3800 metres long.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Angola International Airport, which serves the city of Luanda, is 3800.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport serves Luanda and its runway is 3800 long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3800.0",
"property": "runwayLength",
"subject": "Angola_International_Airport"
},
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3800.86\"^^xsd:double",
"property": "runwayLength",
"subject": "Angola_International_Airport"
},
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
}
],
[
{
"object": "\"3800.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Angola_International_Airport"
},
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id301"
}
},
{
"302": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae, and its length is 597.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Ardmore Airport (New Zealand) is 597.0 and its 3rd runway has a surface type of Poaceae.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "597.0",
"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poaceae",
"property": "3rd_runway_SurfaceType",
"subject": "Ardmore_Airport_(New_Zealand)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"597.408\"^^xsd:double",
"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poaceae",
"property": "r3Surface",
"subject": "Ardmore_Airport_(New_Zealand)"
}
],
[
{
"object": "\"597.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poaceae",
"property": "r3Surface",
"subject": "Ardmore_Airport_(New_Zealand)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id302"
}
},
{
"303": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Ashgabat International airport is 12467 feet in length and the Airport is elevated 211 metres above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Ashgabat International Airport is 211 metres above sea level and its first runway has a length of 12467 feet.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ashgabat International Airport is elevated 211 metres above sea level and the first runway has a length of 12467 feet.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "12467",
"property": "1st_runway_LengthFeet",
"subject": "Ashgabat_International_Airport"
},
{
"object": "211",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Ashgabat_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "12467",
"property": "r1LengthF",
"subject": "Ashgabat_International_Airport"
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{
"object": "\"211.0\"^^xsd:double",
"property": "elevation",
"subject": "Ashgabat_International_Airport"
}
],
[
{
"object": "12467",
"property": "r1LengthF",
"subject": "Ashgabat_International_Airport"
},
{
"object": "\"210.922\"^^xsd:double",
"property": "elevation",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id303"
}
},
{
"304": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Turkmenistan Airlines, which is headquartered in Turkmenistan, is the operating organization for Ashgabat International Airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The headquarters of Turkmenistan Airlines are located in Turkmenistan and they are the operating organisation for Ashgabat International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ashgabat International Airport is operated by Turkmenistan Airlines, the headquarters of which are located in Turkmenistan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Turkmenistan_Airlines",
"property": "operatingOrganisation",
"subject": "Ashgabat_International_Airport"
},
{
"object": "Turkmenistan",
"property": "headquarter",
"subject": "Turkmenistan_Airlines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Turkmenistan_Airlines",
"property": "operator",
"subject": "Ashgabat_International_Airport"
},
{
"object": "Turkmenistan",
"property": "headquarter",
"subject": "Turkmenistan_Airlines"
}
],
[
{
"object": "Turkmenistan_Airlines",
"property": "operator",
"subject": "Ashgabat_International_Airport"
},
{
"object": "Turkmenistan",
"property": "headquarters",
"subject": "Turkmenistan_Airlines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id304"
}
},
{
"305": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is in Egg Harbor Township, New Jersey, United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is located in Egg Harbor Township, New Jersey, United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is located at Egg Harbor Township, New Jersey, in the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id305"
}
},
{
"306": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "4/22 is the runway name of Atlantic City International Airport which is operated by the Port Authority of New York and New Jersey.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Port Authority of New York and New Jersey operate the Atlantic City International Airport which has a runway known as 4/22.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Port_Authority_of_New_York_and_New_Jersey",
"property": "operatingOrganisation",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "\"4/22\"",
"property": "runwayName",
"subject": "Atlantic_City_International_Airport"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Port_Authority_of_New_York_and_New_Jersey",
"property": "operator",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "\"4/22\"",
"property": "runwayDesignation",
"subject": "Atlantic_City_International_Airport"
}
],
[
{
"object": "\"Port Authority of New York and New Jersey & SJTA\"@en",
"property": "operator",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "\"4/22\"",
"property": "runwayDesignation",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id306"
}
},
{
"307": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport, (runway length 3048.0), is operated by the Port Authority of New York and New Jersey.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Operated by the Port Authority of New York and New Jersey, Atlantic City International Airport has a runway which is 3048.0 metres long.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3048.0",
"property": "runwayLength",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Port_Authority_of_New_York_and_New_Jersey",
"property": "operatingOrganisation",
"subject": "Atlantic_City_International_Airport"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3048.0\"^^xsd:double",
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},
{
"object": "Port_Authority_of_New_York_and_New_Jersey",
"property": "operator",
"subject": "Atlantic_City_International_Airport"
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],
[
{
"object": "\"3048.0\"^^xsd:double",
"property": "runwayLength",
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},
{
"object": "\"Port Authority of New York and New Jersey & SJTA\"@en",
"property": "operator",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id307"
}
},
{
"308": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Afonso Pena International Airport is operated by Infraero which is in Brasilia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Afonso Pena International Airport is operated by Infraero, Brasilia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The operating organization for Afonso Pena International Airport is Infraero in Brasilia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Brasília",
"property": "location",
"subject": "Infraero"
},
{
"object": "Infraero",
"property": "operatingOrganisation",
"subject": "Afonso_Pena_International_Airport"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Brasília",
"property": "location",
"subject": "Infraero"
},
{
"object": "Infraero",
"property": "operator",
"subject": "Afonso_Pena_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id308"
}
},
{
"309": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "FC Magedburg has 27250 members and was in Regionalliga in 2014.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "FC Magdeburg played in the Regionalliga in 2014-15 season and has 27250 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "1.FC Magdeburg has 27250 members and in the 2014-15 season they played in the Regionalliga.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "27250",
"property": "numberOfMembers",
"subject": "1._FC_Magdeburg"
},
{
"object": "2014–15_Regionalliga",
"property": "season",
"subject": "1._FC_Magdeburg"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"27250\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "1._FC_Magdeburg"
},
{
"object": "2014–15_Regionalliga",
"property": "season",
"subject": "1._FC_Magdeburg"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id309"
}
},
{
"310": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Verona, Italy is the home to A.C. Chievo Verona which has 39371 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Verona, Italy is the home to A.C. Chievo Verona who have 39371 members.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Verona, Italy\"",
"property": "ground",
"subject": "A.C._Chievo_Verona"
},
{
"object": "39371",
"property": "numberOfMembers",
"subject": "A.C._Chievo_Verona"
}
],
"originaltriplesets": {
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[
{
"object": "\"Verona, Italy\"@en",
"property": "ground",
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{
"object": "39371",
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],
[
{
"object": "\"Verona, Italy\"@en",
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id310"
}
},
{
"311": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran, who has managed AC Chievo Verona, was born in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Associazione Calcio ChievoVerona is managed by Italian born Rolando Maran.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of A.C. Chievo Verona is Rolando Maran, who was born in Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rolando_Maran",
"property": "manager",
"subject": "A.C._Chievo_Verona"
},
{
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"subject": "Rolando_Maran"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rolando_Maran",
"property": "manager",
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{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Rolando_Maran"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id311"
}
},
{
"312": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodriguez manages A.D. Isidro Metapan. He plays for FC Dallas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodriguez, manager of A D Isidro Metapan, plays for FC Dallas.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jorge_Humberto_Rodríguez",
"property": "manager",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "FC_Dallas",
"property": "club",
"subject": "Jorge_Humberto_Rodríguez"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jorge_Humberto_Rodríguez",
"property": "manager",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "FC_Dallas",
"property": "team",
"subject": "Jorge_Humberto_Rodríguez"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id312"
}
},
{
"313": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "With 1500 members, A.E Dimitra Efxeinoupolis, were in the 2014-15 A EPSTH, Greece.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis were in the 2014–15 A EPSTH. Greece season, and have 1500 members.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1500",
"property": "numberOfMembers",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "2014–15_A_EPSTH,_Greece",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "1500",
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{
"object": "2014–15_A_EPSTH,_Greece",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
[
{
"object": "\"1500\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "2014–15_A_EPSTH,_Greece",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id313"
}
},
{
"314": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker (footballer) was at Blackburn Rovers FC and has also managed AFC Blackpool.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker who played for Blackburn Rovers FC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker is the manager of A.F.C. Blackpool, and plays for Blackburn Rovers F.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Blackburn_Rovers_F.C.",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
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"subject": "A.F.C._Blackpool"
},
{
"object": "Blackburn_Rovers_F.C.",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id314"
}
},
{
"315": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had Stuart Parker as their manager, and he is attached to Irlam Town Football Club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker (footballer) who is a member of the Irlam Town F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Footballer Stuart Parker, attached to Irlam Town Football Club, also manages A.F.C. Blackpool.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Irlam_Town_F.C.",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Irlam_Town_F.C.",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id315"
}
},
{
"316": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ground of AFC Fylde can be found in the county of Lancashire, it has 3180 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of AFC Fylde, which holds 3180 fans, can be found in the county of Lancashire.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of AFC Fylde can be found in the county of Lancashire and they have 3180 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3180",
"property": "numberOfMembers",
"subject": "A.F.C._Fylde"
},
{
"object": "Lancashire",
"property": "ground",
"subject": "A.F.C._Fylde"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lancashire",
"property": "ground",
"subject": "A.F.C._Fylde"
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{
"object": "3180",
"property": "capacity",
"subject": "A.F.C._Fylde"
}
],
[
{
"object": "\"3180\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.F.C._Fylde"
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{
"object": "Lancashire",
"property": "ground",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id316"
}
},
{
"317": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A.S. Gubbio 1910 has 5300 members and has a ground in Gubbio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Gubbio 1910, which has its ground in Gubbio, has 5300 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Gubbio 1910 has 5300 members and a ground in Gubbio.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "5300",
"property": "numberOfMembers",
"subject": "A.S._Gubbio_1910"
},
{
"object": "Gubbio",
"property": "ground",
"subject": "A.S._Gubbio_1910"
}
],
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[
{
"object": "\"5300\"^^xsd:nonNegativeInteger",
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{
"object": "Gubbio",
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}
],
[
{
"object": "Gubbio",
"property": "ground",
"subject": "A.S._Gubbio_1910"
},
{
"object": "5300",
"property": "capacity",
"subject": "A.S._Gubbio_1910"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id317"
}
},
{
"318": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.S Livorno Calcio ground is in Livorno and the club plays in Serie B.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AS Livorno Calcio play in Serie B, and their grounds are in Livorno.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AS Livorno club in Serie B plays in Livorno.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Livorno",
"property": "ground",
"subject": "A.S._Livorno_Calcio"
},
{
"object": "Serie_B",
"property": "league",
"subject": "A.S._Livorno_Calcio"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Livorno",
"property": "ground",
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{
"object": "Serie_B",
"property": "league",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id318"
}
},
{
"319": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Christian Panucci plays at A.S. Roma. He also manages A.S. Livorno Calcio .",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S Livorno Calcio is managed by Christian Panucci, who plays for A S Roma.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio is managed by Christian Panucci who plays at the A.S. Roma.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Christian_Panucci",
"property": "manager",
"subject": "A.S._Livorno_Calcio"
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{
"object": "A.S._Roma",
"property": "club",
"subject": "Christian_Panucci"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Christian_Panucci",
"property": "manager",
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{
"object": "A.S._Roma",
"property": "team",
"subject": "Christian_Panucci"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id319"
}
},
{
"320": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of ACF Fiorentina is Paulo Sousa, his club is Juventus.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Paulo Sousa is the manager of the ACF Fiorentina and his club is Juventus.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Paulo_Sousa",
"property": "manager",
"subject": "ACF_Fiorentina"
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{
"object": "Juventus_F.C.",
"property": "club",
"subject": "Paulo_Sousa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Paulo_Sousa",
"property": "manager",
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{
"object": "Juventus_F.C.",
"property": "team",
"subject": "Paulo_Sousa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id320"
}
},
{
"321": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "ACF Fiorentina has 47290 members and their full name is ACF Fiorentina S.p.A.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The full name of ACF Fiorentia is \"ACF Fiorentina S.p.A.\" and they have 47290 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "ACF Fiorentina (with full name being ACF Fiorentina S.p.A.) has 47290 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "47290",
"property": "numberOfMembers",
"subject": "ACF_Fiorentina"
},
{
"object": "\"ACF Fiorentina S.p.A.\"",
"property": "fullname",
"subject": "ACF_Fiorentina"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "47290",
"property": "capacity",
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{
"object": "\"ACF Fiorentina S.p.A.\"@en",
"property": "fullname",
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],
[
{
"object": "\"ACF Fiorentina S.p.A.\"@en",
"property": "fullname",
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},
{
"object": "\"47290\"^^xsd:nonNegativeInteger",
"property": "capacity",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id321"
}
},
{
"322": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. has 69618 members and its ground is in Greece.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens's ground is in Greece and the club has 69618 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athen's ground is in Greece and has 69618 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greece",
"property": "ground",
"subject": "AEK_Athens_F.C."
},
{
"object": "69618",
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],
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[
{
"object": "Greece",
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{
"object": "\"69618\"^^xsd:nonNegativeInteger",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id322"
}
},
{
"323": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ground for AEK Athens FC, who are in the Superleague of Greece is the Olympic Stadium (Athens).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Olympic Stadium, in Athens, is the home ground of AEK Athens FC which is in the Superleague of Greece.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. play in the Superleague Greece and have the Olympic Stadium (Athens) as their home ground.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Olympic_Stadium_(Athens)",
"property": "ground",
"subject": "AEK_Athens_F.C."
},
{
"object": "Superleague_Greece",
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"subject": "AEK_Athens_F.C."
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],
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[
{
"object": "Olympic_Stadium_(Athens)",
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{
"object": "Superleague_Greece",
"property": "league",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id323"
}
},
{
"324": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs)'s ground is Sportpark De Toekomst where the tenant is Jong Ajax.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jong Ajax is the tenant of Sportpark De Toekomst which is the ground for AFC Ajac (amateurs).",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Jong Ajax are the tenants of the De Toekomst Sportpark which is the ground of AFC Ajax (amateurs).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sportpark_De_Toekomst",
"property": "ground",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "Jong_Ajax",
"property": "tenant",
"subject": "Sportpark_De_Toekomst"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sportpark_De_Toekomst",
"property": "ground",
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{
"object": "Jong_Ajax",
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"subject": "Sportpark_De_Toekomst"
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],
[
{
"object": "Sportpark_De_Toekomst",
"property": "ground",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "Jong_Ajax",
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"subject": "Sportpark_De_Toekomst"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id324"
}
},
{
"325": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZAL Arena is the ground of AZAL PFK which is located in Shuvalan, Baku, Azerbaijan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AZAL Arena, Shuvalan, Baku, Azerbaijan, is the ground of AZAL PFK.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AZAL PFK play their home matches at the AZAL Arena in Shuvalan, Baku, Azerbaijan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "AZAL_Arena",
"property": "ground",
"subject": "AZAL_PFK"
},
{
"object": "\"Shuvalan, Baku, Azerbaijan\"",
"property": "location",
"subject": "AZAL_PFK"
}
],
"originaltriplesets": {
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[
{
"object": "AZAL_Arena",
"property": "ground",
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{
"object": "\"Shuvalan, Baku, Azerbaijan\"@en",
"property": "location",
"subject": "AZAL_PFK"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id325"
}
},
{
"326": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault, 3000 members strong, play in the 2011 PDL season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault; has 3000 members and plays in the 2011 PDL season.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2011_PDL_season",
"property": "season",
"subject": "Akron_Summit_Assault"
},
{
"object": "3000",
"property": "numberOfMembers",
"subject": "Akron_Summit_Assault"
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],
"originaltriplesets": {
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{
"object": "\"3000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "Akron_Summit_Assault"
},
{
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],
[
{
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{
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{
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},
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"lex": "George Winkler, from the United States of America, was the architect of 320 South Boston Building.",
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"lex": "The 3Arena, created by architect HOK SVE, is located at East Link Bridge.",
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"comment": "good",
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"lex": "The Addis Ababa City Hall is located in Addis Ababa, Ethiopia.",
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"lex": "Adisham Hall which is located in Haputale, Sri Lanka has the architectural style Tudor Revival.",
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"comment": "good",
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"comment": "good",
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"lex": "Ampara Hospital is in the Eastern Province, of Sri Lanka whose leader is Austin Fernando.",
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"447": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. (who's grounds are in Athens) is in the Superleague of Greece. Olympiacos FC are previous champions of the Superleague Greece.",
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},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens's ground is in Greece and it competes in the Superleague Greece, the past champions were Olympiacos F.C.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Superleague_Greece",
"property": "league",
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{
"object": "Olympiacos_F.C.",
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"subject": "Superleague_Greece"
},
{
"object": "Greece",
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"subject": "AEK_Athens_F.C."
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],
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{
"object": "Superleague_Greece",
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{
"object": "Olympiacos_F.C.",
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{
"object": "Greece",
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]
]
},
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}
},
{
"448": {
"category": "SportsTeam",
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{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. manager is Gus Poyet who's club is Real Zaragoza, having previously played for Chelsea F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens are managed by Gus Poyet who is associated with the Real Zaragoza,a football club and played for Chelsea F.C.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Gus_Poyet",
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{
"object": "Real_Zaragoza",
"property": "club",
"subject": "Gus_Poyet"
},
{
"object": "Chelsea_F.C.",
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],
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{
"object": "Gus_Poyet",
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{
"object": "Real_Zaragoza",
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{
"object": "Chelsea_F.C.",
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],
[
{
"object": "Gus_Poyet",
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{
"object": "Real_Zaragoza",
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{
"object": "Chelsea_F.C.",
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]
]
},
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}
},
{
"449": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
}
],
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{
"object": "Eberhard_van_der_Laan",
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},
{
"object": "Amsterdam-Centrum",
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"subject": "Amsterdam"
}
],
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{
"object": "Amsterdam",
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{
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},
{
"object": "Amsterdam-Centrum",
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}
],
[
{
"object": "Amsterdam-Centrum",
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},
{
"object": "Amsterdam",
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},
{
"object": "Eberhard_van_der_Laan",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id449"
}
},
{
"450": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Noord is part of Amsterdam whose leader is Eberhard van der Laan. The city is the location of the homeground of AFC Ajax (amateurs).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of AFC Ajax (amateurs) can be found in Amsterdam, which has Eberhard van der Laan as leader, and a region called Amsterdam-Noord.",
"xml_id": "Id2"
}
],
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{
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],
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{
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],
[
{
"object": "Amsterdam",
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{
"object": "Eberhard_van_der_Laan",
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id450"
}
},
{
"451": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "John van den Brom manages the AZ Alkmaar and is in the Jong Ajax club. He currently plays for Istanbulspor A.S.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "John van den Brom, who manages AZ Alkmaar, plays for Jong Ajax and Istanbulspor AS.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of AZ Alkmaar is john van den Brom who is in the Jong Ajax club and plays for Istanbulspor A.S.",
"xml_id": "Id3"
}
],
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{
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{
"object": "İstanbulspor_A.Ş.",
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"subject": "John_van_den_Brom"
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],
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{
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{
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]
},
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}
},
{
"452": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense play in the Campeonato Brasileiro Série C league based in Brazil. Their ground is known as Estádio Municipal Coaracy da Mata Fonseca.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense play in the Campeonato Brasileiro Série C league in Brazil. Their ground is the Estadio Municipal Coaracy da Mata Fonseca.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Agremiação Sportiva Arapiraquense play in the Campeonato Brasileiro Série C league in Brazil. Their ground is known as the Estadio Municipal Coaracy da Mata Fonseca.",
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}
],
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{
"object": "Brazil",
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},
{
"object": "Estádio_Municipal_Coaracy_da_Mata_Fonseca",
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],
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{
"object": "Brazil",
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{
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]
},
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}
},
{
"453": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "The manager of A.S. Roma is Luciano Spalletti who used to play for Udinese Calcio but now plays for Empoli FC.",
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},
{
"comment": "good",
"lang": "",
"lex": "Luciano Spalletti played for Udinese Calcio and now manages A.S. Roma and plays for Empoli F.C..",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Luciano Spalletti plays for Udinese Calcio and Empoli F.C. as well as managing A.S. Roma.",
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}
],
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{
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{
"object": "Empoli_F.C.",
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"subject": "Luciano_Spalletti"
},
{
"object": "Luciano_Spalletti",
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],
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{
"object": "Udinese_Calcio",
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{
"object": "Empoli_F.C.",
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"subject": "Luciano_Spalletti"
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{
"object": "Luciano_Spalletti",
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]
]
},
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}
},
{
"454": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "Massimo Drago was at the club ASD Licita 1931 but currently manages A.C. Cesena and plays for S.S. Chieti Calcio.",
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},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago has been the manager of A C Cesena and was at the club ASD Licita 1931. He now plays for S.S. Chieti Calcio.",
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},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago plays for both A.S.D. Licata 1931 and S.S. Chieti Calcio as well as managing A.C. Cesena.",
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}
],
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"modifiedtripleset": [
{
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{
"object": "Massimo_Drago",
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{
"object": "A.S.D._Licata_1931",
"property": "club",
"subject": "Massimo_Drago"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "S.S._Chieti_Calcio",
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"object": "Massimo_Drago",
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{
"object": "A.S.D._Licata_1931",
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},
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}
},
{
"455": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
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"lex": "Massimo Drago has been the manager of A C Cesena and played for A.S.D. S.S. Nola 1925. He currently plays for U.S Castrovillari Calcio.",
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},
{
"comment": "good",
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"lex": "Massimo Drago plays for U.S Castrovillari Calcio and A.S.D. S.S. Nola 1925. He also manages AC Cesena.",
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},
{
"comment": "good",
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"lex": "Massimo Drago played for A.S.D. S.S. Nola 1925, now he manages A.C. Cesena and plays for U.S Castrovillari Calcio.",
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}
],
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{
"object": "U.S._Castrovillari_Calcio",
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"subject": "Massimo_Drago"
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{
"object": "Massimo_Drago",
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{
"object": "A.S.D._S.S._Nola_1925",
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],
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[
{
"object": "U.S._Castrovillari_Calcio",
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{
"object": "Massimo_Drago",
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{
"object": "A.S.D._S.S._Nola_1925",
"property": "team",
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]
]
},
"shape": "NA",
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"xml_id": "Id455"
}
},
{
"456": {
"category": "SportsTeam",
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{
"comment": "good",
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},
{
"comment": "good",
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"lex": "Massimo Drago's club is the S.S. Chieti Calcio, he also manages A.C. Cesena and was once a player for US Castrovillari Calcio.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago plays for S.S.Chieti Calcio and manages A.C. Cesena, he once also was a player for US Castrovillari Calcio.",
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}
],
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{
"object": "U.S._Castrovillari_Calcio",
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{
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]
},
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}
},
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"457": {
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{
"comment": "good",
"lang": "",
"lex": "103 Colmore Row was designed by John Madin who was born in Birmingham.",
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},
{
"comment": "good",
"lang": "",
"lex": "103 Colmore Row was designed by Birmingham born architect John Madin.",
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},
{
"comment": "good",
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"lex": "103 Colmore Row was designed by the architect, John Madin from Birmingham.",
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],
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{
"object": "Birmingham",
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],
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{
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{
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{
"object": "Birmingham",
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]
},
"shape": "NA",
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}
},
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"458": {
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{
"comment": "good",
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"lex": "200 Public Square is located in Cleveland, part of Cuyahoga County, Ohio which is led by Frank G Jackson.",
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},
{
"comment": "good",
"lang": "",
"lex": "200 Public square is in Cleveland which is part of Cuyahoga County, Ohio. The leader in Cleveland is Frank G Jackson.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Frank G Jackson leads Cleveland, part of Cuyahoga County in Ohio where 200 Public Square is located.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Cleveland",
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{
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{
"object": "Frank_G._Jackson",
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],
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[
{
"object": "Cleveland",
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{
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"subject": "Cleveland"
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{
"object": "Frank_G._Jackson",
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],
[
{
"object": "Cleveland",
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{
"object": "Cuyahoga_County,_Ohio",
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"object": "Frank_G._Jackson",
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]
},
"shape": "NA",
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"xml_id": "Id458"
}
},
{
"459": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "250 Delaware Avenue in the United States has 12 floors that cover 30843.8 square metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "250 Delaware Avenue in the United States has 12 floors with a total floor area of 30843.8 square metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "250 Delaware Avenue is located in the United States. It has a floor area of 30843.8 square metres and a floor count of 12.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
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"subject": "250_Delaware_Avenue"
},
{
"object": "30843.8 (square metres)",
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"subject": "250_Delaware_Avenue"
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{
"object": "12",
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],
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{
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{
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],
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{
"object": "United_States",
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{
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{
"object": "\"12\"^^xsd:positiveInteger",
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]
},
"shape": "NA",
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}
},
{
"460": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "300 North LaSalle is located in Chicago, DuPage County in Illinois, where Rahm Emanuel is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "300 North LaSalle is located in Chicago, part of DuPage County, Illinois where Rahm Emanuel is a leader.",
"xml_id": "Id2"
},
{
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{
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},
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{
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],
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"comment": "good",
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"comment": "good",
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"comment": "good",
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},
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"comment": "good",
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{
"comment": "good",
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{
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{
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"comment": "good",
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"comment": "good",
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"lex": "The Asian Americans are an ethnic group within the United States, which is the location of Albany, part of Dougherty County in Georgia.",
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"comment": "good",
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"lex": "Albany is part of Dougherty County in Georgia, in the United States, where Asian Americans are one of the ethnic groups.",
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"comment": "good",
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"comment": "good",
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"lex": "The New Mexico Senate leads Albuquerque (new Mexico) which has a total area of 490.9 square kilometres and the population density of 1142.3 people per square kilometers.",
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{
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},
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"comment": "good",
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"comment": "good",
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"lex": "Alpharetta is part of Fulton County, Georgia which is in the United States. African Americans are an ethnic group in the US.",
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},
{
"comment": "good",
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"lex": "Fulton County is the location of Alpharetta,Georgia, in the United States, where African Americans are one of the ethnic groups.",
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],
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{
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{
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"comment": "good",
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"comment": "good",
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"lex": "Alpharetta is part of the state of Georgia in the United States. One of the ethnic groups found in this country is Asian Americans.",
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},
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"comment": "good",
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"lex": "Alpharetta, Georgia, is in the United States, where Asian Americans are one of the ethnic groups.",
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}
],
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{
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},
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},
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"comment": "good",
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},
{
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"lex": "The Asian Americans are an ethnic group in the United States which has the capital of Washington DC and is the location of Alpharetta in Georgia.",
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},
{
"comment": "good",
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"lex": "The Asian Americans are an ethnic group in the United States where the capital city is Washington DC and Alpharetta is located in Georgia.",
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}
],
"links": [],
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{
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{
"object": "Washington,_D.C.",
"property": "capital",
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},
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},
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"487": {
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"lexicalisations": [
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Anaheim, California has a total area of 131.6 square kilometres, area codes 657 and 714, and a UTC offset of -7.",
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"comment": "good",
"lang": "",
"lex": "With a total area of 131.6 square kilometres, Anaheim (California), has a UTC offset of - 7 and the area codes: 657, 714.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"-7\"",
"property": "UTCOffset",
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{
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"lex": "Located at the California State Capitol, The California State Senate, leads Anaheim, California.",
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"lex": "Anderson is a city that's part of Lafayette Township, Madison County,Indiana and is found in the United States.",
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"lex": "Angola is in Pleasant Township of Steuben County, Indiana of the United States where Asian Americans are one of several ethnic groups.",
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"comment": "good",
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"lex": "Led by the United States House of Representatives, Antioch is part of Contra Costa County, part of the San Francisco Bay Area.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Led by the United States House of Representatives, Antioch, part of Contra Costa County, which is part of the San Francisco Bay area, in California.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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"subject": "Antioch,_California"
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{
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"comment": "good",
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"lex": "Arlington is located in Tarrant County,Texas whose county seat is Fort Worth in the United States.",
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"comment": "good",
"lang": "",
"lex": "Arlington in the United States is part of Tarrant County in Texas where the county seat is Fort Worth.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Arlington is part of Tarrant County, Texas, where the county seat is Fort Worth.",
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],
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"property": "isPartOf",
"subject": "Arlington,_Texas"
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{
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{
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{
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"lang": "",
"lex": "The Asian Americans are an ethnic group found in the United States which has the capital city of Washington DC and is the location of Atlanta.",
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},
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"comment": "good",
"lang": "",
"lex": "The Asian Americans are an ethnic group in the United States which is the location of Atlanta and has the capital of Washington DC.",
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],
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"modifiedtripleset": [
{
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{
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},
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},
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlanta, which has the area code of 404, has a population density of 1299 inhabitants per sq km and a total area of 347.1 sq km.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "With a total area of 347.1 square kilometres, Atlanta, has a population density of 1299.0 inhabitants per square kilometre, and the area code, 404.",
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}
],
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"modifiedtripleset": [
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},
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},
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"comment": "good",
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"lex": "Atlantic City, New Jersey is in the United States where the largest city in that state is Newark.",
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"comment": "good",
"lang": "",
"lex": "Atlantic City, New Jersey is in the United States. Newark is also the largest city of New Jersey.",
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],
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}
],
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},
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"lex": "Auburn is located in Lee County, Alabama in the U.S.",
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},
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"lex": "The United States has the capital of Washington DC and is the location of Auburn in the state of Washington,.",
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},
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"comment": "good",
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"lex": "Auburn is part of Washington state, in the United States, the capital of which is Washington, D.C.",
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},
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"comment": "good",
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},
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"lex": "Atlanta is the capital of Georgia (United States), the state where Albany, Dougherty County, is located.",
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],
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{
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"property": "subdivisionName",
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},
"shape": "NA",
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},
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"comment": "good",
"lang": "",
"lex": "Alexandria, Indiana is part of Madison County Indiana in the United States. The largest city in Madison County is Anderson.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alexandria in the United States is part of Madison County, Indiana which has the largest city of Anderson.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Madison_County,_Indiana"
},
{
"object": "Madison_County,_Indiana",
"property": "isPartOf",
"subject": "Alexandria,_Indiana"
},
{
"object": "Anderson,_Indiana",
"property": "largestCity",
"subject": "Madison_County,_Indiana"
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],
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[
{
"object": "United_States",
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{
"object": "Madison_County,_Indiana",
"property": "isPartOf",
"subject": "Alexandria,_Indiana"
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{
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"property": "largestCity",
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]
},
"shape": "NA",
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}
},
{
"502": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The capital of the state of New Jersey in the U.S. is Trenton. Atlantic City is also part of that state.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City is in the state of New Jersey, United States, where the Trenton is the capital.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Trenton,_New_Jersey",
"property": "capital",
"subject": "New_Jersey"
},
{
"object": "United_States",
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"subject": "New_Jersey"
},
{
"object": "New_Jersey",
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{
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"property": "capital",
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{
"object": "United_States",
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"subject": "New_Jersey"
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{
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},
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},
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"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arlington is part of Texas where the largest city is Houston and the English language is spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "In Texas; Houston is the largest city, Arlington is part of the state, and English is spoken.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
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"subject": "Texas"
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{
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{
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],
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{
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{
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{
"object": "Houston",
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]
},
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},
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},
{
"comment": "good",
"lang": "",
"lex": "Attica, Fountain County, Indiana is in the United States: where Washington, D.C.. is the capital.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Attica, is part of Fountain County, Indiana, in the United States, the capital of which is, Washington, D.C.",
"xml_id": "Id3"
}
],
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"subject": "Attica,_Indiana"
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{
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],
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{
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{
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],
[
{
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]
},
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}
},
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Attica is in Fountain County Indiana in the United States. Washington DC is the country's capital.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Attica, is in Fountain County, Indiana, in the United States, the capital of which is Washington, D.C.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
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{
"object": "United_States",
"property": "country",
"subject": "Fountain_County,_Indiana"
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{
"object": "Fountain_County,_Indiana",
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],
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},
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}
},
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"comment": "good",
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"lex": "Akron, Ohio is located in the United States; where Washington, D.C. is the capital, and where English is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron, Ohio is located in the United States, where English is spoken, and where the capital is Washington DC.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
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},
{
"object": "English_language",
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{
"object": "United_States",
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],
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{
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]
]
},
"shape": "NA",
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}
},
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"507": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The United States, which includes the ethnic group of Asian Americans, is the location of Akron, Summit County, Ohio.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akron is a part of Summit County, Ohio, in the United States, where Asian Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
},
{
"object": "Summit_County,_Ohio",
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],
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]
},
"shape": "NA",
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},
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"508": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Albany is part of Benton County in Oregon, in the U.S, of which The Asian Americans are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albany is part of Benton County, Oregon, in the United States, where Asian Americans are one of the ethnic groups.",
"xml_id": "Id3"
}
],
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{
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{
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]
},
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},
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"509": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is in the United States; where Barack Obama is the leader and Asian Americans call home.",
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},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is in the United States, of which Barack Obama is a leader and Asian Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asian_Americans",
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{
"object": "Barack_Obama",
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"subject": "United_States"
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{
"object": "United_States",
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"subject": "Albuquerque,_New_Mexico"
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],
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{
"object": "United_States",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id509"
}
},
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"510": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia is in the United States; where English is spoken and African Americans call home.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia is in the US where English is spoken and African Americans are an ethnic group.",
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}
],
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{
"object": "English_language",
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{
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{
"object": "African_Americans",
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{
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]
},
"shape": "NA",
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},
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States, where the Asian Americans are an ethnic group, uses the English language and is the location of Angola, Indiana.",
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},
{
"comment": "good",
"lang": "",
"lex": "Asian Americans are an ethnic group in the U.S which is where they speak english and the city Angola, Indiana is located.",
"xml_id": "Id3"
}
],
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{
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{
"object": "Asian_Americans",
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"object": "Asian_Americans",
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]
},
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"xml_id": "Id511"
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},
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"comment": "good",
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{
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"xml_id": "Id2"
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],
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"property": "operatingOrganisation",
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},
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"comment": "good",
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"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Lake Placid, N.Y. is served by the Adirondack Regional Airport, where the length of the runway is 2003.0. and serves the city of Saranac Lake, New York.",
"xml_id": "Id3"
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],
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},
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},
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}
],
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{
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{
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{
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{
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},
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},
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"515": {
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{
"comment": "good",
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{
"comment": "good",
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"lex": "The Agra Airport (VIAG) in Uttar Pradesh is operated by the Indian Air Force.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Indian Air Force operates Agra Airport which is located in Uttar Pradesh and has the ICAO location identifier VIAG.",
"xml_id": "Id3"
}
],
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"property": "location",
"subject": "Agra_Airport"
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{
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{
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{
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{
"comment": "good",
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"lex": "In 1986 the USAF bombed Libya. It operates the Al Asad airbase and uses the McDonnell Douglas F-15 Eagle fighter jet.",
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}
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},
{
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}
],
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"xml_id": "Id518"
}
},
{
"519": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alderney Airport, whose runway is 877 m, serves the city of Alderney. Its surface is poaceae.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
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"property": "1st_runway_SurfaceType",
"subject": "Alderney_Airport"
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{
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"property": "runwayLength",
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{
"object": "Alderney",
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"object": "Alderney",
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]
},
"shape": "NA",
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"xml_id": "Id519"
}
},
{
"520": {
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"comment": "good",
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},
{
"comment": "good",
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"lex": "Located in Pakistan, Allama Iqbal International airport is operated by the Pakistan Civil Aviation Authority, the headquarters of which is the Jinnah International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Located in Pakistan, Allama Iqbal International airport is governed by the Pakistan Civil Aviation Authority which has its headquarters at Jinnah International Airport.",
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}
],
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{
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}
},
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport, whose runway name is 18L/36 is located in Punjab, Pakistan. Its length is 2900.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport is located in Punjab, Pakistan, with the runway's name is 18L/36R, and the length is 29000.0.",
"xml_id": "Id3"
}
],
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"property": "location",
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]
},
"shape": "NA",
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"xml_id": "Id521"
}
},
{
"522": {
"category": "Airport",
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"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "With an elevation of 210 metres above sea level, Alpena County Regional Airport is located in Maple Ridge Township, Alpena County, Michigan, in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "At 210 metres above sea level, Alpena County Regional Airport is located in Maple Ridge Township, Alpena County, Michigan, in the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport is in Maple Ridge Township, Alpena County, Michigan, US and is 210m above sea level.",
"xml_id": "Id3"
}
],
"links": [],
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"property": "elevationAboveTheSeaLevel_(in_metres)",
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"object": "Maple_Ridge_Township,_Alpena_County,_Michigan",
"property": "location",
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{
"object": "United_States",
"property": "country",
"subject": "Maple_Ridge_Township,_Alpena_County,_Michigan"
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{
"object": "United_States",
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],
[
{
"object": "\"210.312\"^^xsd:double",
"property": "elevation",
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{
"object": "United_States",
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"subject": "Maple_Ridge_Township,_Alpena_County,_Michigan"
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id522"
}
},
{
"523": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "The runway length at Andrews County airport ( located in Texas and 973.0 above sea level), is 1773.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Located in Texas, Andrews County Airport, is 973.0 above sea level and has a runway length of 1773 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas and is 973 metres above sea level. Its runway length is 1773.0.",
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}
],
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{
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{
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]
},
"shape": "NA",
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"xml_id": "Id523"
}
},
{
"524": {
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Located in Texas, Andrews County Airport is 973.0 above sea level and has a runway length of 896.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport which is located in Texas is 973 metres above sea level and has a runway length of 896.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "Texas",
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{
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[
{
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{
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]
},
"shape": "NA",
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"xml_id": "Id524"
}
},
{
"525": {
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"lexicalisations": [
{
"comment": "good",
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"lex": "Andrews County Airport is located in Texas , Austin and the language spoken in Texas is English.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Austin is the capital of Texas, where they speak English, and Andrews County Airport is located.",
"xml_id": "Id2"
}
],
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"modifiedtripleset": [
{
"object": "Texas",
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{
"object": "Austin,_Texas",
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{
"object": "English_language",
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{
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]
},
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"xml_id": "Id525"
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},
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"526": {
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas in the United States where the inhabitants have the demonym tejano,.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas, United States where the inhabitants have the demonym of Tejano.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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"object": "Texas",
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{
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id526"
}
},
{
"527": {
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"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves the city of Antwerp which is in Belgium and one of the spoken languages is German.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp, served by Antwerp International airport, is a popular tourist destination in Belgium. German is spoken in Belgium.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp is located in German speaking part of Belgium and served by the International airport.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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"object": "Antwerp",
"property": "cityServed",
"subject": "Antwerp_International_Airport"
},
{
"object": "German_language",
"property": "language",
"subject": "Belgium"
},
{
"object": "Belgium",
"property": "country",
"subject": "Antwerp"
}
],
"originaltriplesets": {
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{
"object": "Antwerp",
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{
"object": "German_language",
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"subject": "Belgium"
},
{
"object": "Belgium",
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]
},
"shape": "NA",
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"xml_id": "Id527"
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},
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is located at Egg Harbor Township, New Jersey, part of Atlantic County, New Jersey, in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is in Egg Harbor Township, Atlantic County, New Jersey, United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Atlantic_County,_New_Jersey",
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}
],
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{
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{
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},
{
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],
[
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
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"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Atlantic_County,_New_Jersey",
"property": "subdivisionName",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id528"
}
},
{
"529": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Port Authority of New York and New Jersey is headquartered in the Four World Trade Center and serves New Jersey. It operates the Atlantic City International Airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is operated by the Port Authority of New York and New Jersey located at Four World Trade Centre and serves the New Jersey region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Four World Trade Center is the headquarters of the Port Authority of New York and New Jersey, which operates the Atlantic City International Airport and serves New Jersey.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "operatingOrganisation",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "New_Jersey",
"property": "regionServed",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
},
{
"object": "Four_World_Trade_Center",
"property": "headquarter",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
}
],
"originaltriplesets": {
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[
{
"object": "Port_Authority_of_New_York_and_New_Jersey",
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},
{
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},
{
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]
]
},
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"xml_id": "Id529"
}
},
{
"530": {
"category": "Airport",
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"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is in Egg Harbor Township, New Jersey, United States and has a runway length of 3048.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport can be found in Egg Harbor Township, New Jersey of the U.S.A. and it has a runway that's 3.048 long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "runwayLength",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
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}
],
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{
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{
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},
{
"object": "United_States",
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]
]
},
"shape": "NA",
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"xml_id": "Id530"
}
},
{
"531": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International airport is located in Lahore, Pakistan. The leader of the country is Anwar Zaheer Jamali.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lahore, Pakistan is served by Allama Iqbal International Airport, and is led by Anwar Zaheer Jamali.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
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"subject": "Lahore"
},
{
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"subject": "Allama_Iqbal_International_Airport"
},
{
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"subject": "Pakistan"
}
],
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{
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{
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},
{
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}
],
[
{
"object": "Pakistan",
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{
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{
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],
[
{
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{
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},
{
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"subject": "Pakistan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id531"
}
},
{
"532": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader party in Madrid (which is part of the community of Madrid and has the Adolfo Suarez Madrid-Barajas Airport) is Ahora Madrid.",
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},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport is located in Madrid which is part of the community of Madrid led by Ahora Madrid.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Community_of_Madrid",
"property": "isPartOf",
"subject": "Madrid"
},
{
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},
{
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],
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},
{
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]
]
},
"shape": "NA",
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}
},
{
"533": {
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{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport is found in Madrid, Spain where the leading party is Ahora Madrid.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ahora Madrid is the leading party of Madrid, Spain. Adolfo Suárez Madrid–Barajas Airport is found in Madrid.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ahora_Madrid",
"property": "leaderParty",
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},
{
"object": "Madrid",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
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{
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],
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{
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{
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{
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]
]
},
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}
},
{
"534": {
"category": "Airport",
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{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport is found in San Sebastián de los Reyes (Spain) where the lead party is the People's Party.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The People's Party is the lead party of San Sebastian de los Reyes, Spain where the Adolfo Suarez Madrid-Barajas airport is located.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "People's_Party_(Spain)",
"property": "leaderParty",
"subject": "San_Sebastián_de_los_Reyes"
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{
"object": "San_Sebastián_de_los_Reyes",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "Spain",
"property": "country",
"subject": "San_Sebastián_de_los_Reyes"
}
],
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{
"object": "People's_Party_(Spain)",
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{
"object": "San_Sebastián_de_los_Reyes",
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"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "Spain",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id534"
}
},
{
"535": {
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{
"comment": "good",
"lang": "",
"lex": "The United States 11th Mississippi Infantry Monument's location is Adams County, Pennsylvania, northwest of Carroll County Maryland.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"United States\"",
"property": "country",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "location",
"subject": "11th_Mississippi_Infantry_Monument"
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],
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{
"object": "\"United States\"@en",
"property": "country",
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{
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{
"object": "Adams_County,_Pennsylvania",
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]
]
},
"shape": "NA",
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},
{
"536": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is placed in the municipality of Gettysburg, Adams County, Pennsylvania, northwest of Carroll County Maryland.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
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{
"object": "Gettysburg,_Pennsylvania",
"property": "municipality",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
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],
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{
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{
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"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
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]
]
},
"shape": "NA",
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}
},
{
"537": {
"category": "Monument",
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{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial, known locally as Türk Şehitleri Anıtı, is located in Baku, the capital city of Azerbaijan.",
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}
],
"links": [],
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{
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"property": "capital",
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{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
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{
"object": "\"Türk Şehitleri Anıtı\"",
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"subject": "Baku_Turkish_Martyrs'_Memorial"
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],
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[
{
"object": "Baku",
"property": "capital",
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{
"object": "Azerbaijan",
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{
"object": "\"Türk Şehitleri Anıtı\"@en",
"property": "nativeName",
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]
]
},
"shape": "NA",
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},
{
"538": {
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{
"comment": "good",
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"lex": "The Baku Turkish Martyrs' Memorial is located in Azerbaijan, where the leader is Artur Rasizade, and legislature is based in the National Assembly.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is in Azerbaijan, where the leader is Artur Rasizade, and the legislature is dictated by the National Assembly.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is in Azerbaijan, where the leader is Artur Rasizade, and legislature is dictated by the National Assembly.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Artur_Rasizade",
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{
"object": "Azerbaijan",
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"subject": "Baku_Turkish_Martyrs'_Memorial"
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{
"object": "National_Assembly_(Azerbaijan)",
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],
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{
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{
"object": "Azerbaijan",
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{
"object": "National_Assembly_(Azerbaijan)",
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]
]
},
"shape": "NA",
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"xml_id": "Id538"
}
},
{
"539": {
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{
"comment": "good",
"lang": "",
"lex": "The Ataturk Monument (Izmir) can be found in Turkey, where the capital is Ankara and leader is Ahmet Davutoglu.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ahmet Davutoglu is a leader from Turkey whose capital is Ankara and where the Ataturk Monument can be found in Izmir.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument (İzmir) is found in Turkey, where the capital is Ankara and the leader is Ahmet Davutoğlu.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ahmet_Davutoğlu",
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"subject": "Turkey"
},
{
"object": "Ankara",
"property": "capital",
"subject": "Turkey"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
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],
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{
"object": "Ahmet_Davutoğlu",
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{
"object": "Ankara",
"property": "capital",
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},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
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]
]
},
"shape": "NA",
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"xml_id": "Id539"
}
},
{
"540": {
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{
"comment": "good",
"lang": "",
"lex": "Guanciale is an ingredient used in the preparation of Amatriciana sauce which is a traditional Italian sauce, that comes from the region of Lazio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Guanciale is an ingredient in Amatriciana sauce which comes from the Lazio region in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana, a sauce containing Guanciale, is a traditional Italian sauce from the Lazio region.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lazio",
"property": "region",
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{
"object": "Italy",
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{
"object": "Guanciale",
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],
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{
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{
"object": "Italy",
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{
"object": "Guanciale",
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]
},
"shape": "NA",
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}
},
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"541": {
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{
"comment": "good",
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"lex": "A dish of Javanese cuisine, Arem arem has compressed rice cooked in banana leaf with vegetables or minced meat fillings.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Arem arem is a Javanese dish and the main ingredients of Arem-arem are compressed rice cooked in banana leaf with vegetables or minced meat fillings.",
"xml_id": "Id2"
}
],
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"modifiedtripleset": [
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{
"object": "Javanese_cuisine",
"property": "region",
"subject": "Arem-arem"
},
{
"object": "\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\"",
"property": "mainIngredients",
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],
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{
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{
"object": "\"compressed rice cooked inbanana leafwith vegetables or minced meat fillings\"",
"property": "ingredientName",
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},
{
"object": "Javanese_cuisine",
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],
[
{
"object": "Banana_leaf",
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{
"object": "Javanese_cuisine",
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{
"object": "\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\"@en",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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}
},
{
"542": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomato, used when making Arrabbiata sauce, is part of the flowering plant division and the Solanaceae family.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Tomato, an ingredient of Arrabbiata sauce, is part of the flowering plant division and belongs to the family of solanaceae.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient found in Arrabbiata sauce is the tomato, which belongs to the solanaceae family and is pat of the flowering plant division.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Tomato"
},
{
"object": "Solanaceae",
"property": "family",
"subject": "Tomato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Tomato"
},
{
"object": "Solanaceae",
"property": "family",
"subject": "Tomato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id542"
}
},
{
"543": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Rome, Italy is where the sauce called arrabbiata is from, it includes olive oil as an ingredient used in it's preparation.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is made from olive oil and comes from Rome in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is a traditional dish from Rome, Italy. Olive oil is one of the ingredients in the sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Olive_oil",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Olive_oil",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Olive_oil",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id543"
}
},
{
"544": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomatoes are found in Arrabbiata sauce, which is a dish from Rome in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce originates from the region of Rome, Italy, and tomato is one of its ingredients.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id544"
}
},
{
"545": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Fish cooked in hot and sour sauce is called asam pedas and can be found in the Malay Peninsula region of Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredient of asam pedas, which hails from Malaysia, Peninsula, is fish cooked in a sour and hot sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food from the Malay Peninsula region and the main ingredients are fish cooked in a sour and hot sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "mainIngredients",
"subject": "Asam_pedas"
},
{
"object": "Malay_Peninsula",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "ingredientName",
"subject": "Asam_pedas"
},
{
"object": "Malay_Peninsula",
"property": "region",
"subject": "Asam_pedas"
}
],
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"@en",
"property": "mainIngredient",
"subject": "Asam_pedas"
},
{
"object": "Malay_Peninsula",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id545"
}
},
{
"546": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia, where the capital is Kuala Lumpur, and Arifin Zakaria is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The capitol city of Malaysia is Kuala Lumpur, the country is led by Arifin Zakaria and is home to the dish asam pedas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Kuala Lumpur is the capital of Malaysia. The leader is Arifin Zakaria and they eat asam pedas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Arifin_Zakaria",
"property": "leaderName",
"subject": "Malaysia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Arifin_Zakaria",
"property": "leader",
"subject": "Malaysia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id546"
}
},
{
"547": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "From Indonesia, Ayam penyet, is a popular dish in Malaysia. Malaysian Chinese is an ethnic group from Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam Penyet is found in Indonesia and Malaysia, home of Malaysian Chinese.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Malaysian_Chinese",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Malaysian_Chinese",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id547"
}
},
{
"548": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A variation on the club sandwich, BLT, has lettuce as an ingredient. A variation of the BLT is a bacon sandwich.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lettuce is an ingredient in a BLT which is a variation of a Club Sandwich. While the Bacon sandwich is a variation of BLT.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lettuce",
"property": "ingredient",
"subject": "BLT"
},
{
"object": "Club_sandwich",
"property": "dishVariation",
"subject": "BLT"
},
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "Lettuce",
"property": "ingredient",
"subject": "BLT"
},
{
"object": "Club_sandwich",
"property": "hasVariant",
"subject": "BLT"
}
],
[
{
"object": "Lettuce",
"property": "ingredient",
"subject": "BLT"
},
{
"object": "Club_sandwich",
"property": "variations",
"subject": "BLT"
},
{
"object": "BLT",
"property": "hasVariant",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id548"
}
},
{
"549": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion come from the United States where Barack Obama is the leader and white Americans are an ethnic group.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion is found in United States, led by Barack Obama and inhabited by White Americans.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id549"
}
},
{
"550": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion originates in the United States, where John Roberts is a leader and the capital is Washington DC.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leader",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id550"
}
},
{
"551": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The BLT and bacon sandwich are variations of a club sandwich which has bread as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The BLT is a variation on the club sandwich or the bacon sandwich, all three are made with bread.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Another variation for a bacon sandwich is a BLT, a variation on a club sandwich with bread as an ingredient.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "dishVariation",
"subject": "BLT"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "hasVariant",
"subject": "BLT"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
[
{
"object": "Club_sandwich",
"property": "variations",
"subject": "BLT"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "BLT",
"property": "hasVariant",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id551"
}
},
{
"552": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is one of the ingredients of Baked Alaska which comes from the region of New York and France.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is an ingredient of Baked Alaska which is claimed to come from France and New York.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is an ingredient of Baked Alaska, which it is claimed originated in New York and France.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id552"
}
},
{
"553": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of France is Manuel Valls where the Euro is the currency and the dish baked Alaska is from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The currency of France is the Euro, it is also where Manuel Valls is a leader and baked Alaska is a popular dish.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
},
{
"object": "Euro",
"property": "currency",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leader",
"subject": "France"
},
{
"object": "Euro",
"property": "currency",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id553"
}
},
{
"554": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is a dish found in the USA which contains christmas pudding and comes from New York.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska contains Christmas pudding and is from the New York region of the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Christmas pudding is an ingredient of Baked Alaska which is a dish from the New York region and found in the whole of the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id554"
}
},
{
"555": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is an ingredient of Baked Alaska which is a dessert. Another type of dessert is a cookie.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is an ingredient in the dessert Baked Alaska, Another type of desert is a cookie.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is a part of the dessert Baked Alaska, cookies are also a dessert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id555"
}
},
{
"556": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding's main ingredients are almond, jam, butter and eggs. It is a dessert from the Derbyshire Dales region.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding is a dessert that originates from the Derbyshire Dales and contains the ingredients ground almonds, jam, butter and eggs.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bakewell pudding are ground almond, jam, butter, eggs. It is classed as a dessert and comes from the region of the Derbyshire Dales.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "\"Dessert\"",
"property": "course",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "mainIngredients",
"subject": "Bakewell_pudding"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "\"Dessert\"@en",
"property": "course",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"@en",
"property": "mainIngredient",
"subject": "Bakewell_pudding"
}
],
[
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "ingredientName",
"subject": "Bakewell_pudding"
},
{
"object": "\"Dessert\"@en",
"property": "course",
"subject": "Bakewell_pudding"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id556"
}
},
{
"557": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variation of Bakewell pudding which originates from the Derbyshire Dales. Some main ingredients in Bakewell pudding are ground almonds, jam, butter and eggs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bakewell pudding are ground almond, jam, butter and eggs. It is a variant of Bakewell tart and comes from the Derbyshire Dales region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding has ground almond, jam, butter and eggs as main ingredients. It is a variation of Bakewell pudding and originates from the Derbyshire Dales.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "mainIngredients",
"subject": "Bakewell_pudding"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "ingredientName",
"subject": "Bakewell_pudding"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id557"
}
},
{
"558": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef is an ingredient of Bakso which comes from and is found in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef is an ingredient of Bakso which is a dish that comes from a region in Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef is an ingredient of Bakso which comes from the region of Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "Beef",
"property": "ingredient",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
},
{
"object": "Beef",
"property": "ingredient",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id558"
}
},
{
"559": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Part of Colombian cuisine, Bandeja paisa is a traditional dish from the Paisa region. One of the main ingredients in this dish is avocado.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Avocado is one of the ingredients in the dish Bandeja paisa, which is a dish from Colombian cuisine. It originates from the Paisa region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Avocado is one of the ingredients in the dish Bandeja paisa, which is typical Colombian cuisine originating from the Paisa region.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Avocado",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Avocado",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id559"
}
},
{
"560": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a traditional dish from the Paisa region and part of Colombian cuisine. Hogao is an ingredient of Bandeja paisa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a traditional dish from the Paisa region of Colombia and includes the ingredient hogao.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Hogao is an ingredient of Bandeja paisa cuisine which is from the Paisa region of Colombia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Hogao",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Hogao",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id560"
}
},
{
"561": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes come from France where two of the leaders are Claude Bartolone and Manuel Valls.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes come from France. Claude Bartolone and Manuel Valls are leaders in France.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes can be found in France where Claude Bartolone and Manuel Valls are leaders.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leader",
"subject": "France"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
},
{
"object": "Manuel_Valls",
"property": "leader",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id561"
}
},
{
"562": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Eaten in the Philippines, batchoy has the main ingredients, noodles, pork organs, vegetables, chicken, shrimp, and beef.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a dish eaten in the Philippines. It is a noodle dish which includes pork organs, vegetables, chicken, shrimp and beef.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Noodles are an ingredient in batchoy, it also includes pork organs, vegetables, chicken, shrimp and beef. It hails from the Philippines.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"",
"property": "mainIngredients",
"subject": "Batchoy"
},
{
"object": "Noodle",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"@en",
"property": "mainIngredient",
"subject": "Batchoy"
},
{
"object": "Noodle",
"property": "ingredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id562"
}
},
{
"563": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of batchoy are noodles, pork organs, vegetables, chicken, shrimp, and beef. It is eaten in the Philippines.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is eaten in the Philippines and the main ingredients are noodles, pork organs, vegetables, chicken, shrimp and beef.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Philippines is the country Batchoy comes from, its main ingredients are noodles, pork organs, vegetables, chicken, shrimp and beef.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"",
"property": "mainIngredients",
"subject": "Batchoy"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"@en",
"property": "mainIngredient",
"subject": "Batchoy"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Batchoy"
}
],
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Batchoy"
},
{
"object": "\"noodles,porkorgans,vegetables,chicken,shrimp,beef\"",
"property": "ingredientName",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id563"
}
},
{
"564": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tony Tan is a leader in Singapore, where Beef kway teow is from. The dish is popular nationwide in Singapore and Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A popular dish in Singapore and Indonesia is beef kway teow, Tony Tan is the leader of Singapore.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"@en",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id564"
}
},
{
"565": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient in beef kway teow, from the Singapore region and popular in Indonesia, is sesame oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is from Singapore and Indonesia and is made with sesame oil.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow comes from the Singapore region of Indonesia and has sesame oil as an ingredient.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id565"
}
},
{
"566": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow originates from Singapore and is also made in Indonesia. One of the ingredients in the dish is oyster sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish commonly found in Singapore, the Singapore region and Indonesia. It contains oyster sauce among the ingredients.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id566"
}
},
{
"567": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country India, which leader is Sumitra Mahajan and its currency is the Indian rupee.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leader",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id567"
}
},
{
"568": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Banana is an ingredient in binignit which is a dessert. A cookie is also a dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Cookies and Binignit, which contains banana as a key ingredient, are types of desserts and should be served as that course.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Binignit is a dessert which contains the ingredient of bananas. Cookie is another dessert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id568"
}
},
{
"569": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Binignit is a type of dessert with the ingredient sago, a cookie is also a dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Cookies and Binignit are types of dessert. A key ingredient of Binignit is Sago.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sago is one of the ingredients in the dish Binignit which is a dessert course. Cookies are also a type of dessert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sago",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sago",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id569"
}
},
{
"570": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Taro is an ingredient in Binignit which is a dessert. Similarly, a cookie is a dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Taro is an ingredient in Binignit which should be served as the dessert course. A cookie is a dessert.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Cookie is a type of dessert, sames as Binignit, that is an ingredient found in taro.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Taro",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Taro",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id570"
}
},
{
"571": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sweet potato, from the Solanales order of plants, and banana are two of the key ingredients in Binignit.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredient of binignit is sweet potato, which is of the order of Solanales. Banana is another ingredient of the dish.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sweet potato are part of the order of Solanales and are one of the main ingredients of binignit, along with banana.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id571"
}
},
{
"572": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Found in Mexico, the food, Bionico (with granola as an ingredient), is served at the dessert course.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is served as a dessert course. It is found in Mexico and requires granola as an ingredient.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id572"
}
},
{
"573": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a food which contains raisins from Guadalajara, Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico, which contains raisins, is from the Guadalajara region of Mexico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico can be found in Guadalajara, Mexico, and it contains raisins.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Raisin",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Raisin",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id573"
}
},
{
"574": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Silvano Aureoles Conejo is the leader in Mexico. The country where the currency is the peso and where the food Bionico is found.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The name of the leader in Mexico is Silvano Aureoles Conejo whose currency is the Mexican peso. Bionico is a food found in that country.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is from Mexico where Silvano Aureoles Conejo is the leader and the currency is the Mexican peso.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Mexican_peso",
"property": "currency",
"subject": "Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leader",
"subject": "Mexico"
},
{
"object": "Mexican_peso",
"property": "currency",
"subject": "Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id574"
}
},
{
"575": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce comes from Italy which has the capital city of Rome and is led by Pietro Grasso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italy is the country Amatriciana sauce comes from. The capital of Italy is Rome and the leader is Pietro Grasso.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Pietro_Grasso",
"property": "leaderName",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Pietro_Grasso",
"property": "leader",
"subject": "Italy"
}
],
[
{
"object": "Pietro_Grasso",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id575"
}
},
{
"576": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a dish from the Philippines, where Arabic is spoken and where one of the ethnic groups is the Igorot people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a dish from the Philippines, where Igorot people are part of one of the ethnic groups and where one of the languages spoken is Arabic.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy comes from the Philippines where the Igorot people are an ethnic group and one of the languages is Arabic.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id576"
}
},
{
"577": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Singapore is Tony Tan where English is spoken and beef kway teow is a popular dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore where Tony Tan is the leader and the English language is spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tony Tan is a leader in Singapore, where english is spoken and Beef kway teow is a popular dish.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tony_Tan",
"property": "leader",
"subject": "Singapore"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
}
],
[
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id577"
}
},
{
"578": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomatoes which are part of the solanaceae family and solanales order are a key ingredient in Amatriciana sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The tomato, used in an Amatriciana sauce, belongs to the family Solanaceae and is of the order Solanales.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tomatoes belong to the order of solanales, the family of solanaceae, and are an ingredient in Amatriciana sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Solanaceae",
"property": "family",
"subject": "Tomato"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
},
{
"object": "Solanales",
"property": "order",
"subject": "Tomato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Solanaceae",
"property": "family",
"subject": "Tomato"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
},
{
"object": "Solanales",
"property": "order",
"subject": "Tomato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id578"
}
},
{
"579": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College is located in Kerala, India and it has 250 academic staff.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College has an academic staff of 250 and is located in the state of Kerala, India.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College in Kerala, India has a staff of 250.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "AWH_Engineering_College"
},
{
"object": "250",
"property": "academicStaffSize",
"subject": "AWH_Engineering_College"
},
{
"object": "Kerala",
"property": "state",
"subject": "AWH_Engineering_College"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "AWH_Engineering_College"
},
{
"object": "\"250\"^^xsd:nonNegativeInteger",
"property": "facultySize",
"subject": "AWH_Engineering_College"
},
{
"object": "Kerala",
"property": "state",
"subject": "AWH_Engineering_College"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id579"
}
},
{
"580": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology is in Bangalore. It has 700 postgraduate students and is affiliated with the Visvesvaraya Technological University.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology, Bangalore, has 700 postgraduate students and is affiliated with Visvesvaraya Technological University.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bangalore",
"property": "city",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "700",
"property": "numberOfPostgraduateStudents",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Visvesvaraya_Technological_University",
"property": "affiliation",
"subject": "Acharya_Institute_of_Technology"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bangalore",
"property": "city",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"~700\"@en",
"property": "postgrad",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Visvesvaraya_Technological_University",
"property": "affiliations",
"subject": "Acharya_Institute_of_Technology"
}
],
[
{
"object": "Bangalore",
"property": "city",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"700\"^^xsd:nonNegativeInteger",
"property": "numberOfPostgraduateStudents",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Visvesvaraya_Technological_University",
"property": "affiliations",
"subject": "Acharya_Institute_of_Technology"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id580"
}
},
{
"581": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College is located in Kerala. The leader of Kerala is Kochi. To the northwest of Kerala is Mahe, India.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Kochi is the leader of Kerala which is southeast of Mahe India, and the location of the AWH Engineering College.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AWH Engineering College in Kerala is southeast of Mahe in India. The leader of Kerala is named Kochi.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kochi",
"property": "leaderName",
"subject": "Kerala"
},
{
"object": "Kerala",
"property": "state",
"subject": "AWH_Engineering_College"
},
{
"object": "Mahé,_India",
"property": "has to its northwest",
"subject": "Kerala"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Kochi",
"property": "leaderName",
"subject": "Kerala"
},
{
"object": "Kerala",
"property": "state",
"subject": "AWH_Engineering_College"
},
{
"object": "Mahé,_India",
"property": "northwest",
"subject": "Kerala"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id581"
}
},
{
"582": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Romania's ethnic group is Germans of Romania and its university is 1 Decembrie 1918 University. Its anthem is called Desteapta-te romane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Romania is the home of the ethnic group of Germans of Romania, and is the location of the 1 Decembrie 1918 University. The Romanian anthem is Desteapta-te romane.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1 Decembrie 1918 University is located in Romania, which is ethnically German and the national anthem is Deșteaptă-te, române!",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germans_of_Romania",
"property": "ethnicGroup",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University"
},
{
"object": "Deșteaptă-te,_române!",
"property": "anthem",
"subject": "Romania"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Germans_of_Romania",
"property": "ethnicGroups",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
},
{
"object": "Deșteaptă-te,_române!",
"property": "anthem",
"subject": "Romania"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id582"
}
},
{
"583": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1 Decembrie 1918 University is in Romania. Romania is led by Klaus Iohannis, a member of Germans of Romania.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 1 Decembrie 1918 University is located in Romania. The Romanian leader is called Klaus Iohannis and one of the country's ethnic groups are the Germans of Romania.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germans_of_Romania",
"property": "ethnicGroup",
"subject": "Romania"
},
{
"object": "Klaus_Iohannis",
"property": "leaderName",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Germans_of_Romania",
"property": "ethnicGroups",
"subject": "Romania"
},
{
"object": "Klaus_Iohannis",
"property": "leaderName",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
}
],
[
{
"object": "Germans_of_Romania",
"property": "ethnicGroup",
"subject": "Romania"
},
{
"object": "Klaus_Iohannis",
"property": "leaderName",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
}
],
[
{
"object": "Klaus_Iohannis",
"property": "leader",
"subject": "Romania"
},
{
"object": "Germans_of_Romania",
"property": "ethnicGroup",
"subject": "Romania"
},
{
"object": "Romania",
"property": "country",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id583"
}
},
{
"584": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The School of Business and Social Sciences at the Aarhus University was established in 1928. It is affiliated with the European University Association in Brussels.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The School of Business and Social Sciences at the Aarhus University was established 1928 and is affiliated with the European University Association (which has its headquarters in Brussels).",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "European_University_Association",
"property": "affiliation",
"subject": "School of Business and Social Sciences at the Aarhus University"
},
{
"object": "Brussels",
"property": "headquarters",
"subject": "European_University_Association"
},
{
"object": "1928",
"property": "established",
"subject": "School of Business and Social Sciences at the Aarhus University"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "European_University_Association",
"property": "affiliation",
"subject": "Aarhus_University,_School_of_Business_and_Social_Sciences"
},
{
"object": "Brussels",
"property": "headquarters",
"subject": "European_University_Association"
},
{
"object": "1928",
"property": "established",
"subject": "Aarhus_University,_School_of_Business_and_Social_Sciences"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id584"
}
},
{
"585": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The School of Business and Social Sciences at the Aarhus University is in Aarhus, Denmark. It was established in 1928.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The School of Business and Social Sciences at the Aarhus University, Denmark, was established in 1928.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Aarhus",
"property": "city",
"subject": "School of Business and Social Sciences at the Aarhus University"
},
{
"object": "Denmark",
"property": "country",
"subject": "School of Business and Social Sciences at the Aarhus University"
},
{
"object": "1928",
"property": "established",
"subject": "School of Business and Social Sciences at the Aarhus University"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Aarhus",
"property": "city",
"subject": "Aarhus_University,_School_of_Business_and_Social_Sciences"
},
{
"object": "Denmark",
"property": "country",
"subject": "Aarhus_University,_School_of_Business_and_Social_Sciences"
},
{
"object": "1928",
"property": "established",
"subject": "Aarhus_University,_School_of_Business_and_Social_Sciences"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id585"
}
},
{
"586": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
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},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id621"
}
},
{
"622": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial in Azerbaijan, which has Baku as its capital, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku and is known locally as Türk Sehitleri Aniti.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs memorial, also known as Turk Sehitleri Aniti, is dedicated to Ottoman soldiers killed in the battle of Baku. It is located in Baku, the capital of Azerbaijan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
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"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"@en",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id622"
}
},
{
"623": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 14th New Jersey Volunteer Infantry Monument was established 1907-07-11 at the Monocacy National Battlefield in the county of Frederick, Maryland, with Frederick, Maryland as its nearest city.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 14th New Jersey Volunteer Infantry Monument was established on July 11th, 1907and located in the Monocacy National Battlefield in the county Frederick, Maryland. The city nearest to Monocacy National Battlefield is Frederick, Maryland.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Frederick_County,_Maryland",
"property": "location",
"subject": "Monocacy_National_Battlefield"
},
{
"object": "\"1907-07-11\"",
"property": "established",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Monocacy_National_Battlefield",
"property": "district",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Frederick,_Maryland",
"property": "nearestCity",
"subject": "Monocacy_National_Battlefield"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Frederick_County,_Maryland",
"property": "location",
"subject": "Monocacy_National_Battlefield"
},
{
"object": "\"1907-07-11\"^^xsd:date",
"property": "established",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Monocacy_National_Battlefield",
"property": "district",
"subject": "14th_New_Jersey_Volunteer_Infantry_Monument"
},
{
"object": "Frederick,_Maryland",
"property": "nearestCity",
"subject": "Monocacy_National_Battlefield"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id623"
}
},
{
"624": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The capital of Turkey is Ankara and the leader is Ahmet Davutoglu. Turkey is the location of the bronze Ataturk monument.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument in Izmir is made of bronze, is located in Turkey, which is led by Ahmet Davutoglu and has Ankara as its capital.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ahmet Davutoglu is the leader of Turkey where the capital is Ankara. The Ataturk monument (Izmir) which is made of bronze is located within the country.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ahmet_Davutoğlu",
"property": "leaderName",
"subject": "Turkey"
},
{
"object": "Ankara",
"property": "capital",
"subject": "Turkey"
},
{
"object": "\"Bronze\"",
"property": "material",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ahmet_Davutoğlu",
"property": "leaderName",
"subject": "Turkey"
},
{
"object": "Ankara",
"property": "capital",
"subject": "Turkey"
},
{
"object": "\"Bronze\"@en",
"property": "material",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id624"
}
},
{
"625": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument in Izmir, Turkey, which is governed by a president, was designed by Pietro Canonica and was inaugurated on July 27th, 1932.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "President of Turkey is the official title of the Turkish leader. The Atatürk Monument (İzmir) designed by Pietro Canonica is found in Turkey and inaugurated on the July 27th, 1932.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Pietro Canonica designed the Ataturk monument (Izmir) which was inaugurated in Turkey on 27 July 1932. Turkey is led by the President of Turkey.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "President_of_Turkey",
"property": "leaderTitle",
"subject": "Turkey"
},
{
"object": "Pietro_Canonica",
"property": "designer",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "\"1932-07-27\"",
"property": "inaugurationDate",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "President_of_Turkey",
"property": "leaderTitle",
"subject": "Turkey"
},
{
"object": "Pietro_Canonica",
"property": "designer",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "\"1932-07-27\"^^xsd:date",
"property": "open",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id625"
}
},
{
"626": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia, is located within the United States,where English is one of the spoken languages.Native Americans are one of the ethnic groups in the United States of America.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia, is in the United States, where English is spoken, and, where Native Americans are one of the ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albany is part of the state of Georgia in the United States. The country uses the English language and includes the ethnic group of Native Americans.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Georgia_(U.S._state)",
"property": "isPartOf",
"subject": "Albany,_Georgia"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Georgia_(U.S._state)",
"property": "subdivisionName",
"subject": "Albany,_Georgia"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id626"
}
},
{
"627": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, in New Mexico, has a total are of 490.9 square kilometres and a land area of 486.2 square kilometres. It has a population density of 1142.3 people per square kilometers, and the area codes are 505 and 575.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico covers a total of 490.0 sq kms which includes a land area of 486.2 sq km. It has the area codes 505 and 575 and a population density of 1142.3 inhabitants per sq km.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "505, 575",
"property": "areaCode",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "486.2 (square kilometres)",
"property": "areaOfLand",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "490.9 (square kilometres)",
"property": "areaTotal",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "1142.3 (inhabitants per square kilometre)",
"property": "populationDensity",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "4.862e+08",
"property": "areaLand",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "\"1142.3\"^^xsd:double",
"property": "populationDensity",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "505575",
"property": "areaCode",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "\"490.9\"^^<http://dbpedia.org/datatype/squareKilometre>",
"property": "areaTotal",
"subject": "Albuquerque,_New_Mexico"
}
],
[
{
"object": "\"505, 575\"",
"property": "areaCode",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "4.862e+08",
"property": "areaLand",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "4.909e+08",
"property": "areaTotal",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "\"1142.3\"^^xsd:double",
"property": "populationDensity",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id627"
}
},
{
"628": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alpharetta, Fulton County, Georgia is located within the United States. Atlanta is the county seat of Fulton County.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alpharetta is part of Fulton County in Georgia (United States), where Atlanta is the county seat.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Georgia_(U.S._state)",
"property": "isPartOf",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Fulton_County,_Georgia",
"property": "isPartOf",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Atlanta",
"property": "countySeat",
"subject": "Fulton_County,_Georgia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Georgia_(U.S._state)",
"property": "subdivisionName",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Fulton_County,_Georgia",
"property": "isPartOf",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Atlanta",
"property": "countySeat",
"subject": "Fulton_County,_Georgia"
}
],
[
{
"object": "United_States",
"property": "country",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Georgia_(U.S._state)",
"property": "isPartOf",
"subject": "Alpharetta,_Georgia"
},
{
"object": "Atlanta",
"property": "countySeat",
"subject": "Fulton_County,_Georgia"
},
{
"object": "Fulton_County,_Georgia",
"property": "subdivisionName",
"subject": "Alpharetta,_Georgia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id628"
}
},
{
"629": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Washington DC is the capital of the United States, where African Americans are an ethnic group. The US is also where you will find Amarillo, which is part of Potter County in Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo is part of Potter County,Texas in the United States.African Americans are an ethnic group in the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo is part of Potter County in Texas in the U.S. Washington, D.C. is the U.S. capital and African Americans are an ethnic group in that country.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Potter_County,_Texas",
"property": "isPartOf",
"subject": "Amarillo,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Potter_County,_Texas"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Potter_County,_Texas",
"property": "isPartOf",
"subject": "Amarillo,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Potter_County,_Texas"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id629"
}
},
{
"630": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amarillo is a part of Potter County, in Texas, USA. Austin is the state capital of Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo is part of Potter County in Texas, United States. Austin is the capital of Texas.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Potter_County,_Texas",
"property": "isPartOf",
"subject": "Amarillo,_Texas"
},
{
"object": "Texas",
"property": "state",
"subject": "Potter_County,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Amarillo,_Texas"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Potter_County,_Texas",
"property": "isPartOf",
"subject": "Amarillo,_Texas"
},
{
"object": "Texas",
"property": "state",
"subject": "Potter_County,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Amarillo,_Texas"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id630"
}
},
{
"631": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anaheim is in California which is led by the California State Assembly found in the California State Capitol. Spanish is spoken in that state.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "California is led by the California State Assembly (location: state capital), is spanish speaking and location of Anaheim.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Part of California (where Spanish is spoken), Anaheim, is led by the California State Assembly, in the California State Capitol.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "California_State_Assembly",
"property": "leaderTitle",
"subject": "Anaheim,_California"
},
{
"object": "California",
"property": "isPartOf",
"subject": "Anaheim,_California"
},
{
"object": "California_State_Capitol",
"property": "location",
"subject": "California_State_Assembly"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "California"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "California_State_Assembly",
"property": "leaderTitle",
"subject": "Anaheim,_California"
},
{
"object": "California",
"property": "isPartOf",
"subject": "Anaheim,_California"
},
{
"object": "California_State_Capitol",
"property": "location",
"subject": "California_State_Assembly"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "California"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id631"
}
},
{
"632": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anderson, Indiana is part of Adams Fall Creek, Lafayette, Richland, Union, and is located at 268.0 above sea level. The population density of Anderson, Indiana, is 523.9 (inhabitants per square kilometre) and the area is 107.43 square kilometres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Anderson, Indiana is part of Adams Fall Creek, Lafayette, Richland, Union and elevated 268.0 meters above sea level.The town has an area of 107.43 square kilometres and its population density is 523.9 inhabitants/sq km.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Anderson, Indiana is part of Adams Fall Creek, Lafayette, Richland, Union. It has a population density of 523.9 (inhabitants per square kilometre), a total area of 107.43 sq. km. and located 268.0 above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "523.9 (inhabitants per square kilometre)",
"property": "populationDensity",
"subject": "Anderson,_Indiana"
},
{
"object": "107.43 (square kilometres)",
"property": "areaTotal",
"subject": "Anderson,_Indiana"
},
{
"object": "268.0",
"property": "elevationAboveTheSeaLevel",
"subject": "Anderson,_Indiana"
},
{
"object": "\"Adams, Fall Creek, Lafayette, Richland, Union\"",
"property": "isPartOf",
"subject": "Anderson,_Indiana"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"523.9\"^^<http://dbpedia.org/datatype/inhabitantsPerSquareKilometre>",
"property": "populationDensity",
"subject": "Anderson,_Indiana"
},
{
"object": "1.07433e+08",
"property": "areaTotal",
"subject": "Anderson,_Indiana"
},
{
"object": "\"267.919\"^^xsd:double",
"property": "elevation",
"subject": "Anderson,_Indiana"
},
{
"object": "\"Adams, Fall Creek, Lafayette, Richland, Union\"@en",
"property": "subdivisionName",
"subject": "Anderson,_Indiana"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id632"
}
},
{
"633": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola is part of Pleasant Township in Steuben County, Indiana, in the U.S. and is 16.55 square kilometres in size.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola is part of Pleasant Township in Steuben County and covers a total area of 16.55 square kilometres. The city is in Indiana, United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola is part of Pleasant township, Steuben County in the US. It is located in the state of Indiana and has a total area of 16.55 sq km.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "16.55 (square kilometres)",
"property": "areaTotal",
"subject": "Angola,_Indiana"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
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{
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"comment": "good",
"lang": "",
"lex": "The city of Albany is part of Benton County, Oregon, in the United States. The capital of the country is, Washington D.C., and African Americans, are one of the country's ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The city of Albany, Oregon, U.S is part of Benton County. One of the ethnic groups in the United States are the African Americans and the capital is Washington DC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Benton_County,_Oregon",
"property": "isPartOf",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Benton_County,_Oregon",
"property": "subdivisionName",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id645"
}
},
{
"646": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "New York City is the largest city in the United States although the capital is Washington DC. Atlantic City, part of New Jersey, is located within the country.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City, is a part of New Jersey, in the United States. The country's capital, is Washington D.C., and its largest city, is New York City.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City, New Jersey is located within the United States which the biggest city is New York City and the capital is Washington, D.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_York_City",
"property": "largestCity",
"subject": "United_States"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_York_City",
"property": "largestCity",
"subject": "United_States"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id646"
}
},
{
"647": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque is in New Mexico, which is located in the United States. The leader of the US is Barack Obama and he has the title of President of the United States. African Americans are an ethnic group in the country.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barack Obama is the president of the United States where one finds African Americans and the city of Albuquerque in New Mexico.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id647"
}
},
{
"648": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque,New Mexico is located in the United States and Asian Americans are an ethnic group there.John Sánchez, is one of the leaders, in the New Mexico Senate which is leading the state.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque is in New Mexico, in the United States, where one of the ethnic groups is Asian Americans. The New Mexico Senate leads Albuquerque and John Sánchez is one of the leaders.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is in the United States, where Asian Americans are an ethnic group. Albuquerque, New Mexico is led by the New Mexico Senate, where John Sánchez is one of the leaders.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "New_Mexico_Senate",
"property": "leaderTitle",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "John_Sánchez",
"property": "leader",
"subject": "New_Mexico_Senate"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Asian_Americans",
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},
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},
{
"object": "John_Sánchez",
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},
{
"object": "United_States",
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"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id648"
}
},
{
"649": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Asian Americans are an ethnic group in the United States, which has the capital city of Washington DC. It is also the location of Albany, part of Benton County in Oregon.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "In the U.S., where the capital is Washington D.C., you'll find Albany in Benton County, Oregon and Asian Americans.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The city of Albany is part of Benton County, Oregon, in the United States, where Washington D.C. is the capital, and where Asian Americans are one of the ethnic groups.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Benton_County,_Oregon",
"property": "isPartOf",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Benton_County,_Oregon",
"property": "subdivisionName",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id649"
}
},
{
"650": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barack Obama led the United States, while the mayor is the leader of Albuquerque, New Mexico, home to many Native Americans.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Mayor_of_Albuquerque",
"property": "leaderTitle",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Mayor_of_Albuquerque",
"property": "leaderTitle",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id650"
}
},
{
"651": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Native Americans are an ethnic group in the United States, where English is the language. Also in the United States, is Albany, in Georgia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "English is spoken in the United States, where Native Americans are an ethnic group. The US is home to Albany, which is located in Georgia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albany,Georgia is located in the United States where English is the official language.The Native Americans are an ethnic group within the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "isPartOf",
"subject": "Albany,_Georgia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "\"United States\"@en",
"property": "subdivisionName",
"subject": "Albany,_Georgia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id651"
}
},
{
"652": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola,Indiana (U.S.), is home to one of its ethnic groups, the Asian Americans. English is spoken in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola, Indiana is in the United States, where English is the language spoken and Asian Americans are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The city of Angola, Indiana is located in the United States. Some Asian Americans live there and the language spoken is English.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id652"
}
},
{
"653": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola, Indiana, which is part of Pleasant Township Steuben County, is in the U.S. where English is spoken and among its ethnic groups are Asian Americans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola is in Pleasant Township which is part of Steuben County, in Indiana, in the United States. The country where English is spoken, and where Asian Americans are one of the ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "English is the language of the United States, where there is an ethnic group called Asian Americans. Also part of the US is the town of Angola, which can be found in Pleasant Township, Steuben County, Indiana.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id653"
}
},
{
"654": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jens Härtel has been the manager of 1 FC Magdeburg, part of SV Babelsberg 03 club and 1 FC Union Berlin. He is also attched to FSV Zwickau.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jens_Härtel",
"property": "manager",
"subject": "1._FC_Magdeburg"
},
{
"object": "SV_Babelsberg_03",
"property": "club",
"subject": "Jens_Härtel"
},
{
"object": "1._FC_Union_Berlin",
"property": "club",
"subject": "Jens_Härtel"
},
{
"object": "FSV_Zwickau",
"property": "club",
"subject": "Jens_Härtel"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jens_Härtel",
"property": "manager",
"subject": "1._FC_Magdeburg"
},
{
"object": "SV_Babelsberg_03",
"property": "managerClub",
"subject": "Jens_Härtel"
},
{
"object": "1._FC_Union_Berlin",
"property": "team",
"subject": "Jens_Härtel"
},
{
"object": "FSV_Zwickau",
"property": "clubs",
"subject": "Jens_Härtel"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id654"
}
},
{
"655": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran has worked at FC Bari 1908. He managed Associazione Calcio ChievoVerona and is in the Carrarese Calcio club. He was born in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran was born in Italy and is in the Carrarese Calcio Club. He currently plays for F.C. Bari 1908 while managing A.C. Chievo Verona.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Rolando Maran, who was born in Italy and has previously worked for FC Bari 1908. He currently plays for Carrarese Calcio and manages Associazione Calcio ChievoVerona.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rolando_Maran",
"property": "manager",
"subject": "A.C._Chievo_Verona"
},
{
"object": "F.C._Bari_1908",
"property": "club",
"subject": "Rolando_Maran"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Rolando_Maran"
},
{
"object": "Carrarese_Calcio",
"property": "club",
"subject": "Rolando_Maran"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rolando_Maran",
"property": "manager",
"subject": "A.C._Chievo_Verona"
},
{
"object": "F.C._Bari_1908",
"property": "managerClub",
"subject": "Rolando_Maran"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Rolando_Maran"
},
{
"object": "Carrarese_Calcio",
"property": "team",
"subject": "Rolando_Maran"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id655"
}
},
{
"656": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Italian born, Michele Marcolini, plays for A.C. Chievo Verona, owns Torino F.C., and is manager of A.C. Lumezzane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italian born Michele Marcolini has been the manager of AC Lumezzane. He currently plays for Torino FC and AC Chievo Verona.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini was born in Italy and has been the manager of AC Lumezzane. He currently owns Torino F.C. and plays for A.C. Chievo Verona.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Michele_Marcolini",
"property": "manager",
"subject": "A.C._Lumezzane"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Michele_Marcolini"
},
{
"object": "Torino_F.C.",
"property": "club",
"subject": "Michele_Marcolini"
},
{
"object": "A.C._Chievo_Verona",
"property": "club",
"subject": "Michele_Marcolini"
}
],
"originaltriplesets": {
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[
{
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"property": "manager",
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},
{
"object": "A.C._Chievo_Verona",
"property": "team",
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},
{
"object": "Italy",
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},
{
"object": "Torino_F.C.",
"property": "clubs",
"subject": "Michele_Marcolini"
}
],
[
{
"object": "Michele_Marcolini",
"property": "manager",
"subject": "A.C._Lumezzane"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Michele_Marcolini"
},
{
"object": "Torino_F.C.",
"property": "team",
"subject": "Michele_Marcolini"
},
{
"object": "A.C._Chievo_Verona",
"property": "clubs",
"subject": "Michele_Marcolini"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id656"
}
},
{
"657": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antonis Milionis is chairman of A.E Dimitra Efxeinoupolis which has 1500 members. It is in the league EPSTH 2nd GROUP and its grounds are in the town of Efxeinoupoli.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis is in the league, EPSTH 2nd GROUP and have 1500 members. Their chairman is Antonis Milionis and their home ground is situated in Efxeinoupoli.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1500",
"property": "numberOfMembers",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Antonis_Milionis",
"property": "chairman",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Efxeinoupoli",
"property": "ground",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "A_EPSTH_2nd_GROUP",
"property": "league",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "1500",
"property": "capacity",
"subject": "A.E_Dimitra_Efxeinoupolis"
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{
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},
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},
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{
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{
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{
"object": "Alagoas",
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},
{
"670": {
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{
"comment": "good",
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"lex": "St Vincent-St Mary High School is the ground of Akron Summit Assault, Akron, Ohio, U.S. Akron, Ohio is part of Summit County, Ohio.",
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},
{
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"lex": "The ground of Akron Summit Assault is at St Vincent-St Mary High School in Akron, Ohio, Summit County, United States.",
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{
"comment": "good",
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"lex": "St Vincent-St Mary High School, Akron, Summit County, Ohio, United States is the ground of Akron Summit Assault.",
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}
],
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{
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{
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{
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},
{
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"comment": "good",
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},
{
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}
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{
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{
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{
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{
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},
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},
{
"672": {
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{
"comment": "good",
"lang": "",
"lex": "Peter Stöger is the manager at 1. FC Köln. He is a former player of SK Vorwärts Steyr, FC Admira Wacker Mödling and FK Austria Wien.",
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"comment": "good",
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{
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{
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{
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{
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{
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},
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}
},
{
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"comment": "good",
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},
{
"comment": "good",
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},
{
"comment": "good",
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{
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},
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},
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"comment": "good",
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}
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},
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},
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{
"comment": "good",
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{
"comment": "good",
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],
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{
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{
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},
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"comment": "good",
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"comment": "good",
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"comment": "good",
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],
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},
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"comment": "good",
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{
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],
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{
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"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id677"
}
},
{
"678": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad airbase is located at Al Anbar Province in Iraq and the runway name is 09R/27L. It is operated by the United States Air Force and has a runway length of 3990.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force operates the Al-Asad airbase; located in Al Anbar Province, Iraq. The runway named 09R/27L is 3990 in length.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "3990.0",
"property": "runwayLength",
"subject": "Al_Asad_Airbase"
},
{
"object": "\"Al Anbar Province, Iraq\"",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
"object": "\"09R/27L\"",
"property": "runwayName",
"subject": "Al_Asad_Airbase"
}
],
"originaltriplesets": {
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{
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"property": "operator",
"subject": "Al_Asad_Airbase"
},
{
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},
{
"object": "\"Al Anbar Province, Iraq\"@en",
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"subject": "Al_Asad_Airbase"
},
{
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]
]
},
"shape": "NA",
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"xml_id": "Id678"
}
},
{
"679": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "The attack aircarft, the Lockheed AC-130, and the fighter aircraft, McDonnell Douglas F-15 Eagle are used by the United States Air Force. It is the operating organisation for Al Asad airbase and one of the noted United States Air Force battles was the Invasion of Grenada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is operated by the United States Air Force which was involved in the Invasion of Grenada. The United States Air Force has The Lockheed AC-130, an attack aircraft and the McDonnell Douglas F-15 Eagle, an aircraft fighter.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force, which operates Al Asad airbase, was involved in battles at the Invasion of Grenada and deploys the Lockheed AC-130 on USAF aircraft carriers and flies the McDonnell Douglas F-15 Eagle aircraft fighter.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "Invasion_of_Grenada",
"property": "battles",
"subject": "United_States_Air_Force"
},
{
"object": "Lockheed_AC-130",
"property": "attackAircraft",
"subject": "United_States_Air_Force"
},
{
"object": "McDonnell_Douglas_F-15_Eagle",
"property": "aircraftFighter",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
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{
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},
{
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"subject": "United_States_Air_Force"
},
{
"object": "Lockheed_AC-130",
"property": "aircraftAttack",
"subject": "United_States_Air_Force"
},
{
"object": "McDonnell_Douglas_F-15_Eagle",
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"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
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"xml_id": "Id679"
}
},
{
"680": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The city of Lahore, Pakistan, is served by Allama Iqbal International airport. It is operated by the Pakistan Civil Aviation Authority. It has a runway length of 2900.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport in Pakistan serves the city of Lahore, has a runway length of 2900.0 and is operated by Pakistan Civil Aviation Authority.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport is located in Lahore Pakistan. It has a runway length of 2900 and is operated by The Pakistan Civil Aviation Authority.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "2900.0",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Pakistan_Civil_Aviation_Authority",
"property": "operatingOrganisation",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Lahore",
"property": "cityServed",
"subject": "Allama_Iqbal_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "\"2900.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Pakistan_Civil_Aviation_Authority",
"property": "operator",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Lahore",
"property": "city",
"subject": "Allama_Iqbal_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id680"
}
},
{
"681": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Serving the city of Amsterdam, Amsterdam Airport Schiphol is -3.3528 metres above sea level. It has a runway named 09/27 Buitenveldertbaan and a runway length of 2014.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam AIrport, Schiphol serves the city of Amsterdam and is -3.3528 from sea level. The runway name is 09/27 Buitenvelderbaan and is 2014.0 in length.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"09/27 'Buitenveldertbaan'\"",
"property": "runwayName",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "2014.0",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "city",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"09/27 'Buitenveldertbaan'\"",
"property": "runwayDesignation",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
],
[
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"09/27 'Buitenveldertbaan'\"",
"property": "runwayDesignation",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id681"
}
},
{
"682": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The city of Amsterdam is served by Amsterdam Schiphol Airport. This airport has an elevation of -3.3528 from sea level and the runway name 18L/36R 'Aalsmeerbaan. It also has the runway length of 2014.0 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol is -3.3528 above sea level, has a runway name 18L/36R'Aalsmeerbaan which is 2014.0 in length and serves the city of Amsterdam.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam is served by Amsterdam Airport Schiphol which is -3.3528 above sea level and has a runway length of 2014.0 with the name of 18L/36R.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"18L/36R 'Aalsmeerbaan'\"",
"property": "runwayName",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "2014.0",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"18L/36R 'Aalsmeerbaan'\"",
"property": "runwayDesignation",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
],
[
{
"object": "Amsterdam",
"property": "city",
"subject": "Amsterdam_Airport_Schiphol"
},
{
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"property": "runwayDesignation",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"2014.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id682"
}
},
{
"683": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport in Texas serves the city of Andrews. It is 973 metres above sea level and has a runway length of 929.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews county airport, serving the city of Andrews in Texas has a runway length of 929.0 and is 973 metres above sea level.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "929.0",
"property": "runwayLength",
"subject": "Andrews_County_Airport"
},
{
"object": "Andrews,_Texas",
"property": "cityServed",
"subject": "Andrews_County_Airport"
},
{
"object": "973.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "\"929.03\"^^xsd:double",
"property": "runwayLength",
"subject": "Andrews_County_Airport"
},
{
"object": "Andrews,_Texas",
"property": "city",
"subject": "Andrews_County_Airport"
},
{
"object": "\"967.435\"^^xsd:double",
"property": "elevation",
"subject": "Andrews_County_Airport"
}
],
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "\"929.03\"^^xsd:double",
"property": "runwayLength",
"subject": "Andrews_County_Airport"
},
{
"object": "Andrews,_Texas",
"property": "city",
"subject": "Andrews_County_Airport"
},
{
"object": "\"973.0\"^^xsd:double",
"property": "elevation",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id683"
}
},
{
"684": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is situated in Texas, United States. The state capital is Austin and the demonym for the local people is Tejano.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Tejano is the demonym for people from the state of Texas in the United States. The state capital is Austin and it is served by Andrews County Airport.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "Tejano",
"property": "demonym",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "Tejano",
"property": "demonym",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id684"
}
},
{
"685": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas, United States which has the capital city of Austin and uses the English language.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas, the capital of which is Austin. English is the language of Texas which is located in the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas in the United States. The capital of Texas is Austin and the language spoken is English.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "English_language",
"property": "language",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "English_language",
"property": "language",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id685"
}
},
{
"686": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located at Ícolo e Bengo in Luanda Province, Angola, and serves the city of Luanda.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located at Ícolo e Bengo, Luanda Province, Angola and serves the city of Luanda.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International airport is located in Icolo e Bengo in Luada Province, Angola and serves the city of Luanda.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
},
{
"object": "Luanda_Province",
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"subject": "Ícolo_e_Bengo"
}
],
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{
"object": "Ícolo_e_Bengo",
"property": "location",
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{
"object": "Angola",
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"subject": "Ícolo_e_Bengo"
},
{
"object": "Luanda",
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"subject": "Angola_International_Airport"
},
{
"object": "Luanda_Province",
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"subject": "Ícolo_e_Bengo"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id686"
}
},
{
"687": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport is operated by the Flemish government, owned by the flemish region, has an elevation of 12.o metres above sea level, and it's runway length is 600.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Flemish government operate Antwerp International Airport which is owned by the Flemish region. The airport has an elevation of 12.0 metres above sea level and a runway length of 600.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport is owned by the Flemish Region and operated by the Flemish Government. The airport has a runway length of 600.00 and is 12 metres above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Flemish_Government",
"property": "operatingOrganisation",
"subject": "Antwerp_International_Airport"
},
{
"object": "Flemish_Region",
"property": "owner",
"subject": "Antwerp_International_Airport"
},
{
"object": "12.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Antwerp_International_Airport"
},
{
"object": "600.0",
"property": "runwayLength",
"subject": "Antwerp_International_Airport"
}
],
"originaltriplesets": {
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[
{
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"property": "operator",
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},
{
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},
{
"object": "\"11.8872\"^^xsd:double",
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{
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],
[
{
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},
{
"object": "\"600.151\"^^xsd:double",
"property": "runwayLength",
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},
{
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{
"object": "\"11.8872\"^^xsd:double",
"property": "elevation",
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],
[
{
"object": "Flemish_Government",
"property": "operator",
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},
{
"object": "Flemish_Region",
"property": "owner",
"subject": "Antwerp_International_Airport"
},
{
"object": "\"12.0\"^^xsd:double",
"property": "elevation",
"subject": "Antwerp_International_Airport"
},
{
"object": "\"600.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Antwerp_International_Airport"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id687"
}
},
{
"688": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Turkmenistan Airlines have their HQ in Ashgabat, Turkmenistan and their hub airport at Turkmenbashi International airport. They are the operating organisation for Ashgabat International airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The operating organization for Ashgabat International Airport is called Turkmenistan Airlines who have headquarters in Ashgabat, Turkmenistan. Their hub airport is Turkmenbashi International Airport.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Turkmenistan_Airlines",
"property": "operatingOrganisation",
"subject": "Ashgabat_International_Airport"
},
{
"object": "Turkmenistan",
"property": "headquarter",
"subject": "Turkmenistan_Airlines"
},
{
"object": "Turkmenbashi_International_Airport",
"property": "hubAirport",
"subject": "Turkmenistan_Airlines"
},
{
"object": "Ashgabat",
"property": "headquarter",
"subject": "Turkmenistan_Airlines"
}
],
"originaltriplesets": {
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{
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"property": "operator",
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{
"object": "Turkmenistan",
"property": "headquarter",
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},
{
"object": "Turkmenbashi_International_Airport",
"property": "targetAirport",
"subject": "Turkmenistan_Airlines"
},
{
"object": "Ashgabat",
"property": "headquarters",
"subject": "Turkmenistan_Airlines"
}
],
[
{
"object": "Turkmenistan_Airlines",
"property": "operator",
"subject": "Ashgabat_International_Airport"
},
{
"object": "Turkmenbashi_International_Airport",
"property": "targetAirport",
"subject": "Turkmenistan_Airlines"
},
{
"object": "Turkmenistan",
"property": "headquarters",
"subject": "Turkmenistan_Airlines"
},
{
"object": "Ashgabat",
"property": "headquarter",
"subject": "Turkmenistan_Airlines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id688"
}
},
{
"689": {
"category": "Airport",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Athens is served by Athens International airport located in Spata. The airport is 94 metres above sea level and has a runway length of 3800.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport is located in Spata and serves the city of Athens. The airport is 94 metres above sea level and has a runway length of 3800.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Athens",
"property": "cityServed",
"subject": "Athens_International_Airport"
},
{
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"property": "location",
"subject": "Athens_International_Airport"
},
{
"object": "94",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Athens_International_Airport"
},
{
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"subject": "Athens_International_Airport"
}
],
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{
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{
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},
{
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},
{
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}
],
[
{
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},
{
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},
{
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"property": "elevation",
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},
{
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}
],
[
{
"object": "Athens",
"property": "cityServed",
"subject": "Athens_International_Airport"
},
{
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"property": "runwayLength",
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},
{
"object": "Spata",
"property": "location",
"subject": "Athens_International_Airport"
},
{
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"property": "elevation",
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}
],
[
{
"object": "Athens",
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},
{
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},
{
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},
{
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}
],
[
{
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},
{
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},
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},
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}
],
[
{
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},
{
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},
{
"object": "Spata",
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},
{
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}
],
[
{
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},
{
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},
{
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},
{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id689"
}
},
{
"690": {
"category": "Airport",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "Don Guardian leads New Jersey (United States) where the township of Egg Harbor is. This township is where the Atlantic City International Airport, which serves serves Atlantic City, N.J., is located.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Atlantic City International Airport is located in Egg Harbor Township in the United States and serves Atlantic city, NJ where Don Guardian is leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City in New Jersey is led by Don Guardian and served by Atlantic City International airport, Egg Harbor Township, New Jersey, United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Atlantic_City,_New_Jersey",
"property": "cityServed",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Don_Guardian",
"property": "leaderName",
"subject": "Atlantic_City,_New_Jersey"
}
],
"originaltriplesets": {
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{
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"property": "cityServed",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Egg_Harbor_Township,_New_Jersey",
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"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
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"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Don_Guardian",
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],
[
{
"object": "Atlantic_City,_New_Jersey",
"property": "city",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Don_Guardian",
"property": "leaderName",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id690"
}
},
{
"691": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is located at Egg Harbor Township, New Jersey in the United States. The airport's owner is South Jersey Transportation Authority headed by Stephen Dilts.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is located in Egg Harbor Township, New Jersey, U.S. The South Jersey Transportation Authority is the owner of the airport and it is run by Stephen Dilts.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is owned by the South Jersey Transportation Authority, the head of which is Stephen Dilts. The airport is located in Egg Township, New Jersey, United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "South_Jersey_Transportation_Authority",
"property": "owner",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "Stephen_Dilts",
"property": "leaderName",
"subject": "South_Jersey_Transportation_Authority"
},
{
"object": "Egg_Harbor_Township,_New_Jersey",
"property": "location",
"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
}
],
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{
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{
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{
"object": "United_States",
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}
]
]
},
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"xml_id": "Id691"
}
},
{
"692": {
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"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Charles Michel is the leader of Belgium where the German language is spoken. Antwerp is located in the country and served by Antwerp International airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International airport services (Charles Michel) led Antwerp, Belgium. German is spoken here..",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Charles_Michel",
"property": "leaderName",
"subject": "Belgium"
},
{
"object": "Antwerp",
"property": "cityServed",
"subject": "Antwerp_International_Airport"
},
{
"object": "German_language",
"property": "language",
"subject": "Belgium"
},
{
"object": "Belgium",
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"subject": "Antwerp"
}
],
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{
"object": "Charles_Michel",
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},
{
"object": "Antwerp",
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"subject": "Antwerp_International_Airport"
},
{
"object": "German_language",
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},
{
"object": "Belgium",
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"subject": "Antwerp"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id692"
}
},
{
"693": {
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{
"comment": "good",
"lang": "",
"lex": "Antwerp is served by Antwerp International Airport and is a popular tourism destination in Belgium where the leader is Philippe of Belgium and the French language is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves the city of Antwerp in Belgium.The country's leader is Philippe of Belgium and French is one of the official language there.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport services, French speaking, Antwerp, Belgium (led by Philippe of Belgium).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippe_of_Belgium",
"property": "leaderName",
"subject": "Belgium"
},
{
"object": "Antwerp",
"property": "cityServed",
"subject": "Antwerp_International_Airport"
},
{
"object": "French_language",
"property": "language",
"subject": "Belgium"
},
{
"object": "Belgium",
"property": "country",
"subject": "Antwerp"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippe_of_Belgium",
"property": "leaderName",
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},
{
"object": "Antwerp",
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"subject": "Antwerp_International_Airport"
},
{
"object": "French_language",
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},
{
"object": "Belgium",
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}
],
[
{
"object": "Philippe_of_Belgium",
"property": "leader",
"subject": "Belgium"
},
{
"object": "Antwerp",
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"subject": "Antwerp_International_Airport"
},
{
"object": "French_language",
"property": "language",
"subject": "Belgium"
},
{
"object": "Belgium",
"property": "country",
"subject": "Antwerp"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id693"
}
},
{
"694": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport serves the city of Saranac Lake, New York part of Harrietstown and Essex County, New York, U.S.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport serves the city of Saranac Lake which is part of Harrietstown and Essex County, New York.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport serves Harrietstown, Essex County and Saranac Lake, New York (United States).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Harrietstown,_New_York"
},
{
"object": "Harrietstown,_New_York",
"property": "isPartOf",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Essex_County,_New_York",
"property": "isPartOf",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Saranac_Lake,_New_York",
"property": "cityServed",
"subject": "Adirondack_Regional_Airport"
}
],
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[
{
"object": "United_States",
"property": "country",
"subject": "Harrietstown,_New_York"
},
{
"object": "Harrietstown,_New_York",
"property": "subdivisionName",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Essex_County,_New_York",
"property": "isPartOf",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Saranac_Lake,_New_York",
"property": "city",
"subject": "Adirondack_Regional_Airport"
}
],
[
{
"object": "United_States",
"property": "country",
"subject": "Harrietstown,_New_York"
},
{
"object": "Harrietstown,_New_York",
"property": "subdivisionName",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Essex_County,_New_York",
"property": "isPartOf",
"subject": "Saranac_Lake,_New_York"
},
{
"object": "Saranac_Lake,_New_York",
"property": "cityServed",
"subject": "Adirondack_Regional_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id694"
}
},
{
"695": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al-Taqaddum Air Base serves the city of Fallujah, in Iraq, where Kurdish languages are spoken and Fuad Masum is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al-Taqaddum Air Base serves the city of Fallujah, which is in Iraq, where Fuad Masum is the leader and the Kurdish languages are spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Kurdish languages are spoken in Iraq which is led by Fuad Masum. The city of Fallujah is in the country and served by the Al Taqaddum air base.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kurdish_languages",
"property": "language",
"subject": "Iraq"
},
{
"object": "Fuad_Masum",
"property": "leaderName",
"subject": "Iraq"
},
{
"object": "Fallujah",
"property": "cityServed",
"subject": "Al-Taqaddum_Air_Base"
},
{
"object": "Iraq",
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"subject": "Fallujah"
}
],
"originaltriplesets": {
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{
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{
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},
{
"object": "Fallujah",
"property": "cityServed",
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},
{
"object": "Iraq",
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}
],
[
{
"object": "Kurdish_languages",
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{
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},
{
"object": "Fallujah",
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},
{
"object": "Iraq",
"property": "country",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id695"
}
},
{
"696": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al-Taqaddum Air Base serves the city of Fallujah in Iraq. The country is led by Haider al-Abadi and uses the Kurdish languages.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al-Taqaddum air base serves the city of Fallujah in Iraq. The leader of the country is Haider al-Abadi and the Kurdish language is spoken.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Haider_al-Abadi",
"property": "leaderName",
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},
{
"object": "Kurdish_languages",
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"subject": "Iraq"
},
{
"object": "Fallujah",
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"subject": "Al-Taqaddum_Air_Base"
},
{
"object": "Iraq",
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}
],
"originaltriplesets": {
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{
"object": "Haider_al-Abadi",
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},
{
"object": "Iraq",
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"subject": "Fallujah"
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{
"object": "Fallujah",
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}
],
[
{
"object": "Haider_al-Abadi",
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{
"object": "Kurdish_languages",
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},
{
"object": "Fallujah",
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"subject": "Al-Taqaddum_Air_Base"
},
{
"object": "Iraq",
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"subject": "Fallujah"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id696"
}
},
{
"697": {
"category": "Airport",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport, which serves the city of Lahore, is located in Punjab, Pakistan. The leader of which is Malik Muhammad Rafique Rajwana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Located in Pakistan, the city of Lahore is served by Allama Iqbal International Airport which is Punjab Pakistan, where the leader is Malik Muhammad Rafique Rajwana.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Malik Muhammad Rafique Rajwana is the leader of Punjab in Pakistan. The region is the location of Allama Iqbal International airport which serves the city of Lahore.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pakistan",
"property": "country",
"subject": "Lahore"
},
{
"object": "Punjab,_Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Lahore",
"property": "cityServed",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Malik_Muhammad_Rafique_Rajwana",
"property": "leaderName",
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}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pakistan",
"property": "country",
"subject": "Lahore"
},
{
"object": "Punjab,_Pakistan",
"property": "location",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Lahore",
"property": "cityServed",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Malik_Muhammad_Rafique_Rajwana",
"property": "leaderName",
"subject": "Punjab,_Pakistan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id697"
}
},
{
"698": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Poaceae belongs to the division of flowering plants, the order of Commelinids and the class of Monocotyledon. It constitutes the surface of the 1st runway at Alderney airport.",
"xml_id": "Id1"
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]
]
},
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},
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},
{
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],
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"comment": "good",
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{
"comment": "good",
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},
{
"comment": "good",
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{
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},
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"comment": "good",
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},
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"comment": "good",
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{
"comment": "good",
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"lex": "Kochi is leader of Kerala state where the AWH Engineering College with an academic staff of 250 is located in the city of Kuttikkattoor.",
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},
{
"comment": "good",
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"lex": "The AWH Engineering College in Kuttikkattoor, Kerala has a staff of 250. The leader of Kerala is named Kochi.",
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],
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},
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"comment": "good",
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"lex": "1 Decembrie 1918 University is located in Romania, which has as its capital, Bucharest. The country's national anthem is Deșteaptă-te, române! and one of the ethnic groups is Germans of Romania.",
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],
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{
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},
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},
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"comment": "good",
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"lex": "The 1 Decembrie 1918 University is located in the Romanian city of Alba Iulia. The leader of Romania is Klaus Iohannis and the capital city is Bucharest.",
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],
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],
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},
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],
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{
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]
]
},
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},
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"comment": "good",
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},
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"comment": "good",
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"lex": "Arrabbiata sauce can be found in Italy, the capital of which is Rome. Pietro Grasso and Sergio Mattarella are leaders of the country.",
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},
{
"comment": "good",
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"lex": "Pietro Grasso and Sergio Mattarella are both leaders in Italy, the capital city of which is Rome. Arrabbiata sauce comes from Italy.",
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}
],
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{
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],
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],
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},
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},
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"745": {
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"comment": "good",
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"lex": "Arròs negre is from Catalonia Spain and its ingredients are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers.",
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{
"comment": "good",
"lang": "",
"lex": "Arròs negre is a traditional dish from Catalonia, Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is from the region of Catalonia in Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink, cubanelle peppers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"",
"property": "mainIngredients",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"@en",
"property": "mainIngredient",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id745"
}
},
{
"746": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A traditional dish from the Catalonia region in Spain is Arròs negre . The main ingredients in it are, white rice, cuttlefish or squid, cephalopod ink, cubanelle peppers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is a traditional dish from Catalonia, Spain. The main ingredients in arròs negre are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is from Catalonia, Spain. The main ingredients of Arròs negre are white rice, cuttlefish or squid, cephalopod ink, cubanelle peppers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"",
"property": "mainIngredients",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "White_rice",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"@en",
"property": "mainIngredient",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "White_rice",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id746"
}
},
{
"747": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Spaniards are the ethnic group in Spain, where a traditional dish is Arròs negre. It comes from the region of Catalonia, which is led by the Parliament of Catalonia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is a traditional dish from Spain, where the ethnic group is the Spaniards. Arros negre is from the region of Catalonia, whose leaders are the Parliament of Catalonia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is from the Catalonia region (led by Parliament) of Spain and enjoyed by Spaniards.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Parliament_of_Catalonia",
"property": "leaderName",
"subject": "Catalonia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Parliament_of_Catalonia",
"property": "leaderName",
"subject": "Catalonia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id747"
}
},
{
"748": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia, where the Malay live, but comes from Sumatra where the Batak live.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish from Sumatra and Malaysia. Malaysian Malay is an ethnic group in Malaysia and Batak of Sumatra.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia and Sumatra. Malay is an ethnic group in Malaysia and Batak the same on Sumatra.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Batak",
"property": "ethnicGroup",
"subject": "Sumatra"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
},
{
"object": "Batak",
"property": "ethnicGroup",
"subject": "Sumatra"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id748"
}
},
{
"749": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish from Sumatra, Malaysia where there are ethnic groups like the Malays and the Minangkabau people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia and Sumatra. Malays are the Minangkabau people are ethnic groups in Sumatra.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish found in Malaysia and Sumatra. The Malays and Minangkabau people are ethnic groups of Sumatra.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Minangkabau_people",
"property": "ethnicGroup",
"subject": "Sumatra"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "Malays_(ethnic_group)",
"property": "ethnicGroup",
"subject": "Sumatra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Minangkabau_people",
"property": "ethnicGroups",
"subject": "Sumatra"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "Malays_(ethnic_group)",
"property": "ethnicGroup",
"subject": "Sumatra"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id749"
}
},
{
"750": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion originated in the United States, where Barack Obama is leader, Washing DC is the capital and where Asian Americans are an ethnic group.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States, capital city Washington, D.C. , is led by Barack Obama. Asian Americans are one of the ethnic groups in the U.S. The country is also the origin of Bacon Explosion.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id750"
}
},
{
"751": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States is the country of the Bacon Explosion and Asian Americans are one of the ethnic groups found there. John Roberts is the leader and the capital is Washington, D.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States where John Roberts is a leader.The country's capital city is Washington D.C. and Asian Americans are one of the ethnic groups of the USA.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "John Roberts is a leader in the US where the Bacon Explosion comes from. The capital of the country is Washington DC and one of the ethnic groups are the Asian Americans.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leader",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id751"
}
},
{
"752": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwiches are popular in the UK. Ketchup can be added to them and a variation is the BLT which is similar to a club sandwich.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwich is a dish from the UK that ketchup is often added to. A variant of this sandwich is a BLT, which itself is a variation of the club sandwich.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The BLT is a variation of both the bacon sandwich and the club sandwich. Bacon sandwiches are popular in the UK and can have the added ingredient of ketchup.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ketchup",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "dishVariation",
"subject": "BLT"
},
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ketchup",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "variations",
"subject": "BLT"
},
{
"object": "BLT",
"property": "hasVariant",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
}
],
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "Ketchup",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "Club_sandwich",
"property": "hasVariant",
"subject": "BLT"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id752"
}
},
{
"753": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The country that bacon sandwich comes from is the United Kingdom. Its main ingredients are bread and bacon and a condiment, often ketchup or brown sauce. A bacon sandwich can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwich is a dish from the United Kingdom. It is also known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin. The main ingredients in a Bacon sandwich are bread and bacon and it can be served with ketchup or brown sauce as a condiment.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Bread and bacon, with a condiment, often ketchup or brown sauce\"",
"property": "mainIngredients",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"",
"property": "alternativeName",
"subject": "Bacon_sandwich"
},
{
"object": "Condiment",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Bread and bacon, with a condiment, often ketchup or brown sauce\"@en",
"property": "mainIngredient",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"@en",
"property": "alias",
"subject": "Bacon_sandwich"
},
{
"object": "Condiment",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id753"
}
},
{
"754": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska and cookies are desserts. Baked Alaska comes from the U.S. which has African Americans as an ethnic group.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska and cookie is a type of dessert and comes from the United States.African Americans are one of the country's ethnic group.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id754"
}
},
{
"755": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Meringue, ice cream, sponge cake or Christmas pudding are the main ingredients of Baked Alaska which is popular in Paris, New York and Hong Kong and originated in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from the US and is popular in Paris, New York and Hong Kong. The main ingredients are meringue, ice cream and sponge cake (or Christmas pudding).",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The dish of Baked Alaska is found in the US, Hong Kong, New York and Paris. The main ingredients are Meringue, ice cream, sponge cake or Christmas pudding.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Meringue, ice cream, sponge cake or Christmas pudding\"",
"property": "mainIngredients",
"subject": "Baked_Alaska"
},
{
"object": "\"Paris, New York or Hong Kong\"",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Meringue, ice cream,sponge cakeorChristmas pudding\"",
"property": "ingredientName",
"subject": "Baked_Alaska"
},
{
"object": "\"Paris, New York or Hong Kong\"@en",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id755"
}
},
{
"756": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from Hong Kong, France, the United States or China and contains Meringue, ice cream, sponge cake or Christmas pudding .",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska hails from Hong Kong but France, the US and China all claim to have invented the dish. The main ingredients are Meringue, ice cream, sponge cake or Christmas pudding.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "\"Meringue, ice cream, sponge cake or Christmas pudding\"",
"property": "mainIngredients",
"subject": "Baked_Alaska"
},
{
"object": "\"France, United States or China\"",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "\"Meringue, ice cream,sponge cakeorChristmas pudding\"",
"property": "ingredientName",
"subject": "Baked_Alaska"
},
{
"object": "\"France, United States or China\"@en",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id756"
}
},
{
"757": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is served in the region of Hong Kong and known to be from the USA. The main ingredients are meringue, ice cream and sponge cake (or Christmas pudding).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from both Hong Kong and the United States. The main ingredients are Meringue, ice cream, sponge cake or Christmas pudding.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Meringue, ice cream, sponge cake or Christmas pudding are main ingredients in baked Alaska which is served in the region of Hong Kong and comes from the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "\"Meringue, ice cream, sponge cake or Christmas pudding\"",
"property": "mainIngredients",
"subject": "Baked_Alaska"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Ice_cream",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "\"Meringue, ice cream,sponge cakeorChristmas pudding\"",
"property": "ingredientName",
"subject": "Baked_Alaska"
},
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Ice_cream",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id757"
}
},
{
"758": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding is from the Derbyshire Dales region in Derbyshire. It's a variant of a bakewell tart and fruit preserves are one of the ingredients used in it.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A variant of bakewell pudding is bakewell tart. Bakewell pudding is from the Derbyshire Dales region which is part of Derbyshire. Fruit preserves are found in the Bakewell tart.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Fruit_preserves",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Fruit_preserves",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id758"
}
},
{
"759": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso is a traditional dish throughout Indonesia and is popular in neighbouring Southeast Asian countries too. Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, and fried shallots are main ingredients in Bakso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is an Indonesian dish which is also popular in neighbouring Southeast Asian countries. The main ingredients of the dish are ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables and fried shallots.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is an Indonesian dish also popular in neighbouring Southeast Asian countries and includes the ingredients ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables and fried shallots.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "\"Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, fried shallots\"",
"property": "mainIngredients",
"subject": "Bakso"
},
{
"object": "\"Nationwide in Indonesia, also popular in neighboring Southeast Asian countries\"",
"property": "region",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
},
{
"object": "\"Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, fried shallots\"@en",
"property": "mainIngredient",
"subject": "Bakso"
},
{
"object": "\"Nationwide in Indonesia, also popular in neighboring Southeast Asian countries\"@en",
"property": "region",
"subject": "Bakso"
}
],
[
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Ground beef,tapioca,noodle, ricevermicelli, beefbroth, kailan,celery, salted vegetables, fried shallots\"",
"property": "ingredientName",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
},
{
"object": "\"Nationwide in Indonesia, also popular in neighboring Southeast Asian countries\"@en",
"property": "region",
"subject": "Bakso"
}
],
[
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Ground beef,tapioca,noodle, ricevermicelli, beefbroth, kailan,celery, salted vegetables, fried shallots\"",
"property": "ingredientName",
"subject": "Bakso"
},
{
"object": "\"Nationwide in Indonesia, also popular in neighboring Southeast Asian countries\"@en",
"property": "region",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id759"
}
},
{
"760": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso is a food from the Country of Indonesia. Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, and fried shallots are main ingredients in Bakso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a food found in Indonesia. The main ingredients of the dish are ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables and fried shallots.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a food found in Indonesia it contains ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, and fried shallots.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Noodle",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "\"Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, fried shallots\"",
"property": "mainIngredients",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Noodle",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
},
{
"object": "\"Ground beef, tapioca, noodle, rice vermicelli, beef broth, kailan, celery, salted vegetables, fried shallots\"@en",
"property": "mainIngredient",
"subject": "Bakso"
}
],
[
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Noodle",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Ground beef,tapioca,noodle, ricevermicelli, beefbroth, kailan,celery, salted vegetables, fried shallots\"",
"property": "ingredientName",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id760"
}
},
{
"761": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is part of Colombian cuisine that originates from the Paisa region. Main ingredients are kidney beans, red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a traditional dish from the Paisa and Colombian regions. The main ingredients in bandeja paisa are red kidney beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa originates from the Paisa region and is part of Colombian cuisine. It includes red and kidney beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kidney_bean",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "\"red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon\"",
"property": "mainIngredients",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Kidney_bean",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "\"red beans,pork belly, whiterice,ground meat,chicharon,fried egg,plantain(patacones),chorizo, arepa, hogao sauce,black pudding(morcilla),avocadoandlemon\"",
"property": "ingredientName",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id761"
}
},
{
"762": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a Colombian cuisine from the Paisa region. The main ingredients of Bandeja paisa are: red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bandeja paisa are red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon. The dish is from Colombian cuisine and originates in the Paisa region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a colombian cuisine from the Paisa region. The main ingredients are red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"red beans, pork belly, white rice, ground meat, chicharon, fried egg, plantain (patacones), chorizo, arepa, hogao sauce, black pudding (morcilla), avocado and lemon\"",
"property": "mainIngredients",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Hogao",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"red beans,pork belly, whiterice,ground meat,chicharon,fried egg,plantain(patacones),chorizo, arepa, hogao sauce,black pudding(morcilla),avocadoandlemon\"",
"property": "ingredientName",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Hogao",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id762"
}
},
{
"763": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes comes from France where French is spoken and leaders include Gerard Larcher and Francois Hollande.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes come from is France where they speak French. The leader is either François Hollande and/or Gerard Larcher.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "France, where French is spoken and which has the leaders Francois Hollande and Gérard Larcher is also home to the Barny cake.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leaderName",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leader",
"subject": "France"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leader",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leaderName",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id763"
}
},
{
"764": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor, which is found in Indonesia and Siomay are both variations of the same dish. Shumai is a variation of the dish Siomay while Shiitake is an ingredient of Shumai.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor originates from the country of Indonesia, Shumai is a variation of the dish Siomay and contains Shiitake.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, found in Indonesia, is a variation of the dish Siomay. Another variation of that dish is Shumai, which is made using shiitake mushrooms.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Siomay"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Siomay",
"property": "hasVariant",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "variations",
"subject": "Siomay"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "\"Indonesia\"@en",
"property": "country",
"subject": "Batagor"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "hasVariant",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "variations",
"subject": "Siomay"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id764"
}
},
{
"765": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "La Paz, Iloilo City, Philippines is the home of Batchoy. The main ingredients of batchoy are noodles, pork organs, vegetables, chicken, shrimp, and beef.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a food found in La Paz, Iloilo City, Philippines. The main ingredients of Batchoy are noodles, pork organs, vegetables, chicken, shrimp, beef.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"",
"property": "mainIngredients",
"subject": "Batchoy"
},
{
"object": "Pork",
"property": "ingredient",
"subject": "Batchoy"
},
{
"object": "La_Paz,_Iloilo_City",
"property": "region",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"@en",
"property": "mainIngredient",
"subject": "Batchoy"
},
{
"object": "Pork",
"property": "ingredient",
"subject": "Batchoy"
},
{
"object": "La_Paz,_Iloilo_City",
"property": "region",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id765"
}
},
{
"766": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Philippines is the home of the soup dish Batchoy. The main ingredients are noodles, pork organs, vegetables, chicken, shrimp and beef.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a soup from the Philippines. The main ingredients of batchoy are noodles, pork organs, vegetables, chicken, shrimp, and beef.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a soup dish from the Philippines and is made with noodles, pork organs, vegetables, chicken, shrimp and beef.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chicken",
"property": "ingredient",
"subject": "Batchoy"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Soup",
"property": "course",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"",
"property": "mainIngredients",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chicken",
"property": "ingredient",
"subject": "Batchoy"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Soup",
"property": "course",
"subject": "Batchoy"
},
{
"object": "\"noodles, pork organs, vegetables, chicken, shrimp, beef\"@en",
"property": "mainIngredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id766"
}
},
{
"767": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is available nationwide in Singapore (where its from) and Indonesia. The main ingredients in it are; Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore and Indonesia. The main ingredients for Beef Kway Teow are: Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish popular in Singapore and Indonesia. Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil are the main ingredients.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "\"Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil\"",
"property": "mainIngredients",
"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "\"Kway teow , beef tenderloin, gula Melaka ,sliced, dried black beans, garlic, dark soy sauce, lengkuas , oyster sauce, soya sauce, chilli and sesame oil\"@en",
"property": "mainIngredient",
"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"@en",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id767"
}
},
{
"768": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish commonly found in Indonesia. The same dish is found in Singapore, where Halimah Yacob is a leader and Standard Chinese is the language spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is found in Indonesia and Singapore. Halimah Yacob is a leader of Standard Chinese speaking Singapore.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id768"
}
},
{
"769": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular food of Indonesia that comes from the Singapore region. Its ingredients include kway teow, beef tender loin, gula Melaka, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, chilli and sesame oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow originates from Singapore and Indonesia. Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil are the main ingredients of the dish.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow originates from Singapore and Indonesia.The main ingredients of the dish are: Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Kway teow, beef tender loin, gula Melaka, sliced, dried black beans, garlic, dark soy sauce, lengkuas, oyster sauce, soya sauce, chilli and sesame oil\"",
"property": "mainIngredients",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Kway teow , beef tenderloin, gula Melaka ,sliced, dried black beans, garlic, dark soy sauce, lengkuas , oyster sauce, soya sauce, chilli and sesame oil\"@en",
"property": "mainIngredient",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Sesame_oil",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id769"
}
},
{
"770": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Originating from the Karnataka region in India, Bhajji (also known as Bhali or bajji), has vegetables as a main ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the Karnataka region of India.The dish contains vegetables and also known as Bhaji or bajji.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"",
"property": "alternativeName",
"subject": "Bhajji"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"@en",
"property": "alias",
"subject": "Bhajji"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id770"
}
},
{
"771": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gram flour and vegetables are main ingredients in bhajji (also known as Bhaji and bajji) which comes from the Karnataka region.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji, also known as Bhaji or bajji, are found in the region of Karnataka and its ingredients are gram flour and vegetables.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji (aka Bhaji or bajji) is from the Karnataka region. It includes gram flour and vegetables.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"",
"property": "mainIngredients",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"",
"property": "alternativeName",
"subject": "Bhajji"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"@en",
"property": "mainIngredient",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"@en",
"property": "alias",
"subject": "Bhajji"
},
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id771"
}
},
{
"772": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The binignit recipe calls for sweet potatoes and sago. The sweet potato belongs to the flowering plant and falls under the order of Solanales.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sago and Sweet potato are ingredients in Binignit. Sweet potato belongs to the Solanales order of flowering plants.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Sweet_potato"
},
{
"object": "Sago",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Sweet_potato"
},
{
"object": "Sago",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id772"
}
},
{
"773": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Binignit is a dish from the region of Visayas in the Philippines. One of the main ingredients of binignit is coconut milk and another ingredient is sago.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredient of binignit is coconut milk and it also contains sago. The dish comes from the Visayas region of the Philippines.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Visayas",
"property": "region",
"subject": "Binignit"
},
{
"object": "Coconut_milk",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
},
{
"object": "Sago",
"property": "ingredient",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Visayas",
"property": "region",
"subject": "Binignit"
},
{
"object": "Coconut_milk",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
},
{
"object": "Sago",
"property": "ingredient",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id773"
}
},
{
"774": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Using one of the required ingredients, granola, Bionico is a dish served for dessert and is found in the region of Jalisco, in Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico, a dessert found in the Jalisco region of Mexico, has granola in it.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The granola-based dessert Bionico is a food found in Jalisco, Mexico.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id774"
}
},
{
"775": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a food found in Guadalajara, Mexico. Sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut are the main ingredients in Bionico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a food originates from the Guadalajara region of Mexico.The main ingredients of the dish are chopped fruits,sour cream,condensed milk,granola,shredded coconut and raisins.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bionico are chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins. It is a Mexican food from the Guadalajara region.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\"",
"property": "mainIngredients",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\"@en",
"property": "mainIngredient",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id775"
}
},
{
"776": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico and cookies are desserts. Bionico originates from Mexico, where the leader is Silvano Aureoles Conejo.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert found in Mexico, the leader is Silvano Aureoles Conejo and cookies are also a dessert.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Cookies and bionico are both types of dessert. The latter dish is found in Mexico where Silvano Aureoles Conejo is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leader",
"subject": "Mexico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id776"
}
},
{
"777": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso is a dish from the country of Indonesia (where the leader is Jusuf Kalla), it includes Tapioca.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a food originates from Indonesia and contains tapioca.The country's leader is Jusuf Kalla.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso has tapioca as an ingredient and is a food found in Indonesia where it originated. The leader of the country is Jusuf Kalla.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Tapioca",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id777"
}
},
{
"778": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is originates from Singapore and Tony Tan is the country's leader.It can also be found in Java where Baduy people are one of the ethnic groups.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baduy is an ethnic group of Java, where the dish ayam penyet can be found. This dish is from the Singapore region, where Tony Tan is a leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from Java, where the Baduy are an ethnic group. The dish comes from Singapore where Tony Tan is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id778"
}
},
{
"779": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a dish eaten in the Philippines. The language used there is Philippines Spanish and two of its ethnic groups are Ilocano people and Zamboangans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is eaten in the Philippines where the spoken language is Philippine Spanish. The Zamboangans and Ilocano people are both ethnic groups in the country.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ilocano_people",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Zamboangans",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Philippine_Spanish",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ilocano_people",
"property": "ethnicGroups",
"subject": "Philippines"
},
{
"object": "Zamboangans",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Philippine_Spanish",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id779"
}
},
{
"780": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batchoy comes from the Philippines where one of the languages is Arabic. It is also where there are several ethnic groups, two of which are Zamboangans and Igorot people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a dish that is found in the Philippines, where the Igorot people and the Zamboangans are ethnic groups and where Arabic is one of the languages spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Zamboangans and Igorot people are ethnic groups in the Philippines where the Arabic language is spoken and batchoy is eaten.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Zamboangans",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Zamboangans",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
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},
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"lang": "",
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],
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},
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},
{
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},
{
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"lang": "",
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],
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},
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},
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},
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},
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{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Fort_Worth,_Texas",
"property": "largestCity",
"subject": "Tarrant_County,_Texas"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "Tarrant_County,_Texas",
"property": "subdivisionName",
"subject": "Arlington,_Texas"
},
{
"object": "Texas",
"property": "isPartOf",
"subject": "Arlington,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id811"
}
},
{
"812": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "New York City (NYC) is the largest U.S. city. Atlantic City, New Jersey are also part of the United States with its capital as Washington, DC and home to White Americans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City is part of New Jersey,U.S. and white Americans are an ethnic group. The capital of the United States is Washington DC, but the largest city is New York.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City, is a part of New Jersey, United States, where the largest city is New York City. Washington, D.C. is the capital of the U.S, where White Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_York_City",
"property": "largestCity",
"subject": "United_States"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_York_City",
"property": "largestCity",
"subject": "United_States"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id812"
}
},
{
"813": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "People who live in the US are referred to as Americans. The United States, where Washington DC is the capital, is home to an ethnic group called Native Americans and is where Albany, Linn County, Oregon is located.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, is part of Linn County, Oregon, in the U.S., where the capital is Washington D.C. The country's inhabitants are known as Americans, and one of the ethnic groups is Native Americans.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Native Americans are one of the ethnic groups in the United States of America but the inhabitants are called Americans. The capital is Washington DC and Albany, part of Linn County, is in the state of Oregon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Linn_County,_Oregon",
"property": "isPartOf",
"subject": "Albany,_Oregon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Linn_County,_Oregon",
"property": "subdivisionName",
"subject": "Albany,_Oregon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id813"
}
},
{
"814": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "One of the ethinic groups of the United states where Washington D.C. is capital is Asian Americans. Albany is a city in Oregon which is also located in the US.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, Oregon is in the United States, whose capital is Washington DC and whose inhabitants are Americans. Asian Americans are an ethnic group there.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "In the U.S. the capital is Washington D.C., the citizens are called Americans and Asian Americans are an ethnic group. Albany is part of Oregon in the U.S.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Oregon",
"property": "isPartOf",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Oregon",
"property": "subdivisionName",
"subject": "Albany,_Oregon"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Oregon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id814"
}
},
{
"815": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Americans are the people occupying the US and one of the ethnic groups are the African Americans. English is the language spoken in the US. Angola is in Steuben County, Indiana, which is part of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola, is in Steuben County, in the United States Where English is spoken, the inhabitants are Americans, and one of the ethnic groups, is African Americans.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Steuben_County,_Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id815"
}
},
{
"816": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Auburn is part of Lee County in Alabama in the U.S. The capital of the U.S. is Washington, D.C. and Native Americans are one ethnic group in that country.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Auburn is part of Lee County, in Alabama, in the United States. This country, has Washington D.C. as the capital, and Native Americans as one of the ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Auburn is part of Lee County, Alabama, United States. The capital of the United States is Washington DC. and Native Americans live there.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Lee_County,_Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
},
{
"object": "United_States",
"property": "country",
"subject": "Lee_County,_Alabama"
},
{
"object": "Alabama",
"property": "state",
"subject": "Lee_County,_Alabama"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Lee_County,_Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
},
{
"object": "United_States",
"property": "country",
"subject": "Lee_County,_Alabama"
},
{
"object": "Alabama",
"property": "state",
"subject": "Lee_County,_Alabama"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id816"
}
},
{
"817": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Auburn is part of Lee County, Alabama in the U.S. Opelika is the county seat in Lee County. Native Americans are one of the ethnic groups in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Native Americans are an ethnic group within the United States which is where Auburn, Alabama is found. It is part of Lee County which has the county seat off Opelika.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Lee County, is in Alabama, in the United States, where one of the ethnic groups is Native Americans. Auburn is part of Lee County, where the county seat is Opelika.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Lee_County,_Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
},
{
"object": "Opelika,_Alabama",
"property": "countySeat",
"subject": "Lee_County,_Alabama"
},
{
"object": "United_States",
"property": "country",
"subject": "Lee_County,_Alabama"
},
{
"object": "Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Lee_County,_Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
},
{
"object": "Opelika,_Alabama",
"property": "countySeat",
"subject": "Lee_County,_Alabama"
},
{
"object": "United_States",
"property": "country",
"subject": "Lee_County,_Alabama"
},
{
"object": "Alabama",
"property": "subdivisionName",
"subject": "Auburn,_Alabama"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id817"
}
},
{
"818": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Newark, Trenton and Atlantic City are all part of New Jersey, within the United States (home of The Native Americans).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "New Jersey is in the United States, where the Native Americans are an ethnic group. Atlantic City, is a part of New Jersey, whose largest city is Newark and capital is Trenton.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City is in New Jersey whose largest city is Newark and whose capital is Trenton. New Jersey is in the US where Native Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Newark,_New_Jersey",
"property": "largestCity",
"subject": "New_Jersey"
},
{
"object": "Trenton,_New_Jersey",
"property": "capital",
"subject": "New_Jersey"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Newark,_New_Jersey",
"property": "largestCity",
"subject": "New_Jersey"
},
{
"object": "Trenton,_New_Jersey",
"property": "capital",
"subject": "New_Jersey"
},
{
"object": "United_States",
"property": "country",
"subject": "New_Jersey"
},
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Atlantic_City,_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id818"
}
},
{
"819": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albuquerque,New Mexico is located in the United States and English is the spoken language there.The leader of the United States is called the President whom was Barack Obama recently.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of the United States, which uses the English language, is President Barack Obama . Albuquerque in New Mexico is located within the country .",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "New_Mexico",
"property": "isPartOf",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "New_Mexico",
"property": "isPartOf",
"subject": "Albuquerque,_New_Mexico"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Barack_Obama",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albuquerque,_New_Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id819"
}
},
{
"820": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Americans live in the United States, where English is spoken and where Asian Americans are an ethnic group. The US is the location of Angola, which is in Steuben County, Indiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola, Indiana, is part of Steuben County, United States where Americans live. The Asian Americans are an ethnic group of the United States and English is the language spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola, Indiana, (part of Steuben County), is in the United States. the country where English is spoken, and where one of the ethnic groups, is Asian Americans.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "United_States"
},
{
"object": "Steuben_County,_Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Angola,_Indiana"
},
{
"object": "Asian_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id820"
}
},
{
"821": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barack Obama is leader of the United States, where the capital is Washington DC and where Native Americans are an ethnic group. The US is home to Akron, which is part of Summit County, Ohio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron is in Summit County, Ohio, in the United States where the capital is Washington D.C. The country's leader is Barack Obama, and Native Americans are one of the ethnic groups.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
},
{
"object": "Summit_County,_Ohio",
"property": "isPartOf",
"subject": "Akron,_Ohio"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
},
{
"object": "Summit_County,_Ohio",
"property": "subdivisionName",
"subject": "Akron,_Ohio"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id821"
}
},
{
"822": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia is in the United States, a country led by the President of the United States and where the inhabitants are called Americans. One of the ethnic groups in the country are the African Americans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of the United States, which includes the ethnic group of African Americans among its population of Americans, is the President. The country is the location of Albany in Georgia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Americans live in the United States where the leader if known as the President. The country includes the ethnic group of African Americans and is the location of Albany, Georgia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "United_States",
"property": "isPartOf",
"subject": "Albany,_Georgia"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "country",
"subject": "Albany,_Georgia"
},
{
"object": "\"United States\"@en",
"property": "subdivisionName",
"subject": "Albany,_Georgia"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id822"
}
},
{
"823": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
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{
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},
{
"object": "2001",
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},
{
"object": "\"250\"^^xsd:nonNegativeInteger",
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{
"object": "\"Kuttikkattoor\"@en",
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]
]
},
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},
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}
],
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{
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{
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{
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{
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{
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{
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]
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},
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},
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{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Lars Lokke Rasmussen is the leader of Denmark where the School of Business and Social Sciences at the Aarhus University in the city of Aarhus is located. The School was established in 1928 and has 737 academic staff members.",
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}
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{
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{
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{
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{
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{
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{
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}
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{
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"subject": "Aarhus"
},
{
"object": "European_University_Association",
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{
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{
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{
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"comment": "good",
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{
"comment": "good",
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],
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{
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{
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{
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},
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{
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{
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{
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"838": {
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{
"object": "Stockport_County_F.C.",
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{
"object": "Blackpool",
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{
"object": "Labour_Party_(UK)",
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],
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{
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{
"object": "Stockport_County_F.C.",
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{
"object": "Blackpool",
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{
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]
},
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},
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"comment": "good",
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},
{
"comment": "good",
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{
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{
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{
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{
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{
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]
]
},
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},
{
"840": {
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{
"comment": "good",
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},
{
"comment": "good",
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"lex": "Gus Poyet is the manager of AEK Athens who play at their home ground of the Olympic Stadium (Athens) at Marousi. He previously played for Chelsea FC and is associated with Real Zaragoza FC.",
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}
],
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{
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{
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{
"object": "Chelsea_F.C.",
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],
[
{
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{
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{
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{
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{
"object": "Chelsea_F.C.",
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},
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},
{
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
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},
{
"comment": "good",
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}
],
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{
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{
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{
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{
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{
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{
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],
[
{
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{
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{
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{
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{
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]
},
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},
{
"842": {
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{
"comment": "good",
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},
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"comment": "good",
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"lex": "The ground of A.S. Gubbio 1910 is located in Italy, where the leader is Sergio Mattarella and Italian is the official language. Italians are the people who live in Italy, where the capital city is Rome.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Italians inhabit Italy, where the official language is Italian. Rome is the capital and one of the leaders is Sergio Mattarella. Also in Italy, is the ground of A.S. Gubbio 1910.",
"xml_id": "Id3"
}
],
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{
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{
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{
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{
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{
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{
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{
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},
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},
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"comment": "good",
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"comment": "good",
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{
"comment": "good",
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"lex": "Arem-arem is nationwide dish in Indonesia where Indonesian is the spoken language, but originates from the Island of Java.The country's vice-president is Jusuf Kalla and the local currency is Indonesian rupiah.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Indonesia",
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{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"",
"property": "region",
"subject": "Arem-arem"
},
{
"object": "Indonesian_rupiah",
"property": "currency",
"subject": "Indonesia"
},
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"@en",
"property": "region",
"subject": "Arem-arem"
},
{
"object": "Indonesian_rupiah",
"property": "currency",
"subject": "Indonesia"
},
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id843"
}
},
{
"844": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Malaysian Chinese and Malay are ethnic groups in Malaysia where the capital is Kuala Lumpur. It is also where the food dish Asam pedas, which come from the region of Sumatra and the Malay Peninsula, is found.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "From the Sumatra and Malay Peninsula regions, Asam pedas, is a food found in Malaysia. The country where the capital is Kuala Lumpur and where the Malay and Malaysian Chinese are two of the ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is from the Sumatra and Malay Peninsula regions of Malaysia. Malaysia, with its capital city of Kuala Lumpur, is the home to both Malay and Malaysian Chinese people.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Chinese",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "\"Sumatra and Malay Peninsula\"",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Chinese",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "\"Sumatra and Malay Peninsula\"@en",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id844"
}
},
{
"845": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Malays and Malaysian Indians are both ethnic groups in Malaysia, the capital city of which is Kuala Lumpur. Asam pedas is a food found in Malaysia. It comes from the Sumatra and Malay Peninsula regions.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia, where the capital is Kuala Lumpur and ethnic groups include Malay and Malaysian Indian. The dish is also found in the regions of Sumatra and the Malay Peninsula.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The dish Asam pedas comes from the region of Sumatra and the Malay Peninsula, Malaysia (capital Kuala Lumpur). Ethnic groups of the region include Malaysian Indian and Malaysian Malay.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "\"Sumatra and Malay Peninsula\"",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "\"Sumatra and Malay Peninsula\"@en",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id845"
}
},
{
"846": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish from Sumatra in Malaysia, The capital of Malaysia is Kuala Lumpur and two of the ethnic group found here are the Malay and Malaysian Indian.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam Pedas, a dish from Sumatra, is found in Malaysia, where the capital if Kuala Lumpur and the Malaysian Malay is an ethnic group along with the Malaysian Indian.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a dish found in Sumatra and Malaysia. Malaysian Indians and Malay are ethnic groups of Malaysia, where the capital is Kuala Lumpur.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
},
{
"object": "Kuala_Lumpur",
"property": "capital",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id846"
}
},
{
"847": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food from the Malay Peninsula region of Malaysia. Abu Zahar is a leader in Malaysia, where there are ethnic groups called the Malays and the Malaysian Indians.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is from the Malay Peninsula region of Malaysia, the country where Abu Zahar Ujang is leader and where Malaysian Malay and Malaysian Indian are ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food from Malaysia and can be found throughout the Malaysian peninsula region.The country's leader is Abu Zahar Ujang and among its ethnic groups are the Malay and Malaysian Indians.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Abu_Zahar_Ujang",
"property": "leaderName",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Malay_Peninsula",
"property": "region",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroups",
"subject": "Malaysia"
},
{
"object": "Abu_Zahar_Ujang",
"property": "leader",
"subject": "Malaysia"
},
{
"object": "Malaysian_Indian",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Malay_Peninsula",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id847"
}
},
{
"848": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the Kansas City metropolitan area of the United States. Its main ingredients are bacon and sausage.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion comes from Kansas City in the United States. It includes bacon obviously, but also sausage.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion originates from the Kansas City metropolitan area of the United States. Its main ingredients are bacon and sausage.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "\"Bacon,sausage\"",
"property": "mainIngredients",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Kansas_City_metropolitan_area",
"property": "region",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "mainIngredients",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "\"Bacon,sausage\"",
"property": "ingredientName",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Kansas_City_metropolitan_area",
"property": "region",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "mainIngredient",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id848"
}
},
{
"849": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States where one of the ethnic groups are White American and Paul Ryan is a leader. English is spoken there and the capital is Washington, D.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion comes from the United States where English is spoken and the capital is Washington D.C. A political leader there is Paul Ryan and White Americans are one ethnic group of the country.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Paul_Ryan",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "White_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Paul_Ryan",
"property": "leader",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "English_language",
"property": "language",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id849"
}
},
{
"850": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich uses bread and bacon with ketchup or brown sauce as a condiment, is also known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin. A variation is the BLT, and the country of origin is the United Kingdom.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich, also known as: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece n' bacon, bacon cob, bacon barm, BLT and bacon muffin, is from the UNited Kingdom. It includes bread, bacon and ketchup or brown sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The variant Blt bacon sandwich includes: bread, bacon, and ketchup or brown sauce. It originates from the United Kingdom and goes by the various names: Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece n' bacon, bacon barm, bacon cob and bacon muffin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bread and bacon, with a condiment, often ketchup or brown sauce\"",
"property": "mainIngredients",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "Ketchup",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"",
"property": "alternativeName",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bread and bacon, with a condiment, often ketchup or brown sauce\"@en",
"property": "mainIngredient",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "Ketchup",
"property": "ingredient",
"subject": "Bacon_sandwich"
},
{
"object": "\"Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, bacon muffin\"@en",
"property": "alias",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id850"
}
},
{
"851": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The French language is spoken in France where the leaders are Manuel Valls and Gerard Larcher. Baked Alaska is from France but it's also served in Hong Kong.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "France, led by Gérard Larcher and Manuel Valls, is the home of Baked Alaska. Outside of this French-speaking nation, Baked Alaska is also popular in Hong Kong.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from France where the leaders include Gérard Larcher and Manuel Valls and they speak French. It is also served in Hong Kong.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leaderName",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Manuel_Valls",
"property": "leader",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Hong_Kong",
"property": "region",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id851"
}
},
{
"852": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient of bandeja paisa is lemon which belongs to the rutaceae family and falls under the order of rosids. Bandeja paisa is a dish from the Antioquia Department region and the department is located in Colombia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lemon is a member of the family Rutaceae and is of the order of Rosids. Lemon is an ingredient in the dish Bandeja Paisa, which hails from the Antioquia Department region of Colombia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombia",
"property": "country",
"subject": "Antioquia_Department"
},
{
"object": "Rosids",
"property": "order",
"subject": "Lemon"
},
{
"object": "Rutaceae",
"property": "family",
"subject": "Lemon"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Colombia",
"property": "country",
"subject": "Antioquia_Department"
},
{
"object": "Rosids",
"property": "order",
"subject": "Lemon"
},
{
"object": "Rutaceae",
"property": "family",
"subject": "Lemon"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id852"
}
},
{
"853": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Francois Hollande and Gerard Larcher are leaders in France, where the language is French. It is also where Barny cakes, made with sponge cake, are from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes, made from sponge cake, come from France where French is the national language and François Hollande and Gérard Larcher are leaders.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "France is led by Francois Hollande and Gerard Larcher. The national language is French. A national dish is Barny cakes, which is a type of sponge cake.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leaderName",
"subject": "France"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leader",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leaderName",
"subject": "France"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Barny_Cakes"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "François_Hollande",
"property": "leader",
"subject": "France"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id853"
}
},
{
"854": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Joko Widodo is the leader of Indonesia, where the dish batagor can be found. Batagor, Siomay (which contains peanut sauce) and Shumai are all variations of the same dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor is a variation of Siomay(which contains peanut sauce) and Shumai.It originates from Indonesia where the leader is president Joko Widodo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, a variation of Shumai and Siomay (includes peanut sauce), is found in Joko Widodo led Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Joko_Widodo",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Joko_Widodo",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Siomay",
"property": "hasVariant",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
},
{
"object": "Shumai",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id854"
}
},
{
"855": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor is found in Indonesia, where the leader is Joko Widodo. The dish is a variation of siomay and shumai (which contains shiitake).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, Shumai and Siomay are variations of the same dish. Batagor is found in the country of Indonesia, which leader is Joko Widodo.Shiitake is an ingredient of Shumai.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Joko_Widodo",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Siomay"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Joko_Widodo",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "hasVariant",
"subject": "Siomay"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Joko_Widodo",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Siomay",
"property": "hasVariant",
"subject": "Batagor"
},
{
"object": "Shumai",
"property": "variations",
"subject": "Siomay"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id855"
}
},
{
"856": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor is found in the country of Indonesia which is led by Jusuf Kalla. Shiitake is an ingredient of Shumai which is a variation of Bataho and Siomay.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor comes from Indonesia where Jusuf Kalla is the vice-president. Siomay and shumai are variations of the dish and all of them contain shiitake.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Jusuf_Kalla",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Shumai",
"property": "hasVariant",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id856"
}
},
{
"857": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji, alternative names: bhaji and bajji, originate from the Karnataka region of India and contain the main ingredients of gram flour and vegetables.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji (also called bahjo or bajji) comes from the Karnataka region of India. It contains gram flour and vegetables.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Bhajji originates from the Karnataka region of India. The main ingredients are gram flour and vegetables however its also known as a Bhaji or Bajji.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"",
"property": "mainIngredients",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"",
"property": "alternativeName",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"@en",
"property": "alias",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
},
{
"object": "\"Gram flour,vegetables\"",
"property": "ingredientName",
"subject": "Bhajji"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"@en",
"property": "mainIngredient",
"subject": "Bhajji"
},
{
"object": "\"Bhaji, bajji\"@en",
"property": "alias",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id857"
}
},
{
"858": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Bhajji originates from the Karnataka region of India. The country's leader is T. S. Thakur and Vajubhai Vala is the leader of Karnataka.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji, from the region of Karnataka, originates from india where people are referred to as indians, the leaders are T.S. Thakur and Vajubhai Vala.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "Vajubhai_Vala",
"property": "leaderName",
"subject": "Karnataka"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "Vajubhai_Vala",
"property": "leaderName",
"subject": "Karnataka"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id858"
}
},
{
"859": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Indian people enjoy the dish bhajji, which originates from the Karnataka region of India. This nation is led by T.S. Thakur and Sumitra Mahajan.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leader",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id859"
}
},
{
"860": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert dish from the Guadalajara region of Mexico and it includes the ingredients chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert dish from the Guadalajara region of Mexico and it contains the main ingredients sour cream, chopped fruits, condensed milk, granola, raisins and shredded coconut.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\"",
"property": "mainIngredients",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "\"Chopped Fruits, Sour Cream, Condensed Milk, Granola, Shredded Coconut, Raisins\"@en",
"property": "mainIngredient",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id860"
}
},
{
"861": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Enrique Pena Nieto is the leader of Mexico which is where they speak Spanish and eat bionico, flavoured with honey, for dessert.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Mexico"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Honey",
"property": "dishVariation",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Mexico"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leader",
"subject": "Mexico"
},
{
"object": "Honey",
"property": "hasVariant",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id861"
}
},
{
"862": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Silvano Aureoles Conejo is the leader of Mexico, where the dish Bionico can be found in the Jalisco region. Bionico is a dessert dish and honey is used in one of its variants.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert often including honey found in the Jalisco region of Mexico. The name of the leader in Mexico is Silvano Aureoles Conejo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The dessert Bionico, containg honey, is from the Jalisco region of Mexico (led by Silvano Aureoles Conejo).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Honey",
"property": "dishVariation",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Silvano_Aureoles_Conejo",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Honey",
"property": "hasVariant",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id862"
}
},
{
"863": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Felipe VI of Spain is the leader of Spain, whose main ethnic group is Spaniards. Arròs negre is a traditional dish from the Catalonia region of Spain, whose leader is Carles Puigdemont.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is a Spanish dish from the region of Catalonia where Carles Puigdemont is a leader. Spaniards are an ethnic group in Spain and the leader is Felipe VI.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Spaniards enjoy the dish arros negre, which is from Catalonia . Led by Carles Puigdemont, Catalonia is a region of Spain (led by Felipe VI).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Carles_Puigdemont",
"property": "leaderName",
"subject": "Catalonia"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leaderName",
"subject": "Spain"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Carles_Puigdemont",
"property": "leaderName",
"subject": "Catalonia"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leader",
"subject": "Spain"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id863"
}
},
{
"864": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Italy is the country Amatriciana sauce comes from. The capital of the country is Rome, the language spoken is Italian and the leaders include Matteo Renzi and Laura Boldrini.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce can be found in italy, where the speak italian, the capital is Rome and their leaders are Matteo Renzi and Laura Boldrini.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Laura_Boldrini",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leader",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Laura_Boldrini",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
}
],
[
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Laura_Boldrini",
"property": "leader",
"subject": "Italy"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id864"
}
},
{
"865": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce comes from Italy, where Italians come from and Rome is the capital city. Sergio Mattarella and Laura Boldrini are leaders in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana is a popular sauce from Italy where the capital is Rome and the population are known as Italians. Leaders of the country include Sergio Mattarella and Laura Boldrini.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Laura_Boldrini",
"property": "leaderName",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
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},
{
"object": "Laura_Boldrini",
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]
},
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}
},
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},
{
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"lang": "",
"lex": "Italy is led by Pietro Grasso and the language spoken in the country is Italian. Matteo Renzi is a leader from Italy which has Rome as its capital and where you can find amatriciana sauce.",
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},
{
"comment": "good",
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}
],
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{
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{
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{
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{
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{
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},
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"comment": "good",
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}
],
"links": [],
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{
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{
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},
{
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},
{
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],
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{
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{
"object": "\"Nationwide, also can be found in Malaysia and Singapore\"@en",
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},
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},
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},
{
"comment": "good",
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"lex": "In Singapore, one of the spoken languages is English, a leader is Tony Tan and ayam penyet is a dish from here. This dish is also found in Java where an ethnic group is the Javanese people.",
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}
],
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{
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},
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}
},
{
"869": {
"category": "Food",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Among the languages spoken in the Philippines are Arabic and Philippine Spanish. The Zamboangans are a group in the Philippines and another group is the Chinese Filipino. Batchoy comes from the Philippines.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy comes from the Philippines, whose ethnic groups include the Zamboangans and the Chinese Filipino, and where Arabic and Philippine Spanish are among the languages spoken.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
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{
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{
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},
{
"object": "Philippines",
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{
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],
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{
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{
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{
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],
[
{
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{
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},
{
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{
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]
]
},
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}
},
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The dish ajoblanco originates from Spain and more specifically Andalusia where Susana Diaz is the leader. In Spain, the language spoken is Spanish and an ethnic group are the Spaniards.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
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"xml_id": "Id3"
}
],
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{
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{
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},
{
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}
],
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]
},
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}
},
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},
{
"comment": "good",
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"lex": "Some famous Spaniards from Spain include Felipe VI (leader of Spain) and Susana Diaz who is the leader of Andalusia where ajoblanco comes from.",
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}
],
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{
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{
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]
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},
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}
},
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},
{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Bean is a US national born in Wheeler, Texas. He is a retired test pilot who joined NASA in 1963.",
"xml_id": "Id3"
}
],
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},
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},
{
"comment": "good",
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}
],
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},
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},
{
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},
{
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}
],
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},
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{
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},
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},
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{
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},
{
"comment": "good",
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}
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},
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}
],
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{
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},
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"object": "Operation_Enduring_Freedom",
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},
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}
},
{
"890": {
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"comment": "good",
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"lex": "Alderney Airport is 88 metres above sea level and serves the city of Alderney. It has a 1st runway made of poaceae which is named 08/26 and has a length of 733.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alderney Airport serves Alderney and its runway name is 08/26. The 1st runway was made of Poaceae and it is 733 m long. The airport is 88 meters above sea level.",
"xml_id": "Id2"
}
],
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"subject": "Alderney_Airport"
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},
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport in Pakistan is governed by the Pakistan Civil Aviation Authority and serves the city of Lahore. The airport runway is named 18L/36R and has a length of 3310.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International airport is located in Pakistan as it serves the city of Lahore and is operated by Pakistan Civil Aviation Authority. the airport runway which named \"18L/36R\" has a total length of 3310.0.",
"xml_id": "Id3"
}
],
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{
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},
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},
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},
{
"comment": "good",
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"lex": "Allama Iqbal International Airport is located in Punjab, Pakistan and governed by The Pakistan Civil Aviation Authority & the name of its runway is \"18R/36L\" and its lentgh is 2900. the airport serves the whole city of Lahore.",
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},
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"comment": "good",
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"lex": "Allama Iqbal International Airport, found in Punjab, Pakistan, serves the city of Lahore and is operated by the Pakistan Civil Aviation Authority. The runaway name is 18R/36L and it has a length of 2900.0.",
"xml_id": "Id3"
}
],
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{
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},
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}
},
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"comment": "good",
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},
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"comment": "good",
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"lex": "Alpena County Regional Airport, which serves Alpena, is found in Maple Ridge Township, Alpena County, Michigan in the U.S.A. The airport is 210 metres above sea level and a runway that is 1,533 in length.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport, which serves the city of Alpena, is found in Maple Ridge Township, Alpena County, Michigan, in the U.S.A. The airport is 210 metres above sea level and has a runway length of 1,533.",
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}
],
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{
"object": "United_States",
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},
{
"comment": "good",
"lang": "",
"lex": "The city of Alpena in Michigan is served by Alpena County Regional Airport which is located 210 metres above sea level in Maple Ridge Township, Alpena County, Michigan. The runway name is 7/25 and it has a length of 1533.0.",
"xml_id": "Id2"
}
],
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"object": "Alpena,_Michigan",
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},
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}
},
{
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The location of Alpena County Regional Airport is Wilson Township, Alpena County, Michigan and it serves Alpena, Michigan. The airport, which lies 210 metres above sea level, has a runway named 1/19/ which is 2744 metres long.",
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}
],
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{
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},
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},
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},
{
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},
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},
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},
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"comment": "good",
"lang": "",
"lex": "The Athens International Airport is located in Athens, Greece which has both Alexis Tsipras and Prokopis Pavlopoulos as leaders. The language spoken here is Greek.",
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],
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},
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},
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},
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],
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},
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"lang": "",
"lex": "AC Hotel Bella Sky Copenhagen is in Denmark where the leader is Lars Løkke Rasmussen and Greenlandic is one of the languages spoken there. The tenant of the AC Hotel Bella Sky Copenhagen is the Marriott International Hotel which was founded in Washington, D.C.",
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},
{
"comment": "good",
"lang": "",
"lex": "Marriott International, founded in Washington DC, is a tenant of AC Hotel Bella Sky located in Copenhagen, Denmark (which is led by Lars Lokke Rasmussen and has Greenlandic as a spoken language).",
"xml_id": "Id2"
}
],
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"comment": "good",
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"comment": "good",
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"comment": "good",
"lang": "",
"lex": "Addis Ababa City Hall's was started in 1961 and completed in 1964. It is 42 metres high and has 140000 square metres of floor area. The \" Government of Addis Ababa\" are the current tenants of that hall.",
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{
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},
{
"comment": "good",
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"xml_id": "Id3"
}
],
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},
{
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{
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},
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],
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},
{
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],
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{
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"property": "locationCountry",
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},
{
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],
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{
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{
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{
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]
},
"shape": "NA",
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"xml_id": "Id916"
}
},
{
"917": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Chinese are one of the ethnic groups in Japan which is led by Taro Aso. The country is the location of the Akita Museum of Art found in Akita, Akita Prefecture.",
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},
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"comment": "good",
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"lex": "Tarō Asō is one of the leaders in Japan, A country where there is an ethnic group of Chinese people. Also where the Akita Museum of Art is located, in Akita which is part of Akita Prefecture.",
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},
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"lang": "",
"lex": "The Chinese people are an ethnic group of Japan which is led by Taro Aso. The country is the location of the Akita Museum of Art in Akita, Akita Prefecture.",
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}
],
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},
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},
{
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"property": "isPartOf",
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},
{
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},
{
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],
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},
{
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},
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},
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id917"
}
},
{
"918": {
"category": "Building",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alan B. Miller Hall was designed by Robert A.M. Stern; it is located in Virginia and it is owned by the College of William and Mary. Its current tenants are the Mason School of Business in the US.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The College of William and Mary is the owner of the Alan B. Miller Hall. which was designed by Robert A.M. Stern. Situated in Virginia, it has as its tenants United States based Mason School of Business.",
"xml_id": "Id2"
}
],
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"object": "Virginia",
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{
"object": "College_of_William_&_Mary",
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"object": "United_States",
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},
"shape": "NA",
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},
{
"919": {
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"comment": "good",
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},
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"comment": "good",
"lang": "",
"lex": "Amdavad ni Gufa is located in Ahmedabad, Gujarat, India. Sumitra Mahajan is an Indian leader and Gujarat is led by the Legislative Assembly.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The location of Amdavad ni Gufa is Ahmedabad, Gujarat, India. Gujarat's leader is known as the Gujarat Legislative Assembly, and the name of the leader in India is Sumitra Mahajan.",
"xml_id": "Id3"
}
],
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"object": "Ahmedabad",
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},
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"920": {
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"comment": "good",
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},
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},
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},
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"comment": "good",
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}
],
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{
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},
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"lex": "Abner W. Sibal ended his military career January 3, 1965.",
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}
],
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},
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},
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"lex": "Abner W. Sibal died in Alexandria, Virginia.",
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}
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"lex": "Adam Holloway was born in Kent.",
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"lex": "Adam Holloway resided in Gravesend.",
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"lex": "Adam Holloway lives in Gravesend.",
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"lex": "The alma mater of Adenan Satem is the University of Adelaide.",
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},
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{
"comment": "good",
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"lex": "Adolf Schärf's place of birth was Mikulov.",
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"lex": "Adolf Schärf was born in Mikulov.",
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"comment": "good",
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"lex": "Adolf Scharf was born in Mikulov.",
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"comment": "good",
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"lex": "Adonis Georgiadis was in office while Antonis Samaras was Prime Minister.",
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"lex": "Adonis Georgiadis was in office under Antonis Samaras, Prime Minister.",
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"lex": "Adonis Georgiadis served in office while Antonis Samaras was Prime Minister.",
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"lex": "Adonis Georgiadis was the Deputy Minister for Development, Competitiveness and Shipping.",
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"lex": "Adonis Georgiadis worked as the Deputy Minister for Development, Competitiveness and Shipping.",
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"lex": "Agnes Kant was born in Hessisch Oldendorf.",
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"lex": "Agnes Ward White was born in Marietta, Ohio.",
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"lex": "The birthplace of Agnes Ward White is Marietta, Ohio.",
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"comment": "good",
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"lex": "Airey Neave was awarded the Military Cross.",
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},
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"lex": "Knightsbridge, London is the birthplace of Airey Neave.",
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"lex": "Airey Neave was born in Knightsbridge, London.",
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"comment": "good",
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"comment": "good",
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"lex": "Airey Neave started serving in military in 1935.",
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"lex": "Airey Neave began his service in 1935.",
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},
{
"996": {
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"comment": "good",
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"lex": "Albert B.White is a member of the Republican Party of the United States.",
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"comment": "good",
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"lex": "Albert B. White is a member of the Republican Party in the United States.",
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"comment": "good",
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"lex": "Albert B White is a member of the Republican Party in the United States.",
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],
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},
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"997": {
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"comment": "good",
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"lex": "The spouse of Albert B. White was Agnes Ward White.",
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"comment": "good",
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"lex": "Albert B. White is married to Agnes Ward White.",
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},
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"998": {
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"comment": "good",
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"lex": "Albert Jennings Fountain served in the Union Army.",
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"comment": "good",
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"lex": "Albert Jennings Fountain was a member of the Union Army.",
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],
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},
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"999": {
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{
"comment": "good",
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"lex": "Alberto Teisaire's nationality is Argentinian.",
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"comment": "good",
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"lex": "Alberto Teisaire is Argentinian.",
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{
"comment": "good",
"lang": "",
"lex": "Alberto Teisaire was Argentinian.",
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},
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"1000": {
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"comment": "good",
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"comment": "good",
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"lex": "Alberto Teisaire was Rear Admiral in the Argentine Navy.",
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},
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"comment": "good",
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"lex": "Alfons Gorbach's place of death was in Austria.",
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"comment": "good",
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"lex": "Alfons Gorbach died in Austria.",
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{
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},
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"comment": "good",
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"lex": "Alfred Moore Scales started his career on 1875-03-04.",
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},
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"1003": {
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"comment": "good",
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"lex": "Alfred Moore Scales took part in the Battle of Chancellorsville.",
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{
"comment": "good",
"lang": "",
"lex": "Alfred Moore Scales fought in the Battle of Chancellorsville.",
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{
"comment": "good",
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"lex": "Alfred Moore Scales was involved in the Battle of Chancellorsville.",
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},
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},
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"1004": {
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"comment": "good",
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"lex": "Darien, Connecticut is the birthplace of Alfred N. Phillips.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alfred N. Phillips place of birth was Darien Connecticut.",
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"comment": "good",
"lang": "",
"lex": "Alfred N. Phillips was born in Darien, Connecticut.",
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},
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},
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},
{
"comment": "good",
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"lex": "Alfred N. Phillips was Mayor of Stamford, Connecticut.",
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],
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},
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"comment": "good",
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"lex": "Allan Shivers started his career from January 21, 1947.",
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}
],
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},
{
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"comment": "good",
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"lex": "Allan Shivers served in the United States Army.",
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],
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{
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{
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[
{
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},
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},
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"1008": {
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"comment": "good",
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"lex": "Allan Shivers had the successor Price Daniel.",
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{
"comment": "good",
"lang": "",
"lex": "Allan Shivers was succeeded by Price Daniel.",
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],
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{
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},
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"comment": "good",
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"lex": "Alvah Sabin was an active politician beginning March 4, 1853.",
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{
"comment": "good",
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"lex": "Alvah Sabin started active service on 1853-03-04.",
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"object": "1853-03-04",
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"subject": "Alvah_Sabin"
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},
{
"1010": {
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"comment": "good",
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{
"comment": "good",
"lang": "",
"lex": "Alvah Sabin belongs to the Whig Party (United States).",
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{
"comment": "good",
"lang": "",
"lex": "Alvah Sabin was a member of the Whig Party of the United States.",
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],
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{
"object": "Whig_Party_(United_States)",
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"subject": "Alvah_Sabin"
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{
"object": "Whig_Party_(United_States)",
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},
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"shape_type": "NA",
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"xml_id": "Id1010"
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},
{
"1011": {
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"comment": "good",
"lang": "",
"lex": "Robert E Lee was a commander in the American Civil War.",
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},
{
"comment": "good",
"lang": "",
"lex": "Robert E. Lee was a commander in the American Civil War.",
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},
{
"comment": "good",
"lang": "",
"lex": "A commander in the American Civil War was Robert E. Lee.",
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}
],
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{
"object": "Robert_E._Lee",
"property": "commander",
"subject": "American_Civil_War"
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"object": "Robert_E._Lee",
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"shape": "NA",
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"xml_id": "Id1011"
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},
{
"1012": {
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{
"comment": "good",
"lang": "",
"lex": "In Austria, the language is Austrian German.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The language in Austria is Austrian German.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The spoken language of Austria is Austrian German.",
"xml_id": "Id3"
}
],
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{
"object": "Austrian_German",
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"subject": "Austria"
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"object": "Austrian_German",
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"subject": "Austria"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1012"
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},
{
"1013": {
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"comment": "good",
"lang": "",
"lex": "Robert E Lee was commander in the Battle of Antietam.",
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},
{
"comment": "good",
"lang": "",
"lex": "Robert E. Lee was a commander in the Battle of Antietam.",
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{
"comment": "good",
"lang": "",
"lex": "A commander at the Battle of Antietam was Robert. E. Lee.",
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}
],
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"object": "Robert_E._Lee",
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"subject": "Battle_of_Antietam"
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"object": "Robert_E._Lee",
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"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1013"
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"1014": {
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"comment": "good",
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"lex": "The Battle of Cold Harbor took place during the American Civil War.",
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}
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"object": "American_Civil_War",
"property": "isPartOfMilitaryConflict",
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"object": "American_Civil_War",
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"xml_id": "Id1014"
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},
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"1015": {
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"comment": "good",
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"lex": "Gabriela Michetti is a leader in Buenos Aires.",
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{
"comment": "good",
"lang": "",
"lex": "The leader of Buenos Aires is called Gabriela Michetti.",
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{
"comment": "good",
"lang": "",
"lex": "The leader of Buenos Aires is Gabriela Michetti.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gabriela_Michetti",
"property": "leaderName",
"subject": "Buenos_Aires"
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"object": "Gabriela_Michetti",
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"shape": "NA",
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},
{
"1016": {
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"comment": "good",
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"lex": "Colin Powell was a commander in the Gulf War.",
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{
"comment": "good",
"lang": "",
"lex": "Colin Powell was the commander in the Gulf War.",
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"object": "Colin_Powell",
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"object": "Colin_Powell",
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},
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"1017": {
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"comment": "good",
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"lex": "Dwight D. Eisenhower was the predecessor to John F. Kennedy.",
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{
"comment": "good",
"lang": "",
"lex": "Dwight D. Eisenhower preceded John F. Kennedy as President.",
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{
"comment": "good",
"lang": "",
"lex": "John F. Kennedy's predecessor was Dwight D. Eisenhower.",
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"subject": "John_F._Kennedy"
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},
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"1018": {
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{
"comment": "good",
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"lex": "The University of Vienna is the Alma mater of Josef Klaus.",
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{
"comment": "good",
"lang": "",
"lex": "Josef Klaus alma mater is the University of Vienna.",
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{
"comment": "good",
"lang": "",
"lex": "The alma mater of Josef Klaus is the University of Vienna.",
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],
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"object": "University_of_Vienna",
"property": "almaMater",
"subject": "Josef_Klaus"
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"object": "University_of_Vienna",
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},
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"1019": {
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"comment": "good",
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"lex": "Francisco Franco was the predecessor of Juan Carlos I of Spain.",
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{
"comment": "good",
"lang": "",
"lex": "The predecessor of Juan Carlos I f Spain was Francisco Franco.",
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"object": "Francisco_Franco",
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"subject": "Juan_Carlos_I_of_Spain"
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"object": "Francisco_Franco",
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},
"shape": "NA",
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},
{
"1020": {
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"comment": "good",
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"lex": "Juan Perón was a member of the Labour Party in Argentina.",
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"comment": "good",
"lang": "",
"lex": "Juan Peron belongs to the Labour Party in Argentina.",
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{
"object": "Labour_Party_(Argentina)",
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"subject": "Juan_Perón"
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"subject": "Juan_Perón"
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},
"shape": "NA",
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"xml_id": "Id1020"
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},
{
"1021": {
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{
"comment": "good",
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"lex": "The leader of the Netherlands is Mark Rutte.",
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"object": "Mark_Rutte",
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"subject": "Netherlands"
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{
"object": "Mark_Rutte",
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{
"object": "Mark_Rutte",
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]
},
"shape": "NA",
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"xml_id": "Id1021"
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},
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"1022": {
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{
"comment": "good",
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"lex": "The United States Army was involved in battles in the Spanish-American War.",
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"object": "Spanish–American_War",
"property": "battles",
"subject": "United_States_Army"
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{
"object": "Spanish–American_War",
"property": "battles",
"subject": "United_States_Army"
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[
{
"object": "Spanish–American_War",
"property": "battle",
"subject": "United_States_Army"
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]
},
"shape": "NA",
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"xml_id": "Id1022"
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},
{
"1023": {
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{
"comment": "good",
"lang": "",
"lex": "The birthplace of William M. O. Dawson was Bloomington Maryland.",
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"comment": "good",
"lang": "",
"lex": "Bloomington, Maryland was the birthplace of William M O Dawson.",
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"comment": "good",
"lang": "",
"lex": "William M.O. Dawson was born in Bloomington, Maryland.",
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"modifiedtripleset": [
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"object": "Bloomington,_Maryland",
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"subject": "William_M._O._Dawson"
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{
"object": "Bloomington,_Maryland",
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"subject": "William_M._O._Dawson"
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},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1023"
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},
{
"1024": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Dodge Coronet is the alternative name for the 1955 Dodge.",
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{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge is also named the Dodge Coronet.",
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],
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"modifiedtripleset": [
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"object": "\"Dodge Coronet\"",
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],
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{
"object": "\"Dodge Coronet\"@en",
"property": "aka",
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]
},
"shape": "NA",
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"xml_id": "Id1024"
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},
{
"1025": {
"category": "MeanOfTransportation",
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{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge engine is 230 cubic inches.",
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{
"comment": "good",
"lang": "",
"lex": "The size of the engine in the 1955 Dodge is 230 cubic inches.",
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{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge has an engine size of 230 cubic inches.",
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"object": "230 (cubic inches)",
"property": "engine",
"subject": "1955_Dodge"
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{
"object": "230",
"property": "engine",
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},
"shape": "NA",
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"xml_id": "Id1025"
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},
{
"1026": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna was christened on the 7th of April 2005.",
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},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna was \"christened\" on the 7th April 2005.",
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{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna was christened on 7th April 2005.",
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],
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"modifiedtripleset": [
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"object": "2005-04-07",
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"subject": "A-Rosa_Luna"
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],
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{
"object": "\"2005-04-07\"^^xsd:date",
"property": "shipChristened",
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{
"object": "\"2005-04-07\"^^xsd:date",
"property": "christeningDate",
"subject": "A-Rosa_Luna"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1026"
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},
{
"1027": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The location of AIDA Cruises is Rostock.",
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{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises are located at Rostock.",
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{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises is based in Rostock.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rostock",
"property": "location",
"subject": "AIDA_Cruises"
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],
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{
"object": "Rostock",
"property": "location",
"subject": "AIDA_Cruises"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1027"
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},
{
"1028": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises is the operator of the AIDAluna.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna is operated by AIDA Cruises.",
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},
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises are the operator of the ship AIDAluna.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAluna"
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],
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"originaltripleset": [
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{
"object": "AIDA_Cruises",
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"subject": "AIDAluna"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1028"
}
},
{
"1029": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was christened on the 16th of March, 2013.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was christened in March 16 2013.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The christening date of AIDAstella is 2013-03-16.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2013-03-16",
"property": "christeningDate",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"--03-16\"^^xsd:gMonthDay",
"property": "shipChristened",
"subject": "AIDAstella"
}
],
[
{
"object": "\"2013-03-16\"^^xsd:date",
"property": "christeningDate",
"subject": "AIDAstella"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1029"
}
},
{
"1030": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was completed on March 11th 2013.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was completed on the 11th March 2013.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDAstella was completed on March 11th 2013.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2013-03-11",
"property": "completionDate",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2013-03-11\"^^xsd:date",
"property": "completionDate",
"subject": "AIDAstella"
}
],
[
{
"object": "\"2013-03-11\"^^xsd:date",
"property": "shipCompleted",
"subject": "AIDAstella"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1030"
}
},
{
"1031": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella had its maiden voyage on March 17, 2013.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ship AIDAstella had her maiden voyage on March 17th 2013.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella had its maiden voyage on the 17th of March 2013.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella ship's first journey took place on 17/03/2013.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "The ship AIDAstella had her maiden voyage on March 17th 2003.",
"xml_id": "Id5"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2013-03-17",
"property": "maidenVoyage",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2013-03-17\"^^xsd:date",
"property": "shipMaidenVoyage",
"subject": "AIDAstella"
}
],
[
{
"object": "\"2013-03-17\"^^xsd:date",
"property": "maidenVoyage",
"subject": "AIDAstella"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1031"
}
},
{
"1032": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ship AIDAstella is operated by AIDA Cruises.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The operator of AIDAstella is AIDA Cruises.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella is operated by AIDA Cruise Line.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
}
],
[
{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1032"
}
},
{
"1033": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The launch site of ALV X-1 was Mid Atlantic Regional Spaceport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The launch site of the ALV X-1 was the Mid-Atlantic Regional Spaceport.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mid-Atlantic_Regional_Spaceport",
"property": "launchSite",
"subject": "ALV_X-1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mid-Atlantic_Regional_Spaceport",
"property": "launchSite",
"subject": "ALV_X-1"
}
],
[
{
"object": "Mid-Atlantic_Regional_Spaceport",
"property": "sites",
"subject": "ALV_X-1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1033"
}
},
{
"1034": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is also known as American Motors Matador.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AMC Matador is also known as American Motors Matador.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is alternatively known as the American Motors Matador.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"American Motors Matador\"",
"property": "alternativeName",
"subject": "AMC_Matador"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"American Motors Matador\"@en",
"property": "aka",
"subject": "AMC_Matador"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1034"
}
},
{
"1035": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) was sold to the Netherlands on the 1st of April 1948.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) was sold to the Netherlands on 01/04/1948.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) was sold to the Netherlands 1 April 1948.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Sold to the Netherlands 1 April 1948\"",
"property": "status",
"subject": "ARA_Veinticinco_de_Mayo_(V-2)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Sold to the Netherlands 1 April 1948\"",
"property": "status",
"subject": "ARA_Veinticinco_de_Mayo_(V-2)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1035"
}
},
{
"1036": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé has a straight-four engine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupe has the straight four engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé has a straight-four engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Straight-four_engine",
"property": "engine",
"subject": "Abarth_1000_GT_Coupé"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Abarth_1000_GT_Coupé__1",
"property": "engine",
"subject": "Abarth_1000_GT_Coupé"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1036"
}
},
{
"1037": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX has a Honda K engine.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Honda_K_engine",
"property": "engine",
"subject": "Acura_TLX"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Honda_K_engine",
"property": "engine",
"subject": "Acura_TLX"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1037"
}
},
{
"1038": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX has an AWD vehicle layout.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "AWD_(vehicle)",
"property": "layout",
"subject": "Acura_TLX"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "AWD_(vehicle)",
"property": "layout",
"subject": "Acura_TLX"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1038"
}
},
{
"1039": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX is related to the Honda Accord.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Honda_Accord",
"property": "relatedMeanOfTransportation",
"subject": "Acura_TLX"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Honda_Accord",
"property": "relatedMeanOfTransportation",
"subject": "Acura_TLX"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1039"
}
},
{
"1040": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The transmission of the Acura TLX is a 9-speed ZF 9HP automatic (V6).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX has a 9-speed ZF 9HP automatic (V6) transmission.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"9-speed ZF 9HP automatic (V6)\"",
"property": "transmission",
"subject": "Acura_TLX"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"9-speedZF9HPautomatic(V6)\"",
"property": "transmission",
"subject": "Acura_TLX"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1040"
}
},
{
"1041": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 was assembled in Arese.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arese",
"property": "assembly",
"subject": "Alfa_Romeo_164"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Arese",
"property": "assembly",
"subject": "Alfa_Romeo_164"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1041"
}
},
{
"1042": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfa Romeo 164 and Lancia Thema are related types of transportation.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The related transport to the Alfa Romeo 164 is the Lancia Thema.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lancia_Thema",
"property": "relatedMeanOfTransportation",
"subject": "Alfa_Romeo_164"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lancia_Thema",
"property": "relatedMeanOfTransportation",
"subject": "Alfa_Romeo_164"
}
],
[
{
"object": "Lancia_Thema",
"property": "related",
"subject": "Alfa_Romeo_164"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1042"
}
},
{
"1043": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 has a 4-speed automatic (ZF 4HP18QE) transmission.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"4-speed automatic (ZF 4HP18QE)\"",
"property": "transmission",
"subject": "Alfa_Romeo_164"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"4-speedautomatic(ZF 4HP18QE)\"",
"property": "transmission",
"subject": "Alfa_Romeo_164"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1043"
}
},
{
"1044": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alhambra was built by the Samuda Brothers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Samuda Brothers built the Alhambra.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Samuda_Brothers",
"property": "builder",
"subject": "Alhambra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Samuda_Brothers",
"property": "builder",
"subject": "Alhambra_(1855)"
}
],
[
{
"object": "\"Samuda Brothers Cubitt Town, London\"@en",
"property": "shipBuilder",
"subject": "Alhambra_(1855)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1044"
}
},
{
"1045": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alhambra ship beam is 8.3m.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra has a ship beam of 8.3m.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra has an 8.3m ship beam.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "8.3 m",
"property": "shipBeam",
"subject": "Alhambra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"8.3 m\"@en",
"property": "shipBeam",
"subject": "Alhambra_(1855)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1045"
}
},
{
"1046": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alhambra had wrecked.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Wrecked\"",
"property": "status",
"subject": "Alhambra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Wrecked\"",
"property": "status",
"subject": "Alhambra_(1855)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1046"
}
},
{
"1047": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alhambra had a top speed of 18.52 km/h.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra has a top speed of 18.52.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The top speed of the Alhambra is 18.52.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "18.52",
"property": "topSpeed",
"subject": "Alhambra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"18.52\"^^xsd:double",
"property": "topSpeed",
"subject": "Alhambra_(1855)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1047"
}
},
{
"1048": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alvis Speed 25 has a straight-six engine.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Straight-six_engine",
"property": "engine",
"subject": "Alvis_Speed_25"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Alvis_Speed_25__6",
"property": "engine",
"subject": "Alvis_Speed_25"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1048"
}
},
{
"1049": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Production, of the Alvis Speed 25, began in 1937.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25 was first manufactured in 1937.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1937",
"property": "modelStartYear",
"subject": "Alvis_Speed_25"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1937\"^^xsd:gYear",
"property": "modelStartYear",
"subject": "Alvis_Speed_25"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1049"
}
},
{
"1050": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The American submarine NR-1 has a ship draft of 4.6m.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The American submarine NR-1 has a ship draft of 4.6 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The American sub NR-1 has a draft of 4.6 m.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "4.6 m",
"property": "shipDraft",
"subject": "American_submarine_NR-1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"*\\n*Box keel depth :\"@en",
"property": "shipDraft",
"subject": "American_submarine_NR-1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1050"
}
},
{
"1051": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket's final flight was on the 28th October 2014.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The final flight of the Antares rocket was on 2014-10-28.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014-10-28",
"property": "finalFlight",
"subject": "Antares_(rocket)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2014-10-28\"^^xsd:date",
"property": "finalFlight",
"subject": "Antares_(rocket)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1051"
}
},
{
"1052": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket was launched at the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The launch site of the Antares rocket was the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The launch site of the rocket Antares was the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
"property": "launchSite",
"subject": "Antares_(rocket)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
"property": "sites",
"subject": "Antares_(rocket)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1052"
}
},
{
"1053": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gabriela Michetti is the leader of Argentina.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gabriela_Michetti",
"property": "leaderName",
"subject": "Argentina"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Gabriela_Michetti",
"property": "leader",
"subject": "Argentina"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1053"
}
},
{
"1054": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Guiana Space Centre was the launch site of the Ariane 5.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 was launched from the Guiana Space Centre.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ariane 5 was launched from the Guiana Space Centre.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Guiana_Space_Centre",
"property": "launchSite",
"subject": "Ariane_5"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Guiana_Space_Centre",
"property": "sites",
"subject": "Ariane_5"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1054"
}
},
{
"1055": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 had its first voyage August 11, 2005.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ariane 5 had its maiden flight on August 11th 2005.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2005-08-11",
"property": "maidenFlight",
"subject": "Ariane_5"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2005-08-11\"^^xsd:date",
"property": "maidenFlight",
"subject": "Ariane_5"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1055"
}
},
{
"1056": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 is a convertible.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Convertible",
"property": "bodyStyle",
"subject": "Aston_Martin_V8"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Convertible",
"property": "bodyStyle",
"subject": "Aston_Martin_V8"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1056"
}
},
{
"1057": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manufacturer of the Aston Martin V8 is the Aston Martin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin made the Aston Martin V8.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 was manufactured by Aston Martin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Aston_Martin",
"property": "manufacturer",
"subject": "Aston_Martin_V8"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Aston_Martin",
"property": "manufacturer",
"subject": "Aston_Martin_V8"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1057"
}
},
{
"1058": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin Virage is made by the company Aston Martin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aston Martin are the manufacturers of the Aston Martin Virage.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin Virage is manufactured by Aston Martin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Aston_Martin",
"property": "manufacturer",
"subject": "Aston_Martin_Virage"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Aston_Martin",
"property": "manufacturer",
"subject": "Aston_Martin_Virage"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1058"
}
},
{
"1059": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlas II had its final flight on March 16th 1998.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The final flight of Atlas II was on 1998-03-16.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The final flight of the Atlas II was on the 16th March 1998.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1998-03-16",
"property": "finalFlight",
"subject": "Atlas_II"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1998-03-16\"^^xsd:date",
"property": "finalFlight",
"subject": "Atlas_II"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1059"
}
},
{
"1060": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlas II was launched from Vandenberg Air Force Base.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Atlas II was launched from Vandenberg Air Force Base.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vandenberg_Air_Force_Base",
"property": "launchSite",
"subject": "Atlas_II"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Vandenberg_Air_Force_Base",
"property": "sites",
"subject": "Atlas_II"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1060"
}
},
{
"1061": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Audi was founded by August Horch.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "August Horch founded Audi.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The company Audi was founded by August Horch.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "August_Horch",
"property": "foundedBy",
"subject": "Audi"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "August_Horch",
"property": "foundedBy",
"subject": "Audi"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1061"
}
},
{
"1062": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is assembled by Audi Brussels.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is assembled at Audi Brussels.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1062"
}
},
{
"1063": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Seat Ibiza and the Audi A1 are both cars and therefore a related means of transportation.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 and the Seat Ibiza are similar means of transport.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "SEAT_Ibiza",
"property": "relatedMeanOfTransportation",
"subject": "Audi_A1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"SEAT Ibiza Mk4\"@en",
"property": "related",
"subject": "Audi_A1"
}
],
[
{
"object": "SEAT_Ibiza",
"property": "relatedMeanOfTransportation",
"subject": "Audi_A1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1063"
}
},
{
"1064": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. is located in Peoria, Illinois.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. is in Peoria, Illinois.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peoria,_Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peoria,_Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1064"
}
},
{
"1065": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Costa Crociere is located in Genoa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Costa Crociere is in Genoa.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1065"
}
},
{
"1066": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The DeSoto Custom and Chrysler Newport are related types of transport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "DeSoto Custom is a related means of transport to Chrysler Newport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The DeSoto Custom is related to the Chrysler Newport.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chrysler_Newport",
"property": "relatedMeanOfTransportation",
"subject": "DeSoto_Custom"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chrysler_Newport",
"property": "relatedMeanOfTransportation",
"subject": "DeSoto_Custom"
}
],
[
{
"object": "Chrysler_Newport",
"property": "related",
"subject": "DeSoto_Custom"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1066"
}
},
{
"1067": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Fiat Croma is related to the Alfa Romeo 164.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alfa_Romeo_164",
"property": "relatedMeanOfTransportation",
"subject": "Fiat_Croma"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Alfa_Romeo_164",
"property": "related",
"subject": "Fiat_Croma"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1067"
}
},
{
"1068": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Germans are the people of Germany.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Germans is the demonym for people who live in Germany.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germans",
"property": "demonym",
"subject": "Germany"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Germans",
"property": "demonym",
"subject": "Germany"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1068"
}
},
{
"1069": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Norbert Lammert is a leader in Germany.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of Germany is Norbert Lammert.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Germany has a leader named Norbert Lammert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Norbert_Lammert",
"property": "leaderName",
"subject": "Germany"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Norbert_Lammert",
"property": "leaderName",
"subject": "Germany"
}
],
[
{
"object": "Norbert_Lammert",
"property": "leader",
"subject": "Germany"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1069"
}
},
{
"1070": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Germany is Stanislaw Tillich.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Stanislaw Tillich is a leader of Germany.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stanislaw_Tillich",
"property": "leaderName",
"subject": "Germany"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stanislaw_Tillich",
"property": "leader",
"subject": "Germany"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1070"
}
},
{
"1071": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gruppo Bertone was founded by Giovanni Bertone.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Giovanni_Bertone",
"property": "foundedBy",
"subject": "Gruppo_Bertone"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Giovanni_Bertone",
"property": "foundedBy",
"subject": "Gruppo_Bertone"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1071"
}
},
{
"1072": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Guiana Space Centre has its headquarters at Kourou in French Guiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Guiana Space Centre's headquarters are located in Kourou, French Guiana.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The headquarters of the Guiana Space Centre is in Kourou, French Guiana.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kourou,_French_Guiana",
"property": "headquarter",
"subject": "Guiana_Space_Centre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Kourou,_French_Guiana",
"property": "headquarters",
"subject": "Guiana_Space_Centre"
}
],
[
{
"object": "Kourou,_French_Guiana",
"property": "headquarter",
"subject": "Guiana_Space_Centre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1072"
}
},
{
"1073": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manufacturer of the Honda K engine is Honda.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Honda are the makers of the Honda K engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Honda K engine was manufactured by Honda.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Honda",
"property": "manufacturer",
"subject": "Honda_K_engine"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Honda",
"property": "manufacturer",
"subject": "Honda_K_engine"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1073"
}
},
{
"1074": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Star Spangled Banner is the national anthem of the united States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The anthem of the United States is the Star Spangled Banner.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Star Spangled Banner is the U.S. anthem.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "The_Star-Spangled_Banner",
"property": "anthem",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "The_Star-Spangled_Banner",
"property": "anthem",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1074"
}
},
{
"1075": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Native Americans are on of the ethnic groups in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States has an ethnic group called Native Americans.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Native Americans are an ethnic group in the U.S.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1075"
}
},
{
"1076": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Chairman of A C Milan is Silvio Berlusconi.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Silvio Berlusconi is the chiarman of A.C. Milan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Silvio_Berlusconi",
"property": "chairman",
"subject": "A.C._Milan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Silvio_Berlusconi",
"property": "chairman",
"subject": "A.C._Milan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1076"
}
},
{
"1077": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Boogaard's birthplace is Canada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Boogaard was born in Canada.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Canada",
"property": "birthPlace",
"subject": "Aaron_Boogaard"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Canada",
"property": "birthPlace",
"subject": "Aaron_Boogaard"
}
],
[
{
"object": "Canada",
"property": "placeOfBirth",
"subject": "Aaron_Boogaard"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1077"
}
},
{
"1078": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt played for the club SV Werder Bremen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt plays for SV Werder Bremen.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt played for SV Werder Bremen.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "SV_Werder_Bremen",
"property": "club",
"subject": "Aaron_Hunt"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "SV_Werder_Bremen",
"property": "clubs",
"subject": "Aaron_Hunt"
}
],
[
{
"object": "SV_Werder_Bremen",
"property": "team",
"subject": "Aaron_Hunt"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1078"
}
},
{
"1079": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez is in the Uruguay Olympic football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez played for the Uruguay Olympic football team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández plays for the Uruguay Olympic football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Uruguay_Olympic_football_team",
"property": "club",
"subject": "Abel_Hernández"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Uruguay_Olympic_football_team",
"property": "team",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1079"
}
},
{
"1080": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez's club is the Uruguay National football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez plays for the Uruguay national football team.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Uruguay_national_football_team",
"property": "club",
"subject": "Abel_Hernández"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Uruguay_national_football_team",
"property": "team",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1080"
}
},
{
"1081": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The footballer Abner was born in Brazil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abner (a footballer) was born in Brazil.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Football player, Abner, was born in Brazil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Brazil",
"property": "birthPlace",
"subject": "Abner_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Brazil",
"property": "birthPlace",
"subject": "Abner_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1081"
}
},
{
"1082": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher's birthplace is Diemen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Diemen is the birth place of Adam Maher.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher was born in Diemen.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Diemen",
"property": "birthPlace",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Diemen",
"property": "birthPlace",
"subject": "Adam_Maher"
}
],
[
{
"object": "Diemen",
"property": "placeOfBirth",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1082"
}
},
{
"1083": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher was born in the Netherlands.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Netherlands",
"property": "birthPlace",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Netherlands",
"property": "placeOfBirth",
"subject": "Adam_Maher"
}
],
[
{
"object": "Netherlands",
"property": "birthPlace",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1083"
}
},
{
"1084": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher played for the Netherlands national under-17 football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher plays for the Netherlands National club for players under 17.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Netherlands_national_under-17_football_team",
"property": "club",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Netherlands_national_under-17_football_team",
"property": "team",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1084"
}
},
{
"1085": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher's club is PSV Eindhoven.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "PSV_Eindhoven",
"property": "club",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "PSV_Eindhoven",
"property": "clubs",
"subject": "Adam_Maher"
}
],
[
{
"object": "PSV_Eindhoven",
"property": "team",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1085"
}
},
{
"1086": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ahmad Kadhim Assad plays for the Iraq national football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ahmad Kadhim Assad represented the Iraq national football team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ahmad Kadhim plays for the Iraqi national football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Iraq_national_football_team",
"property": "club",
"subject": "Ahmad_Kadhim_Assad"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Iraq_national_football_team",
"property": "team",
"subject": "Ahmad_Kadhim_Assad"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1086"
}
},
{
"1087": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams was born in Point Fortin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams' birthplace is Point Fortin.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Point_Fortin",
"property": "birthPlace",
"subject": "Akeem_Adams"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Point_Fortin",
"property": "birthPlace",
"subject": "Akeem_Adams"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1087"
}
},
{
"1088": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Dent's former team is the Atlanta Falcons.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Dent used to play for the Atlanta Falcons.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Atlanta_Falcons",
"property": "formerTeam",
"subject": "Akeem_Dent"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Atlanta_Falcons",
"property": "formerTeam",
"subject": "Akeem_Dent"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1088"
}
},
{
"1089": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley's club is the Connecticut Huskies.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley plays for the Connecticut Huskies.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Connecticut_Huskies",
"property": "club",
"subject": "Akeem_Priestley"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Connecticut_Huskies",
"property": "team",
"subject": "Akeem_Priestley"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1089"
}
},
{
"1090": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al-Khor Sports Club is in the Qatar Stars League.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al-Khor Sports Club play in the Qatar Stars League.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Qatar_Stars_League",
"property": "league",
"subject": "Al-Khor_Sports_Club"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Qatar_Stars_League",
"property": "league",
"subject": "Al-Khor_Sports_Club"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1090"
}
},
{
"1091": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Basim Qasim is the manager of Al-Zawra'a SC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Basim Qasim manages Al-Zawra'a SC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of Al-Zawra'a SC is Basim Qasim.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Basim_Qasim",
"property": "manager",
"subject": "Al-Zawra'a_SC"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Basim_Qasim",
"property": "manager",
"subject": "Al-Zawra'a_SC"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1091"
}
},
{
"1092": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Kharaitiyat SC play in the Qatar Stars League.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Qatar_Stars_League",
"property": "position",
"subject": "Al_Kharaitiyat_SC"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Qatar_Stars_League",
"property": "position",
"subject": "Al_Kharaitiyat_SC"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1092"
}
},
{
"1093": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul-Zahra was born in Baghdad.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baghdad is the birth place of Alaa Abdul-Zahra.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra was born in Baghdad.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baghdad",
"property": "birthPlace",
"subject": "Alaa_Abdul-Zahra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baghdad",
"property": "birthPlace",
"subject": "Alaa_Abdul-Zahra"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1093"
}
},
{
"1094": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul-Zahra's club is Sanat Mes Kerman F.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra plays for Sanat Mes Kerman FC.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sanat_Mes_Kerman_F.C.",
"property": "club",
"subject": "Alaa_Abdul-Zahra"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sanat_Mes_Kerman_F.C.",
"property": "clubs",
"subject": "Alaa_Abdul-Zahra"
}
],
[
{
"object": "Sanat_Mes_Kerman_F.C.",
"property": "team",
"subject": "Alaa_Abdul-Zahra"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1094"
}
},
{
"1095": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alan Martin played football for Hamilton Academical F.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Martin is a footballer for the Hamilton Academical FC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The footballer Alan Martin plays for Hamilton Academical F.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hamilton_Academical_F.C.",
"property": "club",
"subject": "Alan_Martin_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hamilton_Academical_F.C.",
"property": "clubs",
"subject": "Alan_Martin_(footballer,_born_1989)"
}
],
[
{
"object": "Hamilton_Academical_F.C.",
"property": "team",
"subject": "Alan_Martin_(footballer,_born_1989)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1095"
}
},
{
"1096": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alan Martin is a footballer with the Scotland national under-19 football team club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Martin played football for the Scotland national under 19 team.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Scotland_national_under-19_football_team",
"property": "club",
"subject": "Alan_Martin_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Scotland_national_under-19_football_team",
"property": "team",
"subject": "Alan_Martin_(footballer,_born_1989)"
}
],
[
{
"object": "Scotland_national_under-19_football_team",
"property": "nationalteam",
"subject": "Alan_Martin_(footballer,_born_1989)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1096"
}
},
{
"1097": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr's birthdate is 1995-09-02.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr. was born on the 2nd September 1995.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1995-09-02",
"property": "birthDate",
"subject": "Aleksander_Barkov,_Jr."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1995-09-02\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Aleksander_Barkov,_Jr."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1097"
}
},
{
"1098": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr has played for the Florida Panthers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov, Jr's club is the Florida Panthers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr. plays for the Florida Panthers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Florida_Panthers",
"property": "club",
"subject": "Aleksander_Barkov,_Jr."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Florida_Panthers",
"property": "team",
"subject": "Aleksander_Barkov,_Jr."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1098"
}
},
{
"1099": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr is 1.905m tall.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov, Jr's height is 1.905 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The height of Aleksander Barkov Jr. is 1.905.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1.905",
"property": "height",
"subject": "Aleksander_Barkov,_Jr."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1.905\"^^xsd:double",
"property": "height",
"subject": "Aleksander_Barkov,_Jr."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1099"
}
},
{
"1100": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr played in the National Hockey League.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr. plays in the National Hockey League.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "National_Hockey_League",
"property": "league",
"subject": "Aleksander_Barkov,_Jr."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "National_Hockey_League",
"property": "league",
"subject": "Aleksander_Barkov,_Jr."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1100"
}
},
{
"1101": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov's date of birth is 1989-02-24.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov was born on 24th February 1989.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov was born 1989-02-24.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1989-02-24",
"property": "birthDate",
"subject": "Aleksandr_Prudnikov"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1989-02-24\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Aleksandr_Prudnikov"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1101"
}
},
{
"1102": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Amkar Perm.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for the FC Amkar Perm football club.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Amkar_Perm",
"property": "club",
"subject": "Aleksandr_Prudnikov"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "FC_Amkar_Perm",
"property": "team",
"subject": "Aleksandr_Prudnikov"
}
],
[
{
"object": "FC_Amkar_Perm",
"property": "clubs",
"subject": "Aleksandr_Prudnikov"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1102"
}
},
{
"1103": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli was born in Batumi.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli's birth place is Batumi.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Batumi",
"property": "birthPlace",
"subject": "Aleksandre_Guruli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Batumi",
"property": "placeOfBirth",
"subject": "Aleksandre_Guruli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1103"
}
},
{
"1104": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli plays for FC Samtredia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli's club is FC Samtredia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Samtredia",
"property": "club",
"subject": "Aleksandre_Guruli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "FC_Samtredia",
"property": "clubs",
"subject": "Aleksandre_Guruli"
}
],
[
{
"object": "FC_Samtredia",
"property": "team",
"subject": "Aleksandre_Guruli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1104"
}
},
{
"1105": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli's club is the Georgia national under 21 football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli once played for the Georgia national under-21 football team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli plays for the Georgian national under 21 team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Georgia_national_under-21_football_team",
"property": "club",
"subject": "Aleksandre_Guruli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Georgia_national_under-21_football_team",
"property": "team",
"subject": "Aleksandre_Guruli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1105"
}
},
{
"1106": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays for A.S. Roma.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli is a member of A.S. Roma.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "A.S._Roma",
"property": "club",
"subject": "Alessio_Romagnoli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "A.S._Roma",
"property": "clubs",
"subject": "Alessio_Romagnoli"
}
],
[
{
"object": "A.S._Roma",
"property": "team",
"subject": "Alessio_Romagnoli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1106"
}
},
{
"1107": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays for the A.S. Roma youth team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays for A.S. Roma's youth team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli played in AS Roma's youth team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "A.S._Roma",
"property": "youthclub",
"subject": "Alessio_Romagnoli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "A.S._Roma",
"property": "youthclubs",
"subject": "Alessio_Romagnoli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1107"
}
},
{
"1108": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in Manitoba.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Plante's birthplace is Manitoba.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Manitoba",
"property": "birthPlace",
"subject": "Alex_Plante"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Manitoba",
"property": "placeOfBirth",
"subject": "Alex_Plante"
}
],
[
{
"object": "Manitoba",
"property": "birthPlace",
"subject": "Alex_Plante"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1108"
}
},
{
"1109": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus's league is the Turkish Basketball Super League.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays in the Turkish Basketball Super League.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The league Alex Tyus plays in is the Turkish Basketball Super League.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1109"
}
},
{
"1110": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlanta Falcons play in Atlanta.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlanta Falcons play in the city of Atlanta.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Atlanta",
"property": "city",
"subject": "Atlanta_Falcons"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Atlanta",
"property": "locationCity",
"subject": "Atlanta_Falcons"
}
],
[
{
"object": "Atlanta",
"property": "city",
"subject": "Atlanta_Falcons"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1110"
}
},
{
"1111": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Don Sweeney is general manager for the Boston Bruins.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Don Sweeney is general manager of the Boston Bruins.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The general Manager of the Boston Bruins is Don Sweeney.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Don_Sweeney",
"property": "generalManager",
"subject": "Boston_Bruins"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Don_Sweeney",
"property": "generalManager",
"subject": "Boston_Bruins"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1111"
}
},
{
"1112": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Budapest is a city in Hungary.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hungary",
"property": "country",
"subject": "Budapest"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hungary",
"property": "country",
"subject": "Budapest"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1112"
}
},
{
"1113": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "English is the language spoken in Canada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "English language is spoken in Canada.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The English language is spoken in Canada.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "Canada"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "Canada"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1113"
}
},
{
"1114": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "In Canada, the language is Slavey.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Canada's language is the Slavey language.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "One of the languages in Canada is Slavey.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Slavey_language",
"property": "language",
"subject": "Canada"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Slavey_language",
"property": "language",
"subject": "Canada"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1114"
}
},
{
"1115": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Columbus Blue Jackets' city is Columbus, Ohio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Columbus Blue Jackets are based in the city of Columbus, Ohio.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Columbus,_Ohio",
"property": "city",
"subject": "Columbus_Blue_Jackets"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Columbus,_Ohio",
"property": "city",
"subject": "Columbus_Blue_Jackets"
}
],
[
{
"object": "Columbus,_Ohio",
"property": "locationCity",
"subject": "Columbus_Blue_Jackets"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1115"
}
},
{
"1116": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Oleh Luzhny is the manager of FC Karpaty Lviv.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of FC Karpaty Lviv is Oleh Luzhny.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Oleh_Luzhny",
"property": "manager",
"subject": "FC_Karpaty_Lviv"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Oleh_Luzhny",
"property": "manager",
"subject": "FC_Karpaty_Lviv"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1116"
}
},
{
"1117": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Valery Petrakov is the manager of FC Torpedo Moscow.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valery_Petrakov",
"property": "manager",
"subject": "FC_Torpedo_Moscow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valery_Petrakov",
"property": "manager",
"subject": "FC_Torpedo_Moscow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1117"
}
},
{
"1118": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gábor Kubatov is the chairman of Ferencvárosi TC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Gabor Kubatov is the chairman of Ferencvarosi TC.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gábor_Kubatov",
"property": "chairman",
"subject": "Ferencvárosi_TC"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Gábor_Kubatov",
"property": "chairman",
"subject": "Ferencvárosi_TC"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1118"
}
},
{
"1119": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The language of Finland is the Finnish language.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Finnish language is spoken in Finland.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Finnish_language",
"property": "language",
"subject": "Finland"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Finnish_language",
"property": "language",
"subject": "Finland"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1119"
}
},
{
"1120": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The coach of the Italian national under 16 football team is Daniele Zoratto.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Daniele Zoratto is coach of the Italy national under 16 football team.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Daniele_Zoratto",
"property": "coach",
"subject": "Italy_national_under-16_football_team"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Daniele_Zoratto",
"property": "coach",
"subject": "Italy_national_under-16_football_team"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1120"
}
},
{
"1121": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Maccabi Ashdod BC's coach is Zvi Sherf.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The coach of Maccabi Ashdod B.C. is Zvi Sherf.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Zvi Sherf is a coach for Maccabi Ashdod BC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Zvi_Sherf",
"property": "coach",
"subject": "Maccabi_Ashdod_B.C."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Zvi_Sherf",
"property": "coach",
"subject": "Maccabi_Ashdod_B.C."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1121"
}
},
{
"1122": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Motherwell FC's ground is Fir Park.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Motherwell F.C. play their home games at Fir Park.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Motherwell F.C. home ground is located in Fir Park.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Fir_Park",
"property": "ground",
"subject": "Motherwell_F.C."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Fir_Park",
"property": "ground",
"subject": "Motherwell_F.C."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1122"
}
},
{
"1123": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jean-Michel Aulas is the owner of Olympique Lyonnais.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jean-Michel Aulas is the owner of the Olympique Lyonnais.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The owner of Olympique Lyonnais is Jean-Michel Aulas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jean-Michel_Aulas",
"property": "owner",
"subject": "Olympique_Lyonnais"
}
],
"originaltriplesets": {
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[
{
"object": "Jean-Michel_Aulas",
"property": "owner",
"subject": "Olympique_Lyonnais"
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]
},
"shape": "NA",
"shape_type": "NA",
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},
{
"1124": {
"category": "Athlete",
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"comment": "good",
"lang": "",
"lex": "The St. Louis Rams are based in the city of St. Louis.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "American Football team, St. Louis Rams, are based in the city of St. Louis.",
"xml_id": "Id2"
}
],
"links": [],
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{
"object": "St._Louis",
"property": "city",
"subject": "St._Louis_Rams"
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],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "St._Louis",
"property": "locationCity",
"subject": "History_of_the_St._Louis_Rams"
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],
[
{
"object": "St._Louis",
"property": "city",
"subject": "History_of_the_St._Louis_Rams"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1124"
}
},
{
"1125": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tennessee Titans are based in Nashville, Tennessee.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Tennessee Titans are based in the city of Nashville, Tennessee.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "American Football team Tennessee Titans are based in the city of Nashville, Tennessee.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Nashville,_Tennessee",
"property": "city",
"subject": "Tennessee_Titans"
}
],
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{
"object": "Nashville,_Tennessee",
"property": "locationCity",
"subject": "Tennessee_Titans"
}
],
[
{
"object": "Nashville,_Tennessee",
"property": "city",
"subject": "Tennessee_Titans"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1125"
}
},
{
"1126": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Bertram started performing in 1998.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1998",
"property": "activeYearsStartYear",
"subject": "Aaron_Bertram"
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],
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{
"object": "\"1998\"^^xsd:gYear",
"property": "activeYearsStartYear",
"subject": "Aaron_Bertram"
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]
]
},
"shape": "NA",
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"xml_id": "Id1126"
}
},
{
"1127": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer collaborates with The Horns of Happiness.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer plays with The Horns of Happiness.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
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"object": "The_Horns_of_Happiness",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Deer"
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],
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{
"object": "The_Horns_of_Happiness",
"property": "associatedBand",
"subject": "Aaron_Deer"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1127"
}
},
{
"1128": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer has been signed to the record label Kill Rock Stars.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer is signed to the record label Kill Rock Stars.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer's record label is called Kill Rock Stars.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kill_Rock_Stars",
"property": "recordLabel",
"subject": "Aaron_Deer"
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],
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{
"object": "Kill_Rock_Stars",
"property": "label",
"subject": "Aaron_Deer"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1128"
}
},
{
"1129": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner falls in the genre of avant-garde metal music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner performs avant-garde metal music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner also uses the musical genre Avant-garde metal.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Avant-garde_metal",
"property": "genre",
"subject": "Aaron_Turner"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Avant-garde_metal",
"property": "genre",
"subject": "Aaron_Turner"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1129"
}
},
{
"1130": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner plays progressive metal music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner performs progressive metal music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner uses the genre Progressive metal.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Progressive_metal",
"property": "genre",
"subject": "Aaron_Turner"
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],
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{
"object": "Progressive_metal",
"property": "genre",
"subject": "Aaron_Turner"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1130"
}
},
{
"1131": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abradab has a background as a solo singer.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abradab's background is that of solo singer.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"solo_singer\"",
"property": "background",
"subject": "Abradab"
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],
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"originaltripleset": [
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{
"object": "\"solo_singer\"@en",
"property": "background",
"subject": "Abradab"
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]
]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id1131"
}
},
{
"1132": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abradab was born on November 12th 1978.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The birth date of Abradab is 12th November 1978.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abradab was born on 12th November 1978.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1978-11-12",
"property": "birthDate",
"subject": "Abradab"
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],
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{
"object": "\"1978-11-12\"^^xsd:date",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1132"
}
},
{
"1133": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "The birth place of Abradab is Poland.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abradab was born in Poland.",
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}
],
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"object": "Poland",
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{
"object": "Poland",
"property": "placeOfBirth",
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],
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{
"object": "Poland",
"property": "birthPlace",
"subject": "Abradab"
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]
},
"shape": "NA",
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"xml_id": "Id1133"
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},
{
"1134": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "The musical genre of Abradab is hip hop music.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abradab performs hip hop music.",
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}
],
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"object": "Hip_hop_music",
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{
"object": "Hip_hop_music",
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"subject": "Abradab"
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]
},
"shape": "NA",
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"xml_id": "Id1134"
}
},
{
"1135": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Agustín Barboza plays Guarania style of music.",
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},
{
"comment": "good",
"lang": "",
"lex": "The genre Guarania, is the musical genre that Agustin Barboza is involved in.",
"xml_id": "Id2"
}
],
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"modifiedtripleset": [
{
"object": "Guarania_(music)",
"property": "genre",
"subject": "Agustín_Barboza"
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],
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{
"object": "Guarania_(music)",
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]
},
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},
{
"1136": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Ahmet Ertegun was born in Istanbul, Turkey.",
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}
],
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"object": "\"Istanbul, Turkey\"",
"property": "birthPlace",
"subject": "Ahmet_Ertegun"
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],
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{
"object": "\"Istanbul, Turkey\"@en",
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"subject": "Ahmet_Ertegun"
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},
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"xml_id": "Id1136"
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},
{
"1137": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Alan Frew is a performer of rock music.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Frews' musical genre is rock music.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Frew's genre is Rock music.",
"xml_id": "Id3"
}
],
"links": [],
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{
"object": "Rock_music",
"property": "genre",
"subject": "Alan_Frew"
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],
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{
"object": "Rock_music",
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"subject": "Alan_Frew"
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},
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"xml_id": "Id1137"
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},
{
"1138": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones has worked with the musical artist Sammy Price.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sammy_Price",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Albennie_Jones"
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],
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"originaltripleset": [
[
{
"object": "Sammy_Price",
"property": "associatedBand",
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],
[
{
"object": "Sammy_Price",
"property": "associatedMusicalArtist",
"subject": "Albennie_Jones"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1138"
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},
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"1139": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac is a solo singer.",
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},
{
"comment": "good",
"lang": "",
"lex": "A solo singer is part of Aleksandra Kovač's background,.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"solo_singer\"",
"property": "background",
"subject": "Aleksandra_Kovač"
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],
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[
{
"object": "\"solo_singer\"@en",
"property": "background",
"subject": "Aleksandra_Kovač"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1139"
}
},
{
"1140": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac was born in Yugoslavia.",
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},
{
"comment": "good",
"lang": "",
"lex": "The birth place of Aleksandra Kovac is Yugoslavia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Yugoslavia is the birth place of Aleksandra Kovač.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Yugoslavia",
"property": "birthPlace",
"subject": "Aleksandra_Kovač"
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],
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{
"object": "Yugoslavia",
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]
]
},
"shape": "NA",
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"xml_id": "Id1140"
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},
{
"1141": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "The musical genre of Aleksandra Kovac is rhythm and blues.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac's genre is rhythm and blues.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac is an exponent of Rhythm and blues.",
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}
],
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"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
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],
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[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1141"
}
},
{
"1142": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač's genre is soul music.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač performs soul music.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Soul_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
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],
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"originaltripleset": [
[
{
"object": "Soul_music",
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]
},
"shape": "NA",
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"xml_id": "Id1142"
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},
{
"1143": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Alex Day is Synthpop.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Day performs Synthpop music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Day uses the Synthpop genre.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Synthpop",
"property": "genre",
"subject": "Alex_Day"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Synthpop",
"property": "genre",
"subject": "Alex_Day"
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]
]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id1143"
}
},
{
"1144": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa is associated with fellow musician Héctor Numa Moraes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa is associated with Hector Numa Moraes.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa is associated with the artist Hector Numa Moraes.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Héctor_Numa_Moraes",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Alfredo_Zitarrosa"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Héctor_Numa_Moraes",
"property": "associatedBand",
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],
[
{
"object": "Héctor_Numa_Moraes",
"property": "associatedMusicalArtist",
"subject": "Alfredo_Zitarrosa"
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]
]
},
"shape": "NA",
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"xml_id": "Id1144"
}
},
{
"1145": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Montevideo is the birth place of Alfredo Zitarrosa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The birth place of Alfredo Zitarrosa is Montevideo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa was born in Montevideo.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Montevideo",
"property": "birthPlace",
"subject": "Alfredo_Zitarrosa"
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],
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{
"object": "Montevideo",
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]
},
"shape": "NA",
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"xml_id": "Id1145"
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},
{
"1146": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa died in Uruguay.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Uruguay",
"property": "deathPlace",
"subject": "Alfredo_Zitarrosa"
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],
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"originaltripleset": [
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{
"object": "Uruguay",
"property": "deathPlace",
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]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id1146"
}
},
{
"1147": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa was signed to the RCA Records label.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa's record label is RCA Records.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa has a recording label in RCA Records.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "RCA_Records",
"property": "recordLabel",
"subject": "Alfredo_Zitarrosa"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "RCA_Records",
"property": "recordLabel",
"subject": "Alfredo_Zitarrosa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1147"
}
},
{
"1148": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alison O'Donnell started performing in 1963.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1963 was the beginning of Alison O'Donnell's active years.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1963",
"property": "activeYearsStartYear",
"subject": "Alison_O'Donnell"
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],
"originaltriplesets": {
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{
"object": "\"1963\"^^xsd:gYear",
"property": "activeYearsStartYear",
"subject": "Alison_O'Donnell"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1148"
}
},
{
"1149": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alison O'Donnell plays jazz music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alison O'Donnell performs jazz music.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jazz",
"property": "genre",
"subject": "Alison_O'Donnell"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jazz",
"property": "genre",
"subject": "Alison_O'Donnell"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1149"
}
},
{
"1150": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alison O'Donnell is a singer and also plays the bodhran, percussion and autoharp.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alison O'Donnell is a musician who uses her voice,bodhran,percussion and autoharp.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The instruments that Alison O'Donnell plays are bodhrán, percussion, autoharp plus she also sings.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Voice, bodhrán, percussion, autoharp\"",
"property": "instrument",
"subject": "Alison_O'Donnell"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Voice, bodhrán, percussion, autoharp\"@en",
"property": "instrument",
"subject": "Alison_O'Donnell"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1150"
}
},
{
"1151": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Allen Forrest was born on February 4, 1981.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allen Forrest's birth date is 04th February 1981.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Allen Forrest's birth date is 1981-02-04.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1981-02-04",
"property": "birthDate",
"subject": "Allen_Forrest"
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],
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{
"object": "\"1981-02-04\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Allen_Forrest"
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]
]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id1151"
}
},
{
"1152": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Allen Forrest plays pop music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Allen Forrest was a pop artist.",
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},
{
"comment": "good",
"lang": "",
"lex": "Allen Forrest is an exponent of Pop music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pop_music",
"property": "genre",
"subject": "Allen_Forrest"
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],
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{
"object": "Pop_music",
"property": "genre",
"subject": "Allen_Forrest"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1152"
}
},
{
"1153": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The location of Alligator Records is Chicago.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alligator records is based in Chicago.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alligator Records is located in Chicago.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chicago",
"property": "location",
"subject": "Alligator_Records"
}
],
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"originaltripleset": [
[
{
"object": "Chicago",
"property": "location",
"subject": "Alligator_Records"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1153"
}
},
{
"1154": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne is associated with the musical artist Johnny Sansone.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne and Johnny Sansone are associates.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Johnny_Sansone",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Anders_Osborne"
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],
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"originaltripleset": [
[
{
"object": "Johnny_Sansone",
"property": "associatedActs",
"subject": "Anders_Osborne"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1154"
}
},
{
"1155": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne performs rhythm and blues music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rhythm and blues is the musical genre of Anders Osborne.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne's genre is Rhythm and blues.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Anders_Osborne"
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],
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"originaltripleset": [
[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Anders_Osborne"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1155"
}
},
{
"1156": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne was signed to the record label 'Alligator Records'.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne is signed to the record label Alligator Records.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Anders Osborne's record label is Alligator Records.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alligator_Records",
"property": "recordLabel",
"subject": "Anders_Osborne"
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],
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"originaltripleset": [
[
{
"object": "Alligator_Records",
"property": "label",
"subject": "Anders_Osborne"
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],
[
{
"object": "Alligator_Records",
"property": "recordLabel",
"subject": "Anders_Osborne"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1156"
}
},
{
"1157": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andra is a rhythm and blues singer.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andra performs rhythm and blues music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The singer Andra's genre is Rhythm and blues.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Andra_(singer)"
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],
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"originaltripleset": [
[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Andra_(singer)"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1157"
}
},
{
"1158": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel began his musical career in 2009.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel started his career in 2009.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2009",
"property": "activeYearsStartYear",
"subject": "Andrew_Rayel"
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],
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"originaltripleset": [
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{
"object": "\"2009\"^^xsd:gYear",
"property": "activeYearsStartYear",
"subject": "Andrew_Rayel"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1158"
}
},
{
"1159": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel is associated with Christian Burns.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The musicians Andrew Rayel and Christian Burns are associates.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Christian_Burns",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Andrew_Rayel"
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],
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"originaltripleset": [
[
{
"object": "Christian_Burns",
"property": "associatedBand",
"subject": "Andrew_Rayel"
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],
[
{
"object": "Christian_Burns",
"property": "associatedMusicalArtist",
"subject": "Andrew_Rayel"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1159"
}
},
{
"1160": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The birth place of Andrew Rayel is Chisinau, Moldova.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel was born in Chisinau, Moldova.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Chișinău, Moldova\"",
"property": "birthPlace",
"subject": "Andrew_Rayel"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Chișinău, Moldova\"@en",
"property": "placeOfBirth",
"subject": "Andrew_Rayel"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1160"
}
},
{
"1161": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The musician, Andrew White, is associated with the musical artist, Marry Banilow.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew White is associated with the musical artist Marry Banilow.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Marry_Banilow",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Marry_Banilow",
"property": "associatedActs",
"subject": "Andrew_White_(musician)"
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],
[
{
"object": "Marry_Banilow",
"property": "associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
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],
[
{
"object": "Marry_Banilow",
"property": "associatedBand",
"subject": "Andrew_White_(musician)"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1161"
}
},
{
"1162": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrew White is a musician signed to the record label B-Unique Records.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The musician Andrew White is signed to the record label B-Unique Records.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "B-Unique_Records",
"property": "recordLabel",
"subject": "Andrew_White_(musician)"
}
],
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"originaltripleset": [
[
{
"object": "B-Unique_Records",
"property": "label",
"subject": "Andrew_White_(musician)"
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],
[
{
"object": "B-Unique_Records",
"property": "recordLabel",
"subject": "Andrew_White_(musician)"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1162"
}
},
{
"1163": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asunción is a part of Gran Asunción.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asunción is part of Gran Asunción.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gran_Asunción",
"property": "isPartOf",
"subject": "Asunción"
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],
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"originaltripleset": [
[
{
"object": "Gran_Asunción",
"property": "isPartOf",
"subject": "Asunción"
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],
[
{
"object": "Gran_Asunción",
"property": "subdivisionName",
"subject": "Asunción"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1163"
}
},
{
"1164": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Christian Burns plays the guitar.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Christian Burns plays guitar.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Guitar",
"property": "instrument",
"subject": "Christian_Burns"
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],
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"originaltripleset": [
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{
"object": "Guitar",
"property": "instrument",
"subject": "Christian_Burns"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1164"
}
},
{
"1165": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Hip hop music has the sub genre Gangsta rap.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Gangsta rap is a sub genre of Hip Hop music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Hip hop music has a sub genre called Gangsta rap.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gangsta_rap",
"property": "musicSubgenre",
"subject": "Hip_hop_music"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Gangsta_rap",
"property": "musicSubgenre",
"subject": "Hip_hop_music"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1165"
}
},
{
"1166": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Hip hop music originated from funk music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Hip Hop music has its origins in Funk.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Hip hop music has its stylistic origins in funk.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Funk",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
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],
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"originaltripleset": [
[
{
"object": "Funk",
"property": "stylisticOrigins",
"subject": "Hip_hop_music"
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],
[
{
"object": "Funk",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1166"
}
},
{
"1167": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The stylistic origin of indie rock is new wave music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Indie Rock has its origins in New wave music.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "New_wave_music",
"property": "stylisticOrigin",
"subject": "Indie_rock"
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],
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[
{
"object": "New_wave_music",
"property": "stylisticOrigin",
"subject": "Indie_rock"
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],
[
{
"object": "New_wave_music",
"property": "stylisticOrigins",
"subject": "Indie_rock"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1167"
}
},
{
"1168": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Manchester is part of Greater Manchester.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Manchester is a part of Greater Manchester.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Manchester is part of the Greater Manchester area.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greater_Manchester",
"property": "isPartOf",
"subject": "Manchester"
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],
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"originaltripleset": [
[
{
"object": "Greater_Manchester",
"property": "isPartOf",
"subject": "Manchester"
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]
]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id1168"
}
},
{
"1169": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Polydor Records are based in London.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Polydor Records is located in London.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Polydor records are located in London.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "London",
"property": "location",
"subject": "Polydor_Records"
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],
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"originaltripleset": [
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{
"object": "London",
"property": "location",
"subject": "Polydor_Records"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1169"
}
},
{
"1170": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The distribution company for RCA Records is Sony Music Entertainment.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "RCA Records distribute through Sony Music Entertainment.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sony_Music_Entertainment",
"property": "distributingCompany",
"subject": "RCA_Records"
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],
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"originaltripleset": [
[
{
"object": "Sony_Music_Entertainment",
"property": "distributingCompany",
"subject": "RCA_Records"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1170"
}
},
{
"1171": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Rock and roll music originated from blues music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rock and Roll has its stylistic origin in the Blues.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Rock and roll's stylistic origins come from Blues.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Blues",
"property": "stylisticOrigin",
"subject": "Rock_and_roll"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Blues",
"property": "stylisticOrigin",
"subject": "Rock_and_roll"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1171"
}
},
{
"1172": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Synthpop is a form of pop music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Synthpop's stylistic origins come from pop music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The stylistic origin of synthpop is pop music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pop_music",
"property": "stylisticOrigin",
"subject": "Synthpop"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pop_music",
"property": "stylisticOrigins",
"subject": "Synthpop"
}
],
[
{
"object": "Pop_music",
"property": "stylisticOrigin",
"subject": "Synthpop"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1172"
}
},
{
"1173": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "People from the United Kingdom are called British people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The native people of the United Kingdom are known as the British people.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "British people is a demonym for people in the United Kingdom.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "British_people",
"property": "demonym",
"subject": "United_Kingdom"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "British_people",
"property": "demonym",
"subject": "United_Kingdom"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1173"
}
},
{
"1174": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "(19255) 1994 VK8 has a density of 2.0 grams per cubic centimetre.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as (19255) 1994 VK8 has a density of 2 grams per cubic centimetres.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2.0 (gramPerCubicCentimetres)",
"property": "density",
"subject": "(19255)_1994_VK8"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2.0\"^^<http://dbpedia.org/datatype/gramPerCubicCentimetre>",
"property": "density",
"subject": "(19255)_1994_VK8"
}
],
[
{
"object": "\"2000.0\"^^<http://dbpedia.org/datatype/kilogramPerCubicMetre>",
"property": "Planet/density",
"subject": "(19255)_1994_VK8"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1174"
}
},
{
"1175": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "(29075) 1950 DA was discovered by Carl A Wirtanen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Carl A Wirtanen discovered (29075) 1950 DA.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Carl A Wirtanen was the discoverer of 29075 1950 DA.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Carl_A._Wirtanen",
"property": "discoverer",
"subject": "(29075)_1950_DA"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Carl_A._Wirtanen",
"property": "discoverer",
"subject": "(29075)_1950_DA"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1175"
}
},
{
"1176": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "(410777) 2009 FD has an absolute magnitude of 22.1.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The absolute magnitude of (410777) 2009 FD is 22.1.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "22.1",
"property": "absoluteMagnitude",
"subject": "(410777)_2009_FD"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"22.1\"^^xsd:double",
"property": "absMagnitude",
"subject": "(410777)_2009_FD"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1176"
}
},
{
"1177": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "(410777) 2009 FD has an apoapsis of 259776702.47055 kilometres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The apoapsis of (410777) 2009 FD is 259776702.47055 kilometers.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "259776702.47055 (kilometres)",
"property": "apoapsis",
"subject": "(410777)_2009_FD"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2.5977670247055E8\"^^<http://dbpedia.org/datatype/kilometre>",
"property": "Planet/apoapsis",
"subject": "(410777)_2009_FD"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1177"
}
},
{
"1178": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "(66063) 1998 RO1 has an apoapsis of 254989570.60815 kilometres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The apoapsis for (66063) 1998 RO1 is 254989570.60815 kilometres.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "254989570.60815 (kilometres)",
"property": "apoapsis",
"subject": "(66063)_1998_RO1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2.5498957060815E8\"^^<http://dbpedia.org/datatype/kilometre>",
"property": "Planet/apoapsis",
"subject": "(66063)_1998_RO1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1178"
}
},
{
"1179": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "66063 1998 RO1 has an epoch date of 4 November 2013.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "(66063) 1998 RO1 has an epoch date of 2013-11-04.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The epoch of (66063) 1998 RO1 is on the 13th of January 2016.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2013-11-04",
"property": "epoch",
"subject": "(66063)_1998_RO1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"November 4, 2013\"",
"property": "epoch",
"subject": "(66063)_1998_RO1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1179"
}
},
{
"1180": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1000 Piazzia has an escape velocity of 0.0252 kilometres per second.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The escape velocity of 1000 Piazzia is 0.0252 km/sec.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The escape velocity of 1000 Piazzia is 0.0252 (kilometrePerSeconds).",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "0.0252 (kilometrePerSeconds)",
"property": "escapeVelocity",
"subject": "1000_Piazzia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"0.0252\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "escapeVelocity",
"subject": "1000_Piazzia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1180"
}
},
{
"1181": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1001 Gaussia was formerly known as 1923 OAA907 XC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The former name of 1001 Gaussia is \"1923 OAA907 XC\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "1001 Gaussia's former name was 1923 OAA907 XC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"1923 OAA907 XC\"",
"property": "formerName",
"subject": "1001_Gaussia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1923 OAA907 XC\"@en",
"property": "formerName",
"subject": "1001_Gaussia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1181"
}
},
{
"1182": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "the maximum temperature of 1001 Gaussia is 165 kelvins.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 1001 Gaussia has a maximum temperature of 165 kelvins.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "165.0 (kelvins)",
"property": "maximumTemperature",
"subject": "1001_Gaussia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"165.0\"^^<http://dbpedia.org/datatype/kelvin>",
"property": "Planet/maximumTemperature",
"subject": "1001_Gaussia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1182"
}
},
{
"1183": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "101 Helena has an apoapsis of 441092000.0 (kilometres).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "101 Helena has an apoapsis of 441092000.0 kilometres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The apoapsis of 101 Helena is 441092000.0 kilometres.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "441092000.0 (kilometres)",
"property": "apoapsis",
"subject": "101_Helena"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"4.41092E8\"^^<http://dbpedia.org/datatype/kilometre>",
"property": "Planet/apoapsis",
"subject": "101_Helena"
}
],
[
{
"object": "4.41092e+11",
"property": "apoapsis",
"subject": "101_Helena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1183"
}
},
{
"1184": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The epoch of 101 Helena is 2006-12-31.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "101 Helena has an epoch of 2006-12-31.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2006-12-31",
"property": "epoch",
"subject": "101_Helena"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"2006-12-31\"^^xsd:date",
"property": "epoch",
"subject": "101_Helena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1184"
}
},
{
"1185": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "101 Helena has a mass of 3.0 kgs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "101 Helena has a mass of 3.0 kilograms.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3.0 (kilograms)",
"property": "mass",
"subject": "101_Helena"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3.0\"^^<http://dbpedia.org/datatype/kilogram>",
"property": "mass",
"subject": "101_Helena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1185"
}
},
{
"1186": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1036 Ganymed has an average speed of 16.86 km per sec.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1036 Ganymed travels at an average speed of 16.86 kilometres per second.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 1036 Ganymed has an average speed of 16.86 k.p.s.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "16.86 (kilometrePerSeconds)",
"property": "averageSpeed",
"subject": "1036_Ganymed"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"16.86\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "avgSpeed",
"subject": "1036_Ganymed"
}
],
[
{
"object": "\"60696.0\"^^xsd:double",
"property": "averageSpeed",
"subject": "1036_Ganymed"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1186"
}
},
{
"1187": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "103 Hera has an absolute magnitude of 7.66.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 103 Hera has an absolute magnitude of 7.66.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The absolute magnitude of 103 Hera is 7.66.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "7.66",
"property": "absoluteMagnitude",
"subject": "103_Hera"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"7.66\"^^xsd:double",
"property": "absoluteMagnitude",
"subject": "103_Hera"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1187"
}
},
{
"1188": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "103 Hera's mass is 7.9kg.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "103 Hera has a mass of 7.9 kilograms.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "7.9 (kilograms)",
"property": "mass",
"subject": "103_Hera"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"7.9\"^^<http://dbpedia.org/datatype/kilogram>",
"property": "Planet/mass",
"subject": "103_Hera"
}
],
[
{
"object": "\"7900.0\"^^xsd:double",
"property": "mass",
"subject": "103_Hera"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1188"
}
},
{
"1189": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The asteroid called 107 Camilla has an absolute magnitude of 7.08.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The absolute magnitude of 107 Camilla is 7.08.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "107 Camilla has an absolute magnitude of 7.08.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "7.08",
"property": "absoluteMagnitude",
"subject": "107_Camilla"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"7.08\"^^xsd:double",
"property": "absoluteMagnitude",
"subject": "107_Camilla"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1189"
}
},
{
"1190": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "107 Camilla was discovered by A Storrs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A Storrs discovered 107 Camilla.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The asteroid called 107 Camilla was discovered by A. Storrs.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "A._Storrs",
"property": "discoverer",
"subject": "107_Camilla"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "A._Storrs",
"property": "discoverer",
"subject": "107_Camilla"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1190"
}
},
{
"1191": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1089 Tama had the former name of \"A894 VA; A904 VD;\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1089 Tama was formerly called \"A894 VA; A904 VD;\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The former name of 1089 Tama is \"A894 VA; A904 VD;\".",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"A894 VA; A904 VD;\"",
"property": "formerName",
"subject": "1089_Tama"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"A894 VA; A904 VD;\"@en",
"property": "formerName",
"subject": "1089_Tama"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1191"
}
},
{
"1192": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1089 Tama has an orbital period of 1202.846 days.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1202.846 (days)",
"property": "orbitalPeriod",
"subject": "1089_Tama"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "1.03926e+08",
"property": "orbitalPeriod",
"subject": "1089_Tama"
}
],
[
{
"object": "\"1202.846\"^^<http://dbpedia.org/datatype/day>",
"property": "Planet/orbitalPeriod",
"subject": "1089_Tama"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1192"
}
},
{
"1193": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1097 Vicia has an average speed of 17.92 kilometres per second.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 1097 Vicia has an average speed of 17.92 k.p.s.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "17.92 (kilometrePerSeconds)",
"property": "averageSpeed",
"subject": "1097_Vicia"
}
],
"originaltriplesets": {
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{
"object": "\"64512.0\"^^xsd:double",
"property": "averageSpeed",
"subject": "1097_Vicia"
}
],
[
{
"object": "\"17.92\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "Planet/averageSpeed",
"subject": "1097_Vicia"
}
],
[
{
"object": "\"17.92\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "avgSpeed",
"subject": "1097_Vicia"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
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}
},
{
"1194": {
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"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The epoch of 1097 Vicia is on 13 January 2016.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The epoch date of 1097 Vicia is 2006.12.31.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2006-12-31",
"property": "epoch",
"subject": "1097_Vicia"
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],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "\"December 31, 2006 (JD2454100.5)\"",
"property": "epoch",
"subject": "1097_Vicia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
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}
},
{
"1195": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The rotation period of 1097 Vicia is 95040.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1097 Vicia has a rotation period of 95040.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The rotation period of 1097 Vicia is 95040.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "95040.0",
"property": "rotationPeriod",
"subject": "1097_Vicia"
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],
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{
"object": "\"95040.0\"^^xsd:double",
"property": "rotationPeriod",
"subject": "1097_Vicia"
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]
},
"shape": "NA",
"shape_type": "NA",
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}
},
{
"1196": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Christian Heinrich Friedrich Peters was the discoverer of 109 Felicitas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "109 Felicitas was discovered by Christian Heinrich Friedrich Peters.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Christian Heinrich Friedrich Peters discovered 109 Felicitas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Christian_Heinrich_Friedrich_Peters",
"property": "discoverer",
"subject": "109_Felicitas"
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],
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{
"object": "Christian_Heinrich_Friedrich_Peters",
"property": "discoverer",
"subject": "109_Felicitas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1196"
}
},
{
"1197": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "109 Felicitas has a periapsis of 283326000000.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The periapsis of 109 Felicitas is 283326000000.0.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "283326000000.0",
"property": "periapsis",
"subject": "109_Felicitas"
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],
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"originaltripleset": [
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{
"object": "2.83326e+11",
"property": "periapsis",
"subject": "109_Felicitas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1197"
}
},
{
"1198": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The average speed of 10 Hygiea is 16.76 km per secs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "10 Hygiea has an average speed of 16.76 kilometres per second.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The average speed of the asteroid 10 Hygiea is 16.76 kilometres per second.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "16.76 (kilometrePerSeconds)",
"property": "averageSpeed",
"subject": "10_Hygiea"
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],
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{
"object": "\"16.76\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "avgSpeed",
"subject": "10_Hygiea"
}
],
[
{
"object": "\"16.76\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
"property": "Planet/averageSpeed",
"subject": "10_Hygiea"
}
],
[
{
"object": "\"60336.0\"^^xsd:double",
"property": "averageSpeed",
"subject": "10_Hygiea"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1198"
}
},
{
"1199": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The periapsis of 1101 Clematis is 445895000000.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1101 Clematis has a periapsis of 445895000000.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "1101 Clemantis has a periapsis of 445895000000.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "445895000000.0",
"property": "periapsis",
"subject": "1101_Clematis"
}
],
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"originaltripleset": [
[
{
"object": "4.45895e+11",
"property": "periapsis",
"subject": "1101_Clematis"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1199"
}
},
{
"1200": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "110 Lydia has an epoch date of December 31st 2006.",
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},
{
"comment": "good",
"lang": "",
"lex": "31 December 2006 is the epoch of 110 Lydia.",
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},
{
"comment": "good",
"lang": "",
"lex": "110 Lydia's epoch is 31st December 2006.",
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}
],
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{
"object": "2006-12-31",
"property": "epoch",
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{
"object": "\"2006-12-31\"^^xsd:date",
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},
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"xml_id": "Id1200"
}
},
{
"1201": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "11264 Claudiomaccone has a temperature of 173.0 (kelvins).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "11264 Claudiomaccone has a temperature of 173.0 kelvins.",
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}
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"property": "temperature",
"subject": "11264_Claudiomaccone"
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"object": "\"173.0\"^^<http://dbpedia.org/datatype/kelvin>",
"property": "Planet/temperature",
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[
{
"object": "\"~173 K\"@en",
"property": "singleTemperature",
"subject": "11264_Claudiomaccone"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1201"
}
},
{
"1202": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson's alma mater was the University of Michigan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson's Alma mater is the University of Michigan.",
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},
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson studied at the University of Michigan.",
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}
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"links": [],
"modifiedtripleset": [
{
"object": "University_of_Michigan",
"property": "almaMater",
"subject": "James_Craig_Watson"
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"originaltripleset": [
[
{
"object": "University_of_Michigan",
"property": "education",
"subject": "James_Craig_Watson"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1202"
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},
{
"1203": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "N R Pogson was born in Nottingham.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "N. R. Pogson was born in Nottingham.",
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}
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"links": [],
"modifiedtripleset": [
{
"object": "Nottingham",
"property": "birthPlace",
"subject": "N._R._Pogson"
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{
"object": "Nottingham",
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{
"object": "Nottingham",
"property": "placeOfBirth",
"subject": "N._R._Pogson"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1203"
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},
{
"1204": {
"category": "Politician",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron S. Daggett fought in the Battle of Fredericksburg and received the Purple Heart.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aaron S Daggett, fought in battles, most notably the Battle of Fredericksburg, he was awarded the Purple Heart.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aaron S Daggett was awarded the Purple Heart and fought in the Battle of Fredericksburg.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Purple_Heart",
"property": "award",
"subject": "Aaron_S._Daggett"
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{
"object": "Battle_of_Fredericksburg",
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{
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},
"shape": "NA",
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},
{
"1205": {
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"comment": "good",
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"lex": "Aaron S Daggett fought at the Battle of Gettysburg and was awarded the Purple Heart.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aaron S. Daggett fought in the Battle of Gettysburg and was awarded the Purple Heart.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aaron S. Daggett, who fought in the Battle of Gettysburg, was awarded the Purple Heart.",
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}
],
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"modifiedtripleset": [
{
"object": "Battle_of_Gettysburg",
"property": "battles",
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{
"object": "Purple_Heart",
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{
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},
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"1206": {
"category": "Politician",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Stephen Yong Kuet Tze is Abdul Rahman Ya'kub's deputy. Abdul Taib Mahmud was Abdul Rahman Ya'kub's successor.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdul Rahman Ya'kub, who succeeded Abdul Taib Mahmud, has a deputy named Stephen Yong Kuet Tze.",
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},
{
"comment": "good",
"lang": "",
"lex": "The successor of Abdul Taib Mahmud was Abdul Rahman Ya'kub, whose deputy is Stephen Yong Kuet Tze.",
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}
],
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"modifiedtripleset": [
{
"object": "Stephen_Yong_Kuet_Tze",
"property": "hasDeputy",
"subject": "Abdul_Rahman_Ya'kub"
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{
"object": "Abdul_Rahman_Ya'kub",
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{
"object": "Stephen_Yong_Kuet_Tze",
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{
"object": "Abdul_Rahman_Ya'kub",
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]
},
"shape": "NA",
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"xml_id": "Id1206"
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},
{
"1207": {
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{
"comment": "good",
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"lex": "Abdul Taid Mahmud was born in Sarawak and is part of the Parti Pesaka Bumiputera Bersatu.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud was born in the Kingdom of Sarawak and his party is the Parti Pesaka Bumiputera Bersatu.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud, who was born in Sarawak, belongs to the Parti Pesaka Bumiputera Bersatu.",
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}
],
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"modifiedtripleset": [
{
"object": "Parti_Pesaka_Bumiputera_Bersatu",
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{
"object": "Kingdom_of_Sarawak",
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{
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{
"object": "Kingdom_of_Sarawak",
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{
"object": "Kingdom_of_Sarawak",
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{
"object": "\"Parti Pesaka Bumiputera Bersatu\"@en",
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},
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},
{
"1208": {
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{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar, was born in Minna, and has the Technical Institute Kaduna, for an almar mater.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar was born in Minna and he attended the Technical Institute Kaduna.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar was born in Minna and attended the Technical Institute in Kaduna.",
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}
],
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"modifiedtripleset": [
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{
"object": "Technical_Institute,_Kaduna",
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{
"object": "Minna",
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{
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},
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},
{
"1209": {
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{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar was born in Niger State and was the Chief of the Defence Staff in Nigeria.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar, who was born in Niger State, was the Chief of the Defence Staff in Nigeria.",
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}
],
"links": [],
"modifiedtripleset": [
{
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{
"object": "Niger_State",
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],
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{
"object": "\"Chief of Defence Staff\"",
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"object": "Niger_State",
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],
[
{
"object": "Niger_State",
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{
"object": "Chief_of_the_Defence_Staff_(Nigeria)",
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},
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},
{
"1210": {
"category": "Politician",
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"comment": "good",
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"lex": "Al-Amin Daggash was succeeded as Chief of the Defence Staff in Nigeria by Abdulsalami Abubakar.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar was the Chief of the Defence Staff in Nigeria and he was succeeded by Al-Amin Daggash.",
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},
{
"comment": "good",
"lang": "",
"lex": "Al-Amin Daggash was succeeded by Abdulsalami Abubakar, who worked as the Nigerian Chief of the Defence Staff.",
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}
],
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{
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{
"object": "Al-Amin_Daggash",
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{
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"object": "Chief_of_the_Defence_Staff_(Nigeria)",
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],
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{
"object": "\"Chief of Defence Staff\"",
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{
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},
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},
{
"1211": {
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{
"comment": "good",
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},
{
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"lex": "Abdulsalami Abubakar ended his career on 1999-05-29 and was succeeded by Olusegun Obasanjo.",
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},
{
"comment": "good",
"lang": "",
"lex": "Olusegun Obasanjo was the successor to Abdulsalami Abubakar, who ended his career on 1999-05-29.",
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}
],
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{
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},
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"comment": "good",
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"lex": "Abdulsalami Abubakar was also in office while Mr. Mike Akhigbe was Vice President. Olusegun Obasanjo succeeded Mr. Abubaka.",
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{
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{
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"lex": "Joseph Stalin was a commander in World War II in which Abner W Sibal also fought.",
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"comment": "good",
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"lex": "Abner W. Sibal fought in World War II and the commander of Russian during that war was Joseph Stalin.",
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"1214": {
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"lex": "Abner W. Sibal serve in the United States army which took part in the Korean War.",
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"lex": "Adam Holloway went to school at Magdalene College Cambridge and was born in Kent.",
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"comment": "good",
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"lex": "Adam Holloway was born in Kent and attended Magdalene College in Cambridge.",
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"1216": {
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"comment": "good",
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"lex": "Born in Kent, Adam Holloway now lives in Gravesend.",
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"comment": "good",
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"lex": "Adam Holloway was born in Kent and lives in Gravesend.",
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"comment": "good",
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"lex": "Adam Holloway was born in Kent and lived in Gravesend.",
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"1217": {
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"lex": "Adam Koc, who won the Virtuti Militari award, fought in World War I.",
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"comment": "good",
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"lex": "Adam Koc fought in WWI and he was awarded the Virtuti Militari award.",
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"comment": "good",
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"lex": "Adam Koc fought in WW1 and received the Virtuti Militari award.",
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{
"1218": {
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"comment": "good",
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"lex": "Adonis Georgiadis was born in Athens and has been the Deputy Parliamentary Spokesman of the Popular Orthodox Rally.",
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{
"comment": "good",
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{
"comment": "good",
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"comment": "good",
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"lex": "Adonis Georgiadis was born in Athens, in Greece.",
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{
"comment": "good",
"lang": "",
"lex": "Adonis Georgiadis was born in Athens in Greece.",
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"object": "Athens",
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"comment": "good",
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"lex": "Greek born Adonis Georgiadis he has worked as the Deputy Parliamentary Spokesman of Popular Orthodox Rally.",
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"lex": "Agnes Kant, born in Hessisch Oldendorf, worked as the Parliamentary group leader.",
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"comment": "good",
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"lex": "Agnes Kant was born in Hessisch Oldendorf and was employed as a Parliamentary group leader.",
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"comment": "good",
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"lex": "Agnes Kant birthplace was in Hessisch Oldendorf. He was a Parliamentary group leader.",
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"comment": "good",
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"lex": "Airey Neave served in the Royal Artillery Unit and was awarded the Military Cross.",
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"comment": "good",
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"lex": "Airey Neave received the Military Cross and served in the Royal Artillery.",
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"comment": "good",
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"lex": "Airey Neave's unit was the Royal Artillery and he was awarded the Military Cross.",
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"subject": "Airey_Neave"
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"comment": "good",
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"lex": "Airey Neave was part of the Battle of France but started his career on March 4th, 1974.",
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"comment": "good",
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"lex": "Airey Neave, began his career on March 4th 1974, and fought in the Battle of France.",
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"comment": "good",
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"lex": "Airey Neave began his career on March 4th 1974 and was involved in the Battle of France.",
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"object": "Battle_of_France",
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"lex": "Hugo Sperrle was a commander in the Battle of France in which Airey Neave was also involved.",
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"1225": {
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"comment": "good",
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"comment": "good",
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"lex": "Born in Kinightsbridge, Airey Neave fought in World War II.",
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"comment": "good",
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"lex": "Airey Neave was born in Knightsbridge and fought in World War II.",
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"1226": {
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"comment": "good",
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"comment": "good",
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"lex": "Franklin D Roosevelt was a commander in World War II , in which Airey Neave fought.",
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"comment": "good",
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},
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"1227": {
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"comment": "good",
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"comment": "good",
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"lex": "Joseph Stalin was a commander in World War II, the war Airey Neave was involved in.",
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"comment": "good",
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"lex": "Airey Neave fought in World War II, where one of the commanders was, joseph Stalin.",
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},
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"1228": {
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"comment": "good",
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"lex": "Albert B White was born on 22nd September 1856 and died in 1941.",
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"comment": "good",
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"lex": "Albert B White was born 1856-09-22 and died in 1941.",
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"comment": "good",
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"lex": "Albert B. White was born on 22nd September 1856 and died in 1941.",
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],
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{
"1245": {
"category": "Politician",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agnes Kant lives in The Netherlands whose leader is Mark Rutte.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agnes Kant is from the Netherlands, the leader of which, is Mark Rutte.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Agnes Kant's country is the Netherlands, the leader of which, is Mark Rutte.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mark_Rutte",
"property": "leaderName",
"subject": "Netherlands"
},
{
"object": "Netherlands",
"property": "country",
"subject": "Agnes_Kant"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mark_Rutte",
"property": "leader",
"subject": "Netherlands"
},
{
"object": "Netherlands",
"property": "country",
"subject": "Agnes_Kant"
}
],
[
{
"object": "Mark_Rutte",
"property": "leaderName",
"subject": "Netherlands"
},
{
"object": "Netherlands",
"property": "country",
"subject": "Agnes_Kant"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1245"
}
},
{
"1246": {
"category": "Politician",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albert B White died in Parkersburg, West Virginia, which is part of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albert B White died at Parkersburg, West Virginia, in the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albert B. White died in Parkersburg, West Virginia United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Parkersburg,_West_Virginia"
},
{
"object": "Parkersburg,_West_Virginia",
"property": "deathPlace",
"subject": "Albert_B._White"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Parkersburg,_West_Virginia"
},
{
"object": "Parkersburg,_West_Virginia",
"property": "placeOfDeath",
"subject": "Albert_B._White"
}
],
[
{
"object": "Parkersburg,_West_Virginia",
"property": "deathPlace",
"subject": "Albert_B._White"
},
{
"object": "United_States",
"property": "country",
"subject": "Parkersburg,_West_Virginia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1246"
}
},
{
"1247": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Boogaard was born in Saskatchewan and his club is Wichita Thunder.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Boogaard was born in Saskatchewan and plays for Wichita Thunder.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Boogaard was born in Saskatchewan and played for Wichita Thunder club.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Wichita_Thunder",
"property": "club",
"subject": "Aaron_Boogaard"
},
{
"object": "Saskatchewan",
"property": "birthPlace",
"subject": "Aaron_Boogaard"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Wichita_Thunder",
"property": "team",
"subject": "Aaron_Boogaard"
},
{
"object": "Saskatchewan",
"property": "placeOfBirth",
"subject": "Aaron_Boogaard"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1247"
}
},
{
"1248": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez played for the club Penarol, which was managed by Jorge Orosmán da Silva.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez has played for the club Peñarol which is managed by Jorge Orosmán da Silva.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Peñarol, managed by Jorge Orosmán da Silva, is a former club of the player Abel Hernandez.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peñarol",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "Jorge_Orosmán_da_Silva",
"property": "manager",
"subject": "Peñarol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peñarol",
"property": "team",
"subject": "Abel_Hernández"
},
{
"object": "Jorge_Orosmán_da_Silva",
"property": "manager",
"subject": "Peñarol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1248"
}
},
{
"1249": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez plays for the US Citta de Palermo club and also played for Penarol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez's club is US Citta di Palermo and Penarol.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández has played for the clubs US Città di Palermo and Penarol.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "U.S._Città_di_Palermo",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "Peñarol",
"property": "club",
"subject": "Abel_Hernández"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "U.S._Città_di_Palermo",
"property": "team",
"subject": "Abel_Hernández"
},
{
"object": "Peñarol",
"property": "clubs",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1249"
}
},
{
"1250": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher is connected to the AZ Alkmaar club and plays for the Netherlands National club for players under 17.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AZ Alkmaar player Adam Maher represented the Netherlands national side at Under 17 level.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher is connected to the AZ Alkmaar club and played for the Netherlands national under-17 football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Netherlands_national_under-17_football_team",
"property": "club",
"subject": "Adam_Maher"
},
{
"object": "AZ_Alkmaar",
"property": "club",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Netherlands_national_under-17_football_team",
"property": "team",
"subject": "Adam_Maher"
},
{
"object": "AZ_Alkmaar",
"property": "clubs",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1250"
}
},
{
"1251": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam Maher's club is PSV Eindhoven and he played for the Netherlands national under-17 football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher played for PSV Eindhoven and represented the Netherlands under 17 football team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher, whose club is PSV Eindhoven, plays for Netherlands National under 17 club.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "PSV_Eindhoven",
"property": "club",
"subject": "Adam_Maher"
},
{
"object": "Netherlands_national_under-17_football_team",
"property": "club",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "PSV_Eindhoven",
"property": "clubs",
"subject": "Adam_Maher"
},
{
"object": "Netherlands_national_under-17_football_team",
"property": "team",
"subject": "Adam_Maher"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1251"
}
},
{
"1252": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ahmad Kadhim Assad clubs are Steel Azin FC and Al Shorta SC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ahmad Kadhim Assad played for both Steel Azin F.C. and Al Shorta SC.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Steel_Azin_F.C.",
"property": "club",
"subject": "Ahmad_Kadhim_Assad"
},
{
"object": "Al_Shorta_SC",
"property": "club",
"subject": "Ahmad_Kadhim_Assad"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Steel_Azin_F.C.",
"property": "team",
"subject": "Ahmad_Kadhim_Assad"
},
{
"object": "Al_Shorta_SC",
"property": "clubs",
"subject": "Ahmad_Kadhim_Assad"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1252"
}
},
{
"1253": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams played for Ferencvarosi TC, where the manager is Thomas Doll.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams club is Ferencvarosi TC, where the manager is Thomas Doll.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ferencvarosi TC is managed by Thomas Doll and is the club of Akeem Adams.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ferencvárosi_TC",
"property": "club",
"subject": "Akeem_Adams"
},
{
"object": "Thomas_Doll",
"property": "manager",
"subject": "Ferencvárosi_TC"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ferencvárosi_TC",
"property": "clubs",
"subject": "Akeem_Adams"
},
{
"object": "Thomas_Doll",
"property": "manager",
"subject": "Ferencvárosi_TC"
}
],
[
{
"object": "Thomas_Doll",
"property": "manager",
"subject": "Ferencvárosi_TC"
},
{
"object": "Ferencvárosi_TC",
"property": "team",
"subject": "Akeem_Adams"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1253"
}
},
{
"1254": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams plays for the Trinidad and Tobago National football team and the T&TEC Sports Club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams, who plays for the Trinidad and Tobago national football team formerly played for the T&TEC Sports Club.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Trinidad_and_Tobago_national_football_team",
"property": "club",
"subject": "Akeem_Adams"
},
{
"object": "T&TEC_Sports_Club",
"property": "club",
"subject": "Akeem_Adams"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Trinidad_and_Tobago_national_football_team",
"property": "team",
"subject": "Akeem_Adams"
},
{
"object": "T&TEC_Sports_Club",
"property": "clubs",
"subject": "Akeem_Adams"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1254"
}
},
{
"1255": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers was number 39 in the draft pick and his former team was New England Patriots.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers draft pick is 39 ad his former team is the New England Patriots.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers was number 39 in the draft pick and was a former member of the New England Patriots.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "New_England_Patriots",
"property": "formerTeam",
"subject": "Akeem_Ayers"
},
{
"object": "\"39\"",
"property": "draftPick",
"subject": "Akeem_Ayers"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "New_England_Patriots",
"property": "formerTeam",
"subject": "Akeem_Ayers"
},
{
"object": "\"39\"",
"property": "draftPick",
"subject": "Akeem_Ayers"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1255"
}
},
{
"1256": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers' former team was Tennessee Titans and he was number 39 in the draft pick.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers used to play for Tennessee Titans and he was number 39 in the draft pick.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers played for Tennessee Titans and was 39 in the draft pick.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tennessee_Titans",
"property": "formerTeam",
"subject": "Akeem_Ayers"
},
{
"object": "\"39\"",
"property": "draftPick",
"subject": "Akeem_Ayers"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tennessee_Titans",
"property": "formerTeam",
"subject": "Akeem_Ayers"
},
{
"object": "\"39\"",
"property": "draftPick",
"subject": "Akeem_Ayers"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1256"
}
},
{
"1257": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Dent was born September 27, 1987.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Dent date of birth is the 27th September 1987.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Dent was born on 27 September 1987.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1987",
"property": "birthYear",
"subject": "Akeem_Dent"
},
{
"object": "1987-09-27",
"property": "birthDate",
"subject": "Akeem_Dent"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1987\"^^xsd:gYear",
"property": "birthYear",
"subject": "Akeem_Dent"
},
{
"object": "\"1987-09-27\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Akeem_Dent"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1257"
}
},
{
"1258": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley's club is the Connecticut Huskies and he previously played for Sheikh Russel KC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley plays for the Connecticut Huskies, having previously played for Sheikh Russel KC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley plays for the Connecticut Huskies and formerly played for Sheikh Russel KC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Connecticut_Huskies",
"property": "club",
"subject": "Akeem_Priestley"
},
{
"object": "Sheikh_Russel_KC",
"property": "club",
"subject": "Akeem_Priestley"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Connecticut_Huskies",
"property": "team",
"subject": "Akeem_Priestley"
},
{
"object": "Sheikh_Russel_KC",
"property": "clubs",
"subject": "Akeem_Priestley"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1258"
}
},
{
"1259": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley’s club is RoPS and he played for Sheikh Russel KC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley plays for Sheikh Russel KC and formerly for RoPS.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley played for the clubs RoPS and Sheikh Russel KC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "RoPS",
"property": "club",
"subject": "Akeem_Priestley"
},
{
"object": "Sheikh_Russel_KC",
"property": "club",
"subject": "Akeem_Priestley"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "RoPS",
"property": "team",
"subject": "Akeem_Priestley"
},
{
"object": "Sheikh_Russel_KC",
"property": "clubs",
"subject": "Akeem_Priestley"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1259"
}
},
{
"1260": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra plays for Al-Khor Sports Club located in Al-Khor.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul -Zahra has played for Al-Khor Sports Club, located in Al-Khor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra plays for the Al-Khor Sports Club, located in Al-Khor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Al-Khor_Sports_Club",
"property": "club",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Al_Khor",
"property": "ground",
"subject": "Al-Khor_Sports_Club"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Al-Khor_Sports_Club",
"property": "team",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Al_Khor",
"property": "ground",
"subject": "Al-Khor_Sports_Club"
}
],
[
{
"object": "Al-Khor_Sports_Club",
"property": "clubs",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Al_Khor",
"property": "ground",
"subject": "Al-Khor_Sports_Club"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1260"
}
},
{
"1261": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra's club is AL Kharaitiyat SC managed by Amar Osim.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alaa Abdul Zahra plays for Al Kharaitiyat SC managed by Amar Osim.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Al_Kharaitiyat_SC",
"property": "club",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Amar_Osim",
"property": "manager",
"subject": "Al_Kharaitiyat_SC"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Al_Kharaitiyat_SC",
"property": "clubs",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Amar_Osim",
"property": "manager",
"subject": "Al_Kharaitiyat_SC"
}
],
[
{
"object": "Al_Kharaitiyat_SC",
"property": "team",
"subject": "Alaa_Abdul-Zahra"
},
{
"object": "Amar_Osim",
"property": "manager",
"subject": "Al_Kharaitiyat_SC"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1261"
}
},
{
"1262": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Former clubs of the footballer Alan Martin include Hamilton Academicals FC and Leeds United FC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Martin plays football for Hamilton Academical F.C and Leeds United F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Martin is a footballer who plays for Hamilton Academical and Leeds United football clubs.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hamilton_Academical_F.C.",
"property": "club",
"subject": "Alan_Martin_(footballer)"
},
{
"object": "Leeds_United_F.C.",
"property": "club",
"subject": "Alan_Martin_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hamilton_Academical_F.C.",
"property": "team",
"subject": "Alan_Martin_(footballer,_born_1989)"
},
{
"object": "Leeds_United_F.C.",
"property": "clubs",
"subject": "Alan_Martin_(footballer,_born_1989)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1262"
}
},
{
"1263": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alan Martin once played for Hamilton Academicals FC, which plays their home games at New Douglas Park.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Hamilton Academical FC's ground is New Douglas Park and is the club of Alan Martin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Martin played for Hamilton Academical F.C. at New Douglas Park.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hamilton_Academical_F.C.",
"property": "club",
"subject": "Alan_Martin_(footballer)"
},
{
"object": "New_Douglas_Park",
"property": "ground",
"subject": "Hamilton_Academical_F.C."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hamilton_Academical_F.C.",
"property": "team",
"subject": "Alan_Martin_(footballer,_born_1989)"
},
{
"object": "New_Douglas_Park",
"property": "ground",
"subject": "Hamilton_Academical_F.C."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1263"
}
},
{
"1264": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov was born on 2 September 1995 and plays for the Florida Panthers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr. was born on the 2nd September 1995 and plays for the Florida Panthers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Born on 2nd September 1995, Aleksander Barkov Jr plays for the Florida Panthers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Florida_Panthers",
"property": "club",
"subject": "Aleksander_Barkov,_Jr."
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{
"object": "1995-09-02",
"property": "birthDate",
"subject": "Aleksander_Barkov,_Jr."
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Florida_Panthers",
"property": "team",
"subject": "Aleksander_Barkov,_Jr."
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{
"object": "\"1995-09-02\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Aleksander_Barkov,_Jr."
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1264"
}
},
{
"1265": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr . was born in Tampere, Finland and played with the Florida Panthers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr was born in Tampere Finland and plays for the Florida Panthers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Born in Tampere, Finland, Aleksander Barkov Jr played for the Florida Panthers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Florida_Panthers",
"property": "club",
"subject": "Aleksander_Barkov,_Jr."
},
{
"object": "\"Tampere, Finland\"",
"property": "birthPlace",
"subject": "Aleksander_Barkov,_Jr."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Florida_Panthers",
"property": "team",
"subject": "Aleksander_Barkov,_Jr."
},
{
"object": "\"Tampere, Finland\"@en",
"property": "placeOfBirth",
"subject": "Aleksander_Barkov,_Jr."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1265"
}
},
{
"1266": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov has played for the club FC Torpedo Moscow, which is managed by Valery Petrakov.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "FC Torpedo Moscow, managed by Valery Petrakov, is a former club of Aleksandr Chumakov.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "FC Torpedo Moscow, managed by Valery Petrakov, is the former club of Aleksandr Chumakov.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Torpedo_Moscow",
"property": "club",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valery_Petrakov",
"property": "manager",
"subject": "FC_Torpedo_Moscow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valery_Petrakov",
"property": "manager",
"subject": "FC_Torpedo_Moscow"
},
{
"object": "FC_Torpedo_Moscow",
"property": "team",
"subject": "Aleksandr_Chumakov"
}
],
[
{
"object": "FC_Torpedo_Moscow",
"property": "clubs",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valery_Petrakov",
"property": "manager",
"subject": "FC_Torpedo_Moscow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1266"
}
},
{
"1267": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov's club is FC Spartak Moscow and he also plays for the FC Kuban Krasnodar club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Former clubs of Aleksandr Prudnikov include FC Spartak Moscow and FC Kuban Krasnodar.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov has played for FC Spartak Moscow and FC Kuban Krasnodar.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Spartak_Moscow",
"property": "club",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "FC_Kuban_Krasnodar",
"property": "club",
"subject": "Aleksandr_Prudnikov"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "FC_Spartak_Moscow",
"property": "team",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "FC_Kuban_Krasnodar",
"property": "clubs",
"subject": "Aleksandr_Prudnikov"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1267"
}
},
{
"1268": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudinov plays for FC Tom Tomsk, which plays in the Russian National Football League.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Tom Tomsk which plays in the Russian Football National league.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov is with the FC Tom Tomsk which is in the Russian Football National League.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Tom_Tomsk",
"property": "club",
"subject": "Aleksandr_Prudnikov"
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{
"object": "Russian_Football_National_League",
"property": "league",
"subject": "FC_Tom_Tomsk"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "FC_Tom_Tomsk",
"property": "team",
"subject": "Aleksandr_Prudnikov"
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{
"object": "Russian_Football_National_League",
"property": "league",
"subject": "FC_Tom_Tomsk"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1268"
}
},
{
"1269": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli played for FC Karpaty Lviv and FC Dinamo Batumi.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli's club is FC Karpaty Lviv and he also played for FC Dinamo Batumi.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli plays for FC Karpaty Lviv club and FC Dinamo Batumi.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Karpaty_Lviv",
"property": "club",
"subject": "Aleksandre_Guruli"
},
{
"object": "FC_Dinamo_Batumi",
"property": "club",
"subject": "Aleksandre_Guruli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "FC_Karpaty_Lviv",
"property": "team",
"subject": "Aleksandre_Guruli"
},
{
"object": "FC_Dinamo_Batumi",
"property": "clubs",
"subject": "Aleksandre_Guruli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1269"
}
},
{
"1270": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in 1989 in Manitoba.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in Manitoba in 1989.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1989",
"property": "birthYear",
"subject": "Alex_Plante"
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{
"object": "Manitoba",
"property": "birthPlace",
"subject": "Alex_Plante"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1989\"^^xsd:gYear",
"property": "birthYear",
"subject": "Alex_Plante"
},
{
"object": "Manitoba",
"property": "placeOfBirth",
"subject": "Alex_Plante"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1270"
}
},
{
"1271": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus was born on the 8th January 1988 and plays for the club Pallacanestro Cantu.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Born on January 8th 1988, Alex Tyus plays for the club Pallacanestro Cantu.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus was born on the 8th January 1988 and his club is Pallacanestro Cantu.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pallacanestro_Cantù",
"property": "club",
"subject": "Alex_Tyus"
},
{
"object": "1988-01-08",
"property": "birthDate",
"subject": "Alex_Tyus"
}
],
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"originaltripleset": [
[
{
"object": "Pallacanestro_Cantù",
"property": "team",
"subject": "Alex_Tyus"
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{
"object": "\"1988-01-08\"^^xsd:date",
"property": "dateOfBirth",
"subject": "Alex_Tyus"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1271"
}
},
{
"1272": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays for the club, Maccabi Tel Aviv B.C which is in the Turkish Basketball Super league.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus has played in the Turkish Basketball Super League and has also represented the club Maccabi Tel Aviv BC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays for Maccabi Tel Aviv B.C. in the Turkish Basketball Super League.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
},
{
"object": "Maccabi_Tel_Aviv_B.C.",
"property": "club",
"subject": "Alex_Tyus"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
},
{
"object": "Maccabi_Tel_Aviv_B.C.",
"property": "team",
"subject": "Alex_Tyus"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1272"
}
},
{
"1273": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in Brandon, Manitoba, which is led by the Conservative Party of Canada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Conservative Party of Canada is the leader of Brandon, Manitoba, where Alex Plante was born.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Plante's birth place is Brandon, Manitoba where the leader is the Conservative Party of Canada.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Conservative_Party_of_Canada",
"property": "leaderName",
"subject": "Brandon,_Manitoba"
},
{
"object": "Brandon,_Manitoba",
"property": "birthPlace",
"subject": "Alex_Plante"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Conservative_Party_of_Canada",
"property": "leaderName",
"subject": "Brandon,_Manitoba"
},
{
"object": "Brandon,_Manitoba",
"property": "placeOfBirth",
"subject": "Alex_Plante"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1273"
}
},
{
"1274": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid was born in Canada where the national anthem is O Canada.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid came from Canada whose anthem is O Canada.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Canada is the birthplace of Adam McQuaid and their national anthem is O Canada.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "O_Canada",
"property": "anthem",
"subject": "Canada"
},
{
"object": "Canada",
"property": "birthPlace",
"subject": "Adam_McQuaid"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "O_Canada",
"property": "anthem",
"subject": "Canada"
},
{
"object": "Canada",
"property": "placeOfBirth",
"subject": "Adam_McQuaid"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1274"
}
},
{
"1275": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid was born in Canada where the English language is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The English language is spoken in Canada which is the birthplace of Adam McQuaid.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "Canada"
},
{
"object": "Canada",
"property": "birthPlace",
"subject": "Adam_McQuaid"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "Canada"
},
{
"object": "Canada",
"property": "placeOfBirth",
"subject": "Adam_McQuaid"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1275"
}
},
{
"1276": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli played for FC Dinamo Batumi managed by Levan Khomeriki.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli plays for FC Dinamo Batumi managed by Levan Khomeriki.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli played for FC Dinamo Batumi, managed by Levan Khomeriki.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Levan_Khomeriki",
"property": "manager",
"subject": "FC_Dinamo_Batumi"
},
{
"object": "FC_Dinamo_Batumi",
"property": "club",
"subject": "Aleksandre_Guruli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Levan_Khomeriki",
"property": "manager",
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{
"object": "FC_Dinamo_Batumi",
"property": "clubs",
"subject": "Aleksandre_Guruli"
}
],
[
{
"object": "FC_Dinamo_Batumi",
"property": "team",
"subject": "Aleksandre_Guruli"
},
{
"object": "Levan_Khomeriki",
"property": "manager",
"subject": "FC_Dinamo_Batumi"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1276"
}
},
{
"1277": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov played with the Spartak Moscow FC whose playing ground is the Otkrytiye Arena.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Spartak Moscow at the Otkrytiye Arena.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Otkrytiye_Arena",
"property": "ground",
"subject": "FC_Spartak_Moscow"
},
{
"object": "FC_Spartak_Moscow",
"property": "club",
"subject": "Aleksandr_Prudnikov"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Otkrytiye_Arena",
"property": "ground",
"subject": "FC_Spartak_Moscow"
},
{
"object": "FC_Spartak_Moscow",
"property": "clubs",
"subject": "Aleksandr_Prudnikov"
}
],
[
{
"object": "FC_Spartak_Moscow",
"property": "team",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "Otkrytiye_Arena",
"property": "ground",
"subject": "FC_Spartak_Moscow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1277"
}
},
{
"1278": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley’s club is RoPS which plays in the Veikkausliiga.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley plays for the club RoPS in the Veikkausliiga league.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Priestley played for club RoPS in the Veikkausliiga.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Veikkausliiga",
"property": "league",
"subject": "RoPS"
},
{
"object": "RoPS",
"property": "club",
"subject": "Akeem_Priestley"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Veikkausliiga",
"property": "league",
"subject": "RoPS"
},
{
"object": "RoPS",
"property": "clubs",
"subject": "Akeem_Priestley"
}
],
[
{
"object": "Veikkausliiga",
"property": "league",
"subject": "RoPS"
},
{
"object": "RoPS",
"property": "team",
"subject": "Akeem_Priestley"
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]
]
},
"shape": "NA",
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"xml_id": "Id1278"
}
},
{
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"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams played for United Petrotrin FC whose ground is Palo Seco Velodrome.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams played for United Petrotrin FC, who were playing in Palo Seco Velodrome.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Palo_Seco_Velodrome",
"property": "ground",
"subject": "United_Petrotrin_F.C."
},
{
"object": "United_Petrotrin_F.C.",
"property": "club",
"subject": "Akeem_Adams"
}
],
"originaltriplesets": {
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{
"object": "Palo_Seco_Velodrome",
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"subject": "United_Petrotrin_F.C."
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{
"object": "United_Petrotrin_F.C.",
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"subject": "Akeem_Adams"
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],
[
{
"object": "Palo_Seco_Velodrome",
"property": "ground",
"subject": "United_Petrotrin_F.C."
},
{
"object": "United_Petrotrin_F.C.",
"property": "team",
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]
},
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}
},
{
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Guitarist Aaron Deer plays Psychedelia.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer plays the guitar and performs Psychedelia music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Guitarist Aaron Deer performs music from the Psychedelia genre.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Psychedelia",
"property": "genre",
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{
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],
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{
"object": "Psychedelia",
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id1280"
}
},
{
"1281": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer is from Indiana in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer is from the United States, specifically Indiana.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Deer was born in Indiana in the United states.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
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"subject": "Aaron_Deer"
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{
"object": "Indiana",
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],
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],
[
{
"object": "Indiana",
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{
"object": "United_States",
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"subject": "Aaron_Deer"
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]
},
"shape": "NA",
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"xml_id": "Id1281"
}
},
{
"1282": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner performs electroacoustic music and played with the band Twilight.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner performs electroacoustic music and he played with the band Twilight.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner was with the band Twilight and plays electroacoustic music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Turner"
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{
"object": "Electroacoustic_music",
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"subject": "Aaron_Turner"
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],
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{
"object": "Twilight_(band)",
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{
"object": "Electroacoustic_music",
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"subject": "Aaron_Turner"
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]
]
},
"shape": "NA",
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"xml_id": "Id1282"
}
},
{
"1283": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner sings for a living and uses the musical genre Avant-garde metal.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner is an avant-garde metal music singer.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Singer Aaron Turner plays Avant-Garde Metal music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singing",
"property": "instrument",
"subject": "Aaron_Turner"
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{
"object": "Avant-garde_metal",
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],
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"originaltripleset": [
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{
"object": "Singing",
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{
"object": "Avant-garde_metal",
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"subject": "Aaron_Turner"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1283"
}
},
{
"1284": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abradab, an artist for the band Kaliber 44, is associated with Magik (rapper).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abradab played with the band Kaliber 44 and is associated with the rapper Magik.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abradab is an artist for the band Kaliber 44 and he is associated with the rapper Magik.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Magik_(rapper)",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Abradab"
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{
"object": "Kaliber_44",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Abradab"
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],
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[
{
"object": "Magik_(rapper)",
"property": "associatedBand",
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{
"object": "Kaliber_44",
"property": "associatedMusicalArtist",
"subject": "Abradab"
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],
[
{
"object": "Kaliber_44",
"property": "associatedBand",
"subject": "Abradab"
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{
"object": "Magik_(rapper)",
"property": "associatedMusicalArtist",
"subject": "Abradab"
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]
]
},
"shape": "NA",
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"xml_id": "Id1284"
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},
{
"1285": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abradab was born on November 12, 1978 in Katowice.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abradab was born in Katowice on 12th November 1978.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1978-11-12",
"property": "birthDate",
"subject": "Abradab"
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{
"object": "Katowice",
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"subject": "Abradab"
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],
"originaltriplesets": {
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{
"object": "\"1978-11-12\"^^xsd:date",
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"object": "Katowice",
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{
"object": "Katowice",
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{
"object": "\"1978-11-12\"^^xsd:date",
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]
]
},
"shape": "NA",
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"xml_id": "Id1285"
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},
{
"1286": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Abradab was born in Katowice, Poland.",
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},
{
"comment": "good",
"lang": "",
"lex": "The birth place of Abradab is Poland, in Katowice.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abradab was born is Katowice, Poland.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Katowice",
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"subject": "Abradab"
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],
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{
"object": "Katowice",
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{
"object": "Poland",
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},
"shape": "NA",
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},
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"1287": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
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"comment": "good",
"lang": "",
"lex": "The musical genre of Ace Wilder is hip hop music which has its origins in Disco.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Ace Wilder's musical genre is Hip hop music which has its origins in Disco.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hip_hop_music",
"property": "genre",
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{
"object": "Disco",
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],
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{
"object": "Hip_hop_music",
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[
{
"object": "Hip_hop_music",
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"subject": "Ace_Wilder"
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{
"object": "Disco",
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},
"shape": "NA",
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"xml_id": "Id1287"
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},
{
"1288": {
"category": "Artist",
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{
"comment": "good",
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"lex": "Ace Wilder has a background as a solo singer and is signed to the record label Warner Music Group.",
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},
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"comment": "good",
"lang": "",
"lex": "Ace Wilder is a solo singer who uses the record label Warner Music group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ace Wilder is a solo singer with the record label Warner Music Group.",
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],
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{
"object": "Warner_Music_Group",
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{
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},
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"1289": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Born in Paraguay, Agustin Barboza is signed to Philips Records.",
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{
"comment": "good",
"lang": "",
"lex": "Paraguay born Agustin Barboza is signed to Philips Records.",
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"comment": "good",
"lang": "",
"lex": "Agustin Barboza was born in Paraguay and is signed to Philips Records.",
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],
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{
"object": "Paraguay",
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"subject": "Agustín_Barboza"
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{
"object": "Paraguay",
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},
"shape": "NA",
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},
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"1290": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Alan Frew is a rock musician, which includes fusion and Bhangra styles.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Frew's genre is Rock music of which bhangra is a fusion of rock.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Alan Frews' musical genre is rock music and a type of rock music fusion is Bhangra.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Rock_music",
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"subject": "Alan_Frew"
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"object": "Bhangra_(music)",
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],
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{
"object": "Rock_music",
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{
"object": "Bhangra_(music)",
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]
},
"shape": "NA",
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"xml_id": "Id1290"
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},
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"1291": {
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{
"comment": "good",
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"lex": "Alan Frew plays rock music, which originated from country music.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Frews' musical genre is rock music, which originated from country music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Frew plays rock music which has its roots in country music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rock_music",
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"subject": "Alan_Frew"
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{
"object": "Country_music",
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"subject": "Rock_music"
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],
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"originaltripleset": [
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{
"object": "Rock_music",
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{
"object": "Country_music",
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"subject": "Rock_music"
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],
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{
"object": "Rock_music",
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"subject": "Alan_Frew"
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{
"object": "Country_music",
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]
},
"shape": "NA",
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"xml_id": "Id1291"
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},
{
"1292": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Jazz music originated from blues music. Albennie Jones is a jazz performer.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones plays Jazz, which originated from Blues music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The origins of Jazz is Blues and a jazz artist is Albennie Jones.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jazz",
"property": "genre",
"subject": "Albennie_Jones"
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{
"object": "Blues",
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"subject": "Jazz"
}
],
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{
"object": "Jazz",
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"object": "Blues",
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"subject": "Jazz"
}
],
[
{
"object": "Jazz",
"property": "genre",
"subject": "Albennie_Jones"
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{
"object": "Blues",
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"subject": "Jazz"
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]
]
},
"shape": "NA",
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"xml_id": "Id1292"
}
},
{
"1293": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac has been a solo singer , and a performer in the pop music genre.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "With a background in solo singing, Aleksandra Kovač, belongs to the musical genre of Pop.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač is a solo pop musician singer.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pop_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
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{
"object": "\"solo_singer\"",
"property": "background",
"subject": "Aleksandra_Kovač"
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],
"originaltriplesets": {
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{
"object": "Pop_music",
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"object": "\"solo_singer\"@en",
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"subject": "Aleksandra_Kovač"
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],
[
{
"object": "\"solo_singer\"",
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"subject": "Aleksandra_Kovač"
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{
"object": "Pop_music",
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]
]
},
"shape": "NA",
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"xml_id": "Id1293"
}
},
{
"1294": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac plays rhythm and blues, which originated from blues music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac is an exponent of Rhythm and blues, the music that originated from the blues.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac plays rhythm and blues which originated from blues music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
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{
"object": "Blues",
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"subject": "Rhythm_and_blues"
}
],
"originaltriplesets": {
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[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
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{
"object": "Blues",
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"subject": "Rhythm_and_blues"
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],
[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
},
{
"object": "Blues",
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]
]
},
"shape": "NA",
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"xml_id": "Id1294"
}
},
{
"1295": {
"category": "Artist",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovac stared in 1990 and she performs soul music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
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"lex": "Alex Day performs Synthpop music which has its stylistic origin in new wave music.",
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"lex": "Alex Day plays Synthpop, which is derived from New Wave music.",
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"lex": "Alfred Garth Jones was born in England in 1872.",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
"lang": "",
"lex": "Allen Forrest is a solo singer whose musical genre is hip hop.",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"lex": "Andra performs rhythm and blues music, a style that originated from blues.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andra is a singer of rhythm and blues which originated from the blues.",
"xml_id": "Id3"
}
],
"links": [],
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{
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{
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[
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"comment": "good",
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"lex": "Andrew Rayel, is associated with musical artist Jonathan Mendelsohn and Christian Burns.",
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},
{
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"lang": "",
"lex": "Andrew Rayel is associated with Christian Burns and the musical artist Jonathan Mendelsohn.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel is musically associated with Christian Burns and Jonathan Mendelsohn.",
"xml_id": "Id3"
}
],
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{
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[
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},
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},
{
"1314": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel has worked musically with Jonathan Mendelsohn and Mark Sixma.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel is associated with Jonathan Mendelsohn and Mark Sima.",
"xml_id": "Id3"
}
],
"links": [],
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{
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},
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},
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel is associated with the musician Jwaydan Moyine and the musical artist Armin van Buuren.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel is associated with the musicians Jwaydan Moyine and Armin van Buuren.",
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}
],
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[
{
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{
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},
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"1316": {
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{
"comment": "good",
"lang": "",
"lex": "The musician Andrew White's genre is alternative rock which has origins in New wave music.",
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},
{
"comment": "good",
"lang": "",
"lex": "New wave music is the origin of Alternative rock, which is the genre of Andrew White.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew White is an alternative rock musician . New Wave music was the origin of Alternative rock.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alternative_rock",
"property": "genre",
"subject": "Andrew_White_(musician)"
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{
"object": "New_wave_music",
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],
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"originaltripleset": [
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{
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{
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],
[
{
"object": "Alternative_rock",
"property": "genre",
"subject": "Andrew_White_(musician)"
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{
"object": "New_wave_music",
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},
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}
},
{
"1317": {
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"comment": "good",
"lang": "",
"lex": "Al Anderson of NRBQ is a country musician in which genre the banjo features.",
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},
{
"comment": "good",
"lang": "",
"lex": "Al Anderson of NRBQ performs country music which is a genre of music which uses the banjo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Al Anderson (NRBQ band) performs country music, in which the banjo is one of the instruments.",
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}
],
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"modifiedtripleset": [
{
"object": "Banjo",
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{
"object": "Country_music",
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],
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{
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{
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"subject": "Al_Anderson_(NRBQ)"
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]
},
"shape": "NA",
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"xml_id": "Id1317"
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},
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"comment": "good",
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"lex": "Andrew White records under label Polydor Records, who are based in London.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew White is a musician signed to the record label Polydor Records which is located in London.",
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{
"comment": "good",
"lang": "",
"lex": "Andrew White is a musician signed to Polydor Records which are based in London.",
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}
],
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"modifiedtripleset": [
{
"object": "London",
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"subject": "Polydor_Records"
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{
"object": "Polydor_Records",
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{
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{
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[
{
"object": "London",
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{
"object": "Polydor_Records",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1318"
}
},
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"1319": {
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"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "(19255) 1994 VK8 has an escape velocity of 0.0925 km/s and an apoapsis of 6603633000.0 km.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "(19255) 1994 VK8 has an apoapsis of 6603633000.0 km and an escape velocity of 0.0925 km per sec.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "(19255) 1994 VK8 has an escape velocity of 0.0925 kilometres per second, and an apoapsis of 6603633000.0 km.",
"xml_id": "Id3"
}
],
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{
"object": "6603633000.0 (kilometres)",
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],
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},
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"1320": {
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"comment": "good",
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"lex": "(410777) 2009 FD has an apoapsis of 259776702.47055 kilometres and a minimum temperature of 211.0 kelvins.",
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}
],
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"object": "\"211.0\"^^<http://dbpedia.org/datatype/kelvin>",
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"xml_id": "Id1320"
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},
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"1321": {
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"comment": "good",
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"lex": "(410777) 2009 FD, which was discovered by Spacewatch, has a periapsis of 88234300000.0.",
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}
],
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"modifiedtripleset": [
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"object": "88234300000.0",
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{
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{
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{
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[
{
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{
"object": "\"Spacewatch\"@en",
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},
"shape": "NA",
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"xml_id": "Id1321"
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},
{
"1322": {
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"comment": "good",
"lang": "",
"lex": "(66063) 1998 RO1, with the epoch date 2013-11-04, has an escape velocity of 0.0999 km/s.",
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},
{
"comment": "good",
"lang": "",
"lex": "The escape velocity of (66063) 1998 R01 is 0.0999 km/sec and its epoch is on the 13th of January 2016.",
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},
{
"comment": "good",
"lang": "",
"lex": "The epoch of (66063) 1998 RO1 is on the 13th of January 2016 and it has an escape velocity of 0.0999 kilometres per second.",
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}
],
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"object": "2013-11-04",
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{
"object": "0.0999 (kilometrePerSeconds)",
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],
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{
"object": "\"November 4, 2013\"",
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{
"object": "\"November 4, 2013\"",
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},
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},
{
"1323": {
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"comment": "good",
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"lex": "(66391) 199 KW4 has an epoch date of July 14th 2004 and its orbital period is 16244700.",
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},
{
"comment": "good",
"lang": "",
"lex": "(66391) 1999 KW4 has an orbital period of 16244700.0 and an epoch date of 2004-07.14.",
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},
{
"comment": "good",
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"lex": "The asteroid called (66391) 1999 KW4 has an orbital period of 16244700.0 and an epoch date of 2004/07/14.",
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}
],
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"modifiedtripleset": [
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{
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{
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{
"object": "1.62447e+07",
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"shape": "NA",
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},
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"1324": {
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"comment": "good",
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{
"comment": "good",
"lang": "",
"lex": "The epoch date for 66391 1999 KW4 is 2004-07.14 and it has a rotation period of 9953.28.",
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},
{
"comment": "good",
"lang": "",
"lex": "(66391) 199 KW4 which has an epoch date of July 14th 2004 has a rotation period of 9953.28.",
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}
],
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"object": "2004-07-14",
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]
},
"shape": "NA",
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"xml_id": "Id1324"
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},
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"1325": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson, who died from peritonitis, discovered 101 Helena.",
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},
{
"comment": "good",
"lang": "",
"lex": "James craig watson who discovered 101 Helena died of peritonitis.",
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}
],
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"modifiedtripleset": [
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{
"object": "Peritonitis",
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"subject": "James_Craig_Watson"
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],
"originaltriplesets": {
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{
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},
"shape": "NA",
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"xml_id": "Id1325"
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},
{
"1326": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The apoapsis measurement of 1036 Ganymed is 611961000.0 (kilometres) and it travels at an average speed of 16.86 kilometres per second.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The apoapsis measurement of the celestial boy known as 1036 Ganymed is 611961000.0 (kilometres) and it has an average speed of 16.86 km per sec.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "1036 Ganymed is a celestial body and has an apoapsis of 611961000.0 kilometres and an average speed of 16.86 km per second.",
"xml_id": "Id3"
}
],
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"object": "\"16.86\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
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"object": "\"6.11961E8\"^^<http://dbpedia.org/datatype/kilometre>",
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},
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},
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"comment": "good",
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},
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"comment": "good",
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{
"comment": "good",
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"lex": "110 Lydia, with a mass of 6.7 kilograms, has the epoch date 31st December 2006.",
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{
"comment": "good",
"lang": "",
"lex": "31 December 2006 is the epoch of 110 Lydia, which weighs 6.7 kg.",
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"property": "epoch",
"subject": "110_Lydia"
}
],
[
{
"object": "\"December 31, 2006 (JD2454100.5)\"",
"property": "epoch",
"subject": "110_Lydia"
},
{
"object": "\"6700.0\"^^xsd:double",
"property": "mass",
"subject": "110_Lydia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1340"
}
},
{
"1341": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The epoch date of 110 Lydia is 31 December 2006 and it has an orbital period of 142603000.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 110 Lydia has an orbital period of 142603000 and an epoch date of 31 December 2006.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "110 Lydia has an epoch date of December 31st 2006 and has an orbital period of 142603000.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2006-12-31",
"property": "epoch",
"subject": "110_Lydia"
},
{
"object": "142603000.0",
"property": "orbitalPeriod",
"subject": "110_Lydia"
}
],
"originaltriplesets": {
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{
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"property": "epoch",
"subject": "110_Lydia"
},
{
"object": "1.42603e+08",
"property": "orbitalPeriod",
"subject": "110_Lydia"
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],
[
{
"object": "1.42603e+08",
"property": "orbitalPeriod",
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{
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"property": "epoch",
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]
},
"shape": "NA",
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"xml_id": "Id1341"
}
},
{
"1342": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna is 125800.0 millimetres in length and classed as a cruise ship.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna which is 125.8 metres long, is classed as a Cruise ship.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna cruise ship is 125800.0 millimetres in length.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cruise_ship",
"property": "shipClass",
"subject": "A-Rosa_Luna"
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{
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"subject": "A-Rosa_Luna"
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],
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[
{
"object": "Cruise_ship",
"property": "shipClass",
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{
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"property": "MeanOfTransportation/length",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1342"
}
},
{
"1343": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A Rosa Luna ship is 125800.0 mms in length and was ordered on 22 January 2004.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna ship which is 125800.0 millimetres in length, was ordered on January 22nd 2004.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna ship was ordered on the 22nd January 2004 and it is 125.8 metres long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2004-01-22",
"property": "shipOrdered",
"subject": "A-Rosa_Luna"
},
{
"object": "125800.0 (millimetres)",
"property": "length",
"subject": "A-Rosa_Luna"
}
],
"originaltriplesets": {
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[
{
"object": "\"2004-01-22\"^^xsd:date",
"property": "shipOrdered",
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},
{
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"property": "MeanOfTransportation/length",
"subject": "A-Rosa_Luna"
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],
[
{
"object": "\"125800.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
"subject": "A-Rosa_Luna"
},
{
"object": "\"2004-01-22\"^^xsd:date",
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"subject": "A-Rosa_Luna"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1343"
}
},
{
"1344": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella is operated by AIDA Cruise Line, which is located in Germany.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises is located in Germany and is operated by AIDAstella.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ship AIDAstella is operated by AIDA Cruises which is located in Germany.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germany",
"property": "location",
"subject": "AIDA_Cruises"
},
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Germany",
"property": "location",
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{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
}
],
[
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
},
{
"object": "Germany",
"property": "location",
"subject": "AIDA_Cruises"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1344"
}
},
{
"1345": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna is operated by AIDA Cruises which are located at Rostock.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises, located in Rostock, are the operator of the ship AIDAluna.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDAluna is operated by AIDA Cruises from Rostock.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rostock",
"property": "location",
"subject": "AIDA_Cruises"
},
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAluna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rostock",
"property": "location",
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{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAluna"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1345"
}
},
{
"1346": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna has a top speed is 40.744 and is 252000.0 mms in length.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna has a top speed of 40.744 and it 252 metres long.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna is 252m long and has a top speed of 40.744.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "40.744",
"property": "topSpeed",
"subject": "AIDAluna"
},
{
"object": "252000.0 (millimetres)",
"property": "length",
"subject": "AIDAluna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"40.744\"^^xsd:double",
"property": "topSpeed",
"subject": "AIDAluna"
},
{
"object": "\"252000.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
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]
]
},
"shape": "NA",
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"xml_id": "Id1346"
}
},
{
"1347": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was christened on the 16th of March, 2013 and is 253260.0 millimetres long.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDAstella is 253260.0 millimetres in length and was christened on 2013-03-16.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2013-03-16",
"property": "christeningDate",
"subject": "AIDAstella"
},
{
"object": "253260.0 (millimetres)",
"property": "length",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
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{
"object": "\"--03-16\"^^xsd:gMonthDay",
"property": "shipChristened",
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],
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{
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"property": "MeanOfTransportation/length",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1347"
}
},
{
"1348": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "The Diesel-electric transmission ALCO RS-3 was built by the Montreal Locomotive Works.",
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},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3, has a diesel-electric transmission and was built by the Montreal Locomotive Works.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Montreal_Locomotive_Works",
"property": "builder",
"subject": "ALCO_RS-3"
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{
"object": "Diesel-electric_transmission",
"property": "powerType",
"subject": "ALCO_RS-3"
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],
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"originaltripleset": [
[
{
"object": "Montreal_Locomotive_Works",
"property": "builder",
"subject": "ALCO_RS-3"
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{
"object": "Diesel-electric_transmission",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1348"
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},
{
"1349": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "The ALV X-1 has 2 rocket stages and a diameter of 1.524 metres.",
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},
{
"comment": "good",
"lang": "",
"lex": "The ALV X-1 rocket has 2 stages and a diameter of 1.524 metres.",
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}
],
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{
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"property": "diameter",
"subject": "ALV_X-1"
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{
"object": "2",
"property": "rocketStages",
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}
],
"originaltriplesets": {
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[
{
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{
"object": "\"2\"^^xsd:nonNegativeInteger",
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],
[
{
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{
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]
},
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"xml_id": "Id1349"
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},
{
"1350": {
"category": "MeanOfTransportation",
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"lexicalisations": [
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is available in a 1974 model and was assembled in Kenosha, Wisconsin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is available in a 1974 model and is assembled in Kenosha, Wisconsin.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Kenosha,_Wisconsin",
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],
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{
"object": "Kenosha,_Wisconsin",
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]
]
},
"shape": "NA",
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"xml_id": "Id1350"
}
},
{
"1351": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Richard A. Teague is the Chrysler employee who designed the AMC Matador.",
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},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador was designed by Richard A Teague who workes for Chrysler.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Richard_A._Teague",
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"subject": "AMC_Matador"
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{
"object": "Chrysler",
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],
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[
{
"object": "Richard_A._Teague",
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{
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],
[
{
"object": "Richard_A._Teague",
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{
"object": "Chrysler",
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]
},
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},
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"1352": {
"category": "MeanOfTransportation",
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"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) is 192000.0 millimetres long and comes from Argentina.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) which is derived from Argentina has a length of 192000 millimetres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ARA Veinticinco de Mayo (V-2) is from Argentina and has a length of 192000 millimetres.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "192000.0 (millimetres)",
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},
{
"object": "Argentina",
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],
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]
},
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"xml_id": "Id1352"
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},
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"1353": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, which is assembled in Milan, is a related means of transportation to Saab 9000 , in that they are both cars.",
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},
{
"comment": "good",
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"lex": "The Alfa Romeo 164, assembled in Milan, is a similar means of transport to the Saab 9000.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "As they are both cars, the Saab 9000 and the Alfa Romeo 164 are related means of transport. The latter has an assembly line in Milan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Milan",
"property": "assembly",
"subject": "Alfa_Romeo_164"
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{
"object": "Saab_9000",
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],
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{
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{
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]
]
},
"shape": "NA",
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},
{
"1354": {
"category": "MeanOfTransportation",
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 was assembled in Italy and is a similar means of transport to the, Fiat Croma.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is similar to the Fiat Croma andis made in Italy.",
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}
],
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],
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
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},
{
"1355": {
"category": "MeanOfTransportation",
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Alfa Romeo 164 is a related transport to Lancia Thema and is assembled in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Lancia Thema and the Italian assembled Alfa Romeo 164 are related types of transport.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lancia_Thema",
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},
{
"object": "Italy",
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}
],
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{
"object": "Lancia_Thema",
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1355"
}
},
{
"1356": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alvis Speed 25 was made by the Alvis Car and Engineering Company of Coventry.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25 was made by Coventry based Alvis Car and Engineering Company.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25 was manufactured by the Alvis Car and Engineering Company, which is located in Coventry.",
"xml_id": "Id3"
}
],
"links": [],
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{
"object": "Alvis_Car_and_Engineering_Company",
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{
"object": "Coventry",
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],
"originaltriplesets": {
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[
{
"object": "Alvis_Car_and_Engineering_Company",
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{
"object": "Coventry",
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]
},
"shape": "NA",
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}
},
{
"1357": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "The Alvis Speed 25 has a 4387.00 cc engine and an impressive transmission coined as \"single plate clutch, separate 4-speed gearbox all-silent and all-syncromesh, centre change lever, open tubular propellor shaft with metal joints , spiral bevel fully floating back axle\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25 has an engine of 4387 cubic centimeters and a transmission system comprising a single plate clutch, separate 4-speed gearbox all-silent and all-syncromesh, centre change lever, open tubular propellor shaft with metal joints and spiral bevel fully floating back axle.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25, which has a 4387.0 cubic centimetres engine, has a transmission system that comprises of a single plate clutch, separate 4-speed gearbox all-silent and all-syncromesh, centre change lever, open tubular propellor shaft with metal joints and spiral bevel fully floating back axle.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "\"single plate clutch, separate 4-speed gearbox all-silent and all-syncromesh, centre change lever, open tubular propellor shaft with metal joints , spiral bevel fully floating back axle\"",
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{
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],
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],
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],
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{
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],
[
{
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},
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},
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{
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"xml_id": "Id2"
},
{
"comment": "good",
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"xml_id": "Id3"
}
],
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{
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],
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],
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},
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},
{
"1359": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "American Motors, who made the AMC Matador, was succeeded by Eagle.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is made by American Motors who were succeeded by Eagle.",
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},
{
"comment": "good",
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"lex": "AMC Matador was manufactured by American Motors who were succeeded by Eagle.",
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],
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{
"object": "Eagle_(automobile)",
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],
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{
"object": "Eagle_(automobile)",
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]
]
},
"shape": "NA",
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"xml_id": "Id1359"
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},
{
"1360": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
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"lex": "The Antares rocket is launched from the Mid ~Atlantic Regional Spaceport and its first voyage was on January 9th 2014.",
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},
{
"comment": "good",
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"lex": "The Antares rocket had its maiden flight on January 9th 2014 and was launched from the Mid-Atlantic Regional Spaceport.",
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},
{
"comment": "good",
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"lex": "The rocker Antares was launched on its maiden flight from the Mid-Atlantic Regional Spaceport on 9 January 2014.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014-01-09",
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{
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},
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},
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"1361": {
"category": "MeanOfTransportation",
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"comment": "good",
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},
{
"comment": "good",
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"lex": "The ARA Veinticinco de Mayo (V-2) comes from Argentina which is led by Gabriela Michetti.",
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},
{
"comment": "good",
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"lex": "The ARA Veinticinco de Mayo (V-2) is from Argentina whose leader is Gabriela Michetti.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Gabriela_Michetti",
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{
"object": "Argentina",
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],
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},
{
"1362": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
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"lex": "Ariane 5 was launched at ELA-3 and manufactured by The Airbus Defence and Space.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 which was launched at the ELA-3, was manufactured by Airbus Defence and Space.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Airbus Defence and Space are the manufacturers of the Ariane 5, the launch site of which, was ELA-3 launchpad.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "ELA-3",
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],
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{
"object": "ELA-3",
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],
[
{
"object": "ELA-3",
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},
"shape": "NA",
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},
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"1363": {
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"comment": "good",
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"comment": "good",
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"lex": "The Airbus Group is the parent company of Airbus Defence and Space who manufactured the Ariane 5.",
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"comment": "good",
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"lex": "The Airbus Defence and Space, whose parent company is Airbus Group, made the Ariane 5.",
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],
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"modifiedtripleset": [
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],
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},
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"comment": "good",
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"comment": "good",
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"lex": "Aston Martin V8 was made in the UK. The Aston Martin V8 is connected to the Aston Martin RHAM/1.",
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{
"comment": "good",
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"lex": "The Aston Martin V8 is similar to the RHAM/1 and is assembled in the UK.",
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],
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"modifiedtripleset": [
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"object": "United_Kingdom",
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]
},
"shape": "NA",
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},
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"1365": {
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"comment": "good",
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{
"comment": "good",
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"lex": "The Aston Martin V8 was manufactured by Aston Martin and was succeeded by the Aston Martin Virage.",
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{
"comment": "good",
"lang": "",
"lex": "Aston Martin produced the Aston Martin V8 which was succeeded by the The Aston Martin Virage.",
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],
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"object": "Aston_Martin",
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],
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},
"shape": "NA",
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},
{
"1366": {
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"lex": "The Aston Martin V8 is connected to the Aston Martin DBS and was succeded by the Aston Martin Virage.",
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],
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],
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},
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},
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"comment": "good",
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"comment": "good",
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],
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],
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},
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"1368": {
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"comment": "good",
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"comment": "good",
"lang": "",
"lex": "The final flight of Atlas II, which has a diameter of 3.04m, was on 16 March 1998.",
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"comment": "good",
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"lex": "Atlas II has a diameter of 3.04m and its final flight was on the 16th March 1998.",
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},
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"1369": {
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"comment": "good",
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"lex": "The Lockheed Martin manufactured Atlas II was launched from Cape Canaveral Air Force Station.",
"xml_id": "Id1"
},
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"comment": "good",
"lang": "",
"lex": "The Atlas II, manufactured by Lockheed Martin, was launched from Cape Canaveral Air Force Station.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "The Atlas II was manufactured by Lockheed Martin and launched from the Cape Canaveral Air Force Station.",
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}
],
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"modifiedtripleset": [
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"object": "Cape_Canaveral_Air_Force_Station",
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"object": "Cape_Canaveral_Air_Force_Station",
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},
"shape": "NA",
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},
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"1370": {
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"lexicalisations": [
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"comment": "good",
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"lex": "Audi e-tron is a division of Audi, who make the Audi A1.",
"xml_id": "Id1"
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"comment": "good",
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"lex": "Audi which is the manufacturer of the Audi A1 has a division called the Audi e-tron.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is manufactured by Audi who have a sub-division known as Audi e-tron.",
"xml_id": "Id3"
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],
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"modifiedtripleset": [
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"object": "Audi",
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"object": "Audi_e-tron",
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],
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{
"object": "Audi",
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"object": "Audi_e-tron",
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]
},
"shape": "NA",
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}
},
{
"1371": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Audi A1 is made by Audi who also own Ducati.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is made by Audi who also own Ducati.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ducati is a subsidiary of Audi, which is the manufacturer of the Audi A1.",
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}
],
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{
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],
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{
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]
},
"shape": "NA",
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},
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"1372": {
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{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The Quattro Gmbh is a subsidiary of the Audi who manufacture the Audi A1.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Quattro Gmbh is a subsidiary of Audi, the company that manufacturers the Audi A1.",
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}
],
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{
"object": "Audi",
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{
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],
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{
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{
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]
},
"shape": "NA",
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},
{
"1373": {
"category": "MeanOfTransportation",
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{
"comment": "good",
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},
{
"comment": "good",
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"lex": "The power type of the AIDAluna is an engine manufactured by Caterpillar Inc where Douglas R Oberhelman is a key person.",
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},
{
"comment": "good",
"lang": "",
"lex": "Douglas R. Oberhelman is a key person at Caterpillar Inc. who provide the engine power for the AIDAluna.",
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],
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{
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{
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{
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{
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]
},
"shape": "NA",
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},
{
"1374": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "The Antares rocket is comparable with the Delta II whose launch site was at Vandenberg Air Force Base.",
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"comment": "good",
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"lex": "The rocket Antares is comparable to the Delta II which was launched from the Vandenberg Air Force base.",
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"comment": "good",
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"lex": "The Delta II, which is comparable to the rocket Antares, was launched at the Vandenberg Air Force Base.",
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{
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"shape": "NA",
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},
{
"1375": {
"category": "MeanOfTransportation",
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{
"comment": "good",
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"lex": "The icebreaker Aleksey Chirikov was built in Finland where Juha Sipila is a leader.",
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},
{
"comment": "good",
"lang": "",
"lex": "The icebreaker, the Aleksey Chirikov, is built by Finland, where the leader is Juha Sipila.",
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{
"comment": "good",
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"lex": "Juha Sipilä is a leader in Finland where the icebreaker ship Aleksey Chirikov was built.",
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[
{
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{
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],
[
{
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{
"object": "Finland",
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]
},
"shape": "NA",
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},
{
"1376": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "General Dynamics is the parent company of, the General Dynamics Electric Boat Co. which is the builder of the American submarine NR-1.",
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{
"comment": "good",
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"lex": "General Dynamics is the parent company of the General Dynamics Electric Boat Co which built the American submarine NR-1.",
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{
"comment": "good",
"lang": "",
"lex": "The American submarine NR-1 was built by General Dynamics Electric Boat, the parent company of which, is General Dynamics.",
"xml_id": "Id3"
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[
{
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[
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{
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[
{
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{
"object": "General_Dynamics",
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},
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},
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"comment": "good",
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"lex": "The A-Rosa Luna is powered by the Rolls-Royce Holdings owned, MTU Friedrichshafen, made engines.",
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{
"comment": "good",
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"lex": "The A rosa Luna is powered by Rolls-Royce Holdings company MTU Friedrichshafen made engines.",
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{
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{
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"object": "MTU_Friedrichshafen",
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},
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},
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"comment": "good",
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"lex": "The Aston Martin V8 is assembled in the United Kingdom, the leader of which, is Elizabeth II.",
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"comment": "good",
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"lex": "Aston Martin V8 was assembled in the United Kingdom which is ruled by Queen Elizabeth II.",
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"comment": "good",
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"lex": "The Aston Martin V8 is assembled in the United Kingdom where the ruler is Queen Elizabeth II.",
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{
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},
"shape": "NA",
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"comment": "good",
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"lex": "Purple Heart recipient Aaron S. Daggat was born in Maine and, most notably, fought in the Battle of Fredericksburg.",
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{
"comment": "good",
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"lex": "Born in Maine, Purple Heart recipient, Aaron S Daggett has fought in battles, the most notable being the Battle of Fredericksburg.",
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"comment": "good",
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"lex": "Aaron S Daggett, born in Maine, fought in the Battle of Fredericksburg. He was given the Purple Heart.",
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{
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{
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{
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"comment": "good",
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"lex": "Ab Klink was born in the Netherlands, belongs to the Christian Democratic Appeal party and graduated from Erasmus University in Rotterdam.",
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{
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},
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"comment": "good",
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"lex": "Abdul Taib Mahmud's birthplace was Kingdom of Sarawak and he was succeeded by Abdul Rahman Ya'Kub whose deputy was Stephen Yong Kuet Tze.",
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"comment": "good",
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"lex": "Abdul Taib Mahmud was born in the Kingdom of Sarawak. Stephen Yong Kuet Tze is the deputy to Abdul Rahman Ya'kub who was succeeded by Abdul Taib Mahmud.",
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{
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{
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},
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"lex": "Abdul Taib Mahmud was born and lives in the Kingdom of Sarawak and is a member of the \"Barisan Ra'ayat Jati Sarawak\" party.",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"lex": "Abdulsalami Abubakar's was born in Niger State. He attended the Technical Institute Kaduna and was part of the Chief of the Defence Staff in Nigeria.",
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{
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],
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{
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{
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},
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"comment": "good",
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"lex": "Nigerian Air Force member Abdulsalami Akubar was succeeded by Olusegun Obasanjo who was in office whilst Atiku Akubakar was Vice President.",
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},
{
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{
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},
{
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],
[
{
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{
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{
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"1400": {
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"comment": "good",
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"lex": "Airey Neave participated in World War II and his career lasted from 30 June 1953 to 30 March 1979.",
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"comment": "good",
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},
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"1401": {
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"comment": "good",
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"lex": "Albert B. White was born 22nd of September, 1856 and died on 3rd of July, 1941.",
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"comment": "good",
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"lex": "Albert Jennings Fountain was born in Staten Island in New York City. He passed away in New Mexico Territory.",
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"comment": "good",
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"lex": "Alvah Sabin, who retired on 3rd March 1857, represented Vermont, where the largest city is Burlington.",
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"comment": "good",
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"lex": "Alvah Sabin represented Vermont - largest city; Burlington - and ended his career on March 3rd 1857.",
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"1414": {
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"comment": "good",
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"lex": "Jefferson Davis was a commander in the American civil war which included the Battle of Fredericksburg in which Alfred Moore Scales fought.",
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"comment": "good",
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"comment": "good",
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"lex": "Alfons Gorbach, was born in the County of Tyrol and died in Austria, the leader of which, is Doris Bures.",
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"comment": "good",
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"comment": "good",
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"lex": "Born in Stellendam, Ab Klink is a national of the Netherlands where the Euro is the currency.",
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"comment": "good",
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"lex": "Ab Klink was born in Stellendam and he is a national of the Netherlands. The euro is the currency there.",
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},
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"comment": "good",
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"xml_id": "Id1"
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{
"comment": "good",
"lang": "",
"lex": "The Polish language is used in Poland, where Adam koc was from. Poland has an ethnic group called Kashubians.",
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},
{
"1424": {
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"comment": "good",
"lang": "",
"lex": "19255 1994 VK8, with a mass of 5.6kg, has an apoapsis of 6603633000.0 kilometres and an escape velocity of 0.0925 km/s.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "19255 1994 VK8 has a mass of 5.6kg and an escape velocity of 0.0925 kilometres per second. Its apoapsis is 6603633000.0 km.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "(19255) 1994 VK8 has an escape velocity of 0.0925 km per sec, a mass of 5.6 kgs and an apoapsis of 6603633000.0 km.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "0.0925 (kilometrePerSeconds)",
"property": "escapeVelocity",
"subject": "(19255)_1994_VK8"
},
{
"object": "5.6 (kilograms)",
"property": "mass",
"subject": "(19255)_1994_VK8"
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{
"object": "6603633000.0 (kilometres)",
"property": "apoapsis",
"subject": "(19255)_1994_VK8"
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"object": "\"333.0\"^^xsd:double",
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{
"object": "\"5600.0\"^^xsd:double",
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},
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}
},
{
"1425": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as (410777) 2009 FD has an orbital period of 39447000 , has a periapsis of 88234300000.0 and it was discovered by Spacewatch.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "(410777) 2009 FD, which was discovered by Spacewatch, has an orbital period of 39447000.0 and a periapsis of 88234300000.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Discovered by Spacewatch, (410777) 2009 FD has a periapsis of 88234300000.0, and an orbital period of 39447000.0.",
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}
],
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"property": "orbitalPeriod",
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{
"object": "88234300000.0",
"property": "periapsis",
"subject": "(410777)_2009_FD"
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{
"object": "Spacewatch",
"property": "discoverer",
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{
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{
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{
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},
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"xml_id": "Id1425"
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},
{
"1426": {
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"comment": "good",
"lang": "",
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{
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},
{
"comment": "good",
"lang": "",
"lex": "The minimum temperature of (66063) 1998 RO1 is -71.0 deg celsius and its max temperature is 500.0 (kelvins). It has an apoapsis of 254989570.60815 kilometres.",
"xml_id": "Id3"
}
],
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"property": "minimumTemperature",
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{
"object": "254989570.60815 (kilometres)",
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{
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},
{
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},
{
"object": "\"2.5498957060815E8\"^^<http://dbpedia.org/datatype/kilometre>",
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],
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{
"object": "\"-71.0\"^^<http://dbpedia.org/datatype/degreeCelsius>",
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{
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},
{
"object": "\"2.5498957060815E8\"^^<http://dbpedia.org/datatype/kilometre>",
"property": "Planet/apoapsis",
"subject": "(66063)_1998_RO1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1426"
}
},
{
"1427": {
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"dbpedialinks": [],
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"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Having a density of 2.0 gm per cu cm, (66391) 1999 KW4, has an apoapsis of 162164091.8388 kilometres, an the average speed of 37.16 km per sec.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as (66391) 1999 KW4 has an apoapsis of 162164091.8388 km and an average speed of 37.16 kilometres per second. It's density is 2.0 gm per cu cm.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "37.16 (kilometrePerSeconds)",
"property": "averageSpeed",
"subject": "(66391)_1999_KW4"
},
{
"object": "2.0 (gramPerCubicCentimetres)",
"property": "density",
"subject": "(66391)_1999_KW4"
},
{
"object": "162164091.8388 (kilometres)",
"property": "apoapsis",
"subject": "(66391)_1999_KW4"
}
],
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{
"object": "\"37.16\"^^<http://dbpedia.org/datatype/kilometrePerSecond>",
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"subject": "(66391)_1999_KW4"
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{
"object": "\"2000.0\"^^<http://dbpedia.org/datatype/kilogramPerCubicMetre>",
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]
},
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"shape_type": "NA",
"size": "3",
"xml_id": "Id1427"
}
},
{
"1428": {
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"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 1000 Piazzia has an orbital period of 488160. It has a periapsis of 352497000000.0 and the epoch date of, 2015-06-27.",
"xml_id": "Id1"
}
],
"links": [],
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"property": "periapsis",
"subject": "1000_Piazzia"
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"object": "2015-06-27",
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"object": "\"27 June 2015 (JD2457200.5)\"",
"property": "epoch",
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{
"object": "\"488160.0\"^^xsd:double",
"property": "orbitalPeriod",
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{
"object": "3.52497e+11",
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},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1428"
}
},
{
"1429": {
"category": "CelestialBody",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "The periapsis of the 1001 Gaussia (formally known as 1923 OAA907 XC) is 419113394.55312 km and it has an epoch date of the 31st July 2016.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1001 Gaussia, which was formerly called \"1923 OAA907 XC\", has an epoch date of 31 July 2016 and a periapsis of 419113394.55312 kilometres.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2015-06-27",
"property": "epoch",
"subject": "1001_Gaussia"
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{
"object": "\"1923 OAA907 XC\"",
"property": "formerName",
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{
"object": "419113394.55312 (kilometres)",
"property": "periapsis",
"subject": "1001_Gaussia"
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{
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{
"object": "\"1923 OAA907 XC\"@en",
"property": "formerName",
"subject": "1001_Gaussia"
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{
"object": "4.19113e+11",
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{
"object": "\"1923 OAA907 XC\"@en",
"property": "formerName",
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{
"object": "4.19113e+11",
"property": "periapsis",
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},
{
"object": "\"2015-06-27\"^^xsd:date",
"property": "epoch",
"subject": "1001_Gaussia"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1429"
}
},
{
"1430": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "101 Helena(mass 3.0kgs) has an escape velocity of 0.0348 kilometres per second and its apoapsis is 441092000.0 (kms).",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "101 Helena; has a mass of 3.0 kgs, an escape velocity of 0.0348 kilometres per second, and an apoapsis of 441092000.0 kilometres.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
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"object": "3.0 (kilograms)",
"property": "mass",
"subject": "101_Helena"
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{
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"property": "escapeVelocity",
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{
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},
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"xml_id": "Id1430"
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},
{
"1431": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1036 Ganymed was discovered by Walter Baade, a German National who studied at the University of Gottingen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Walter Baade, a German national who graduated from the University of Gottingen, discovered 1036 Ganymed.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Walter Baade, a German National, discovered 1036 Ganymed. His alma mater was the University of Gottingen.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Walter_Baade",
"property": "discoverer",
"subject": "1036_Ganymed"
},
{
"object": "Germany",
"property": "nationality",
"subject": "Walter_Baade"
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{
"object": "University_of_Göttingen",
"property": "almaMater",
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{
"object": "Germany",
"property": "nationality",
"subject": "Walter_Baade"
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{
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]
},
"shape": "NA",
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"size": "3",
"xml_id": "Id1431"
}
},
{
"1432": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "103 Hera has an epoch date of August 27th 2011 and was discovered by James Craig Watson ,who died in Madison,Wisconsin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson was the discoverer of 103 Hera, the epoch of which is on the 31st of July 2016. He died in Madison, Wisconsin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "103 Hera, which has the epoch of 27 August 2011, was discovered by James Craig Watson, who died in Madison, Wisconsin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "James_Craig_Watson",
"property": "discoverer",
"subject": "103_Hera"
},
{
"object": "2011-08-27",
"property": "epoch",
"subject": "103_Hera"
},
{
"object": "Madison,_Wisconsin",
"property": "deathPlace",
"subject": "James_Craig_Watson"
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],
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{
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{
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"property": "epoch",
"subject": "103_Hera"
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{
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],
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{
"object": "James_Craig_Watson",
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{
"object": "\"August 27, 2011 (JD2455800.5)\"",
"property": "epoch",
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{
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]
},
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"xml_id": "Id1432"
}
},
{
"1433": {
"category": "CelestialBody",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "James Craig Watson, who discovered 103 Hera, died in Madison, Wisconsin of peritonitis.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "103 Hera was discovered by James Craig Watson. He died in Madison, Wisconsin of peritonitis.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
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}
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"comment": "good",
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Weighing 7.5kgs, 109 Felicitas, has an epoch date of December 31st 2006, and its orbital period is, 139705000.0.",
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},
{
"comment": "good",
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},
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},
{
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},
{
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},
{
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{
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{
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},
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}
},
{
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"comment": "good",
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{
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},
{
"comment": "good",
"lang": "",
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},
{
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},
{
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},
{
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"property": "associatedBand",
"subject": "Abradab"
},
{
"object": "Katowice",
"property": "hometown",
"subject": "Abradab"
},
{
"object": "Magik_(rapper)",
"property": "associatedMusicalArtist",
"subject": "Abradab"
}
],
[
{
"object": "Magik_(rapper)",
"property": "associatedBand",
"subject": "Abradab"
},
{
"object": "Kaliber_44",
"property": "associatedMusicalArtist",
"subject": "Abradab"
},
{
"object": "Katowice",
"property": "hometown",
"subject": "Abradab"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1456"
}
},
{
"1457": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Hip hop music, with origins in Disco and Funk, is the genre of musician, Ace Wilder.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Ace Wilder is hip hop music. Hip Hop music originates from Funk and has its stylistic origins in disco.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Hip hop is the genre of musician Ace Wilder. Hip Hop derives from Disco and Funk.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Hip_hop_music",
"property": "genre",
"subject": "Ace_Wilder"
},
{
"object": "Funk",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
},
{
"object": "Disco",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Hip_hop_music",
"property": "genre",
"subject": "Ace_Wilder"
},
{
"object": "Funk",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
},
{
"object": "Disco",
"property": "stylisticOrigins",
"subject": "Hip_hop_music"
}
],
[
{
"object": "Hip_hop_music",
"property": "genre",
"subject": "Ace_Wilder"
},
{
"object": "Funk",
"property": "stylisticOrigins",
"subject": "Hip_hop_music"
},
{
"object": "Disco",
"property": "stylisticOrigin",
"subject": "Hip_hop_music"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1457"
}
},
{
"1458": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agustín Barboza was born in Asunción, Paraguay which is led by Juan Afara.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agustín Barboza was born in Asunción, Paraguay. The leader of Paraguay is Juan Afara.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Asunción",
"property": "birthPlace",
"subject": "Agustín_Barboza"
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{
"object": "Juan_Afara",
"property": "leaderName",
"subject": "Paraguay"
},
{
"object": "Paraguay",
"property": "birthPlace",
"subject": "Agustín_Barboza"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Asunción",
"property": "placeOfBirth",
"subject": "Agustín_Barboza"
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{
"object": "Juan_Afara",
"property": "leaderName",
"subject": "Paraguay"
},
{
"object": "Paraguay",
"property": "birthPlace",
"subject": "Agustín_Barboza"
}
],
[
{
"object": "Paraguay",
"property": "placeOfBirth",
"subject": "Agustín_Barboza"
},
{
"object": "Asunción",
"property": "birthPlace",
"subject": "Agustín_Barboza"
},
{
"object": "Juan_Afara",
"property": "leaderName",
"subject": "Paraguay"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1458"
}
},
{
"1459": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Anderson sings for the band NRBQ and their specialty is Rock music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Al Anderson is a singer who plays with the band NRBQ. His genre is rock music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Singer Al Anderson, plays with and is a member of the band NRBQ, the musical genre of which, is Rock music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "NRBQ",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Al_Anderson_(NRBQ band)"
},
{
"object": "Singing",
"property": "instrument",
"subject": "Al_Anderson_(NRBQ band)"
},
{
"object": "Rock_music",
"property": "genre",
"subject": "Al_Anderson_(NRBQ band)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "NRBQ",
"property": "associatedMusicalArtist",
"subject": "Al_Anderson_(NRBQ)"
},
{
"object": "Singing",
"property": "instrument",
"subject": "Al_Anderson_(NRBQ)"
},
{
"object": "Rock_music",
"property": "genre",
"subject": "Al_Anderson_(NRBQ)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1459"
}
},
{
"1460": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alan Frew's genre is Rock music, which originated from country music. The music Bhangra is part of the fusion genre, partly coming from Rock music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alan Frew is a performer of rock music which originated from country music. A musical fusion of rock music is bhangra music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Bhangra music is part of the fusion genre,partly coming from Rock music which Alan Frew also plays and whose origins are coming from the folk style.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rock_music",
"property": "genre",
"subject": "Alan_Frew"
},
{
"object": "Country_music",
"property": "stylisticOrigin",
"subject": "Rock_music"
},
{
"object": "Bhangra_(music)",
"property": "musicFusionGenre",
"subject": "Rock_music"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rock_music",
"property": "genre",
"subject": "Alan_Frew"
},
{
"object": "Country_music",
"property": "stylisticOrigin",
"subject": "Rock_music"
},
{
"object": "Bhangra_(music)",
"property": "musicFusionGenre",
"subject": "Rock_music"
}
],
[
{
"object": "Rock_music",
"property": "genre",
"subject": "Alan_Frew"
},
{
"object": "Country_music",
"property": "stylisticOrigins",
"subject": "Rock_music"
},
{
"object": "Bhangra_(music)",
"property": "musicFusionGenre",
"subject": "Rock_music"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1460"
}
},
{
"1461": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones plays Jazz music which originated from Blues music. A fusion of Jazz is Afrobeat music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones is a Jazz musician. Jazz comes from Blues music and the Afrobeat is a fusion genre of that music.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones plays jazz which originates from Blues music. Afrobeat music also has elements of Jazz.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jazz",
"property": "genre",
"subject": "Albennie_Jones"
},
{
"object": "Blues",
"property": "stylisticOrigin",
"subject": "Jazz"
},
{
"object": "Afrobeat",
"property": "musicFusionGenre",
"subject": "Jazz"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jazz",
"property": "genre",
"subject": "Albennie_Jones"
},
{
"object": "Blues",
"property": "stylisticOrigin",
"subject": "Jazz"
},
{
"object": "Afrobeat",
"property": "musicFusionGenre",
"subject": "Jazz"
}
],
[
{
"object": "Jazz",
"property": "genre",
"subject": "Albennie_Jones"
},
{
"object": "Blues",
"property": "stylisticOrigins",
"subject": "Jazz"
},
{
"object": "Afrobeat",
"property": "musicFusionGenre",
"subject": "Jazz"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1461"
}
},
{
"1462": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones, born in Errata, Mississippi, is a performer of rhythm and blues, of which disco is a derivative.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones was born in Errata Mississippi and his musical genre is rhythm and blues which is where Disco derives.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones was born in Errata, Mississippi and performs in the rhythm and blues genre. Disco is a derivative of rhythm and blues.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Albennie_Jones"
},
{
"object": "Errata,_Mississippi",
"property": "birthPlace",
"subject": "Albennie_Jones"
},
{
"object": "Disco",
"property": "derivative",
"subject": "Rhythm_and_blues"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Albennie_Jones"
},
{
"object": "Errata,_Mississippi",
"property": "birthPlace",
"subject": "Albennie_Jones"
},
{
"object": "Disco",
"property": "derivative",
"subject": "Rhythm_and_blues"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1462"
}
},
{
"1463": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač plays pop music for the K2 band. He knows musician, Bebi Dol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač is associated with the musical artist Bebi Dol, is part of the band K2 (Kovac sisters duo), and musical genre is Pop.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Pop musician Aleksandra Kovač is associated with the musical artist Bebi Dol and is part of the band K2 Kovac sisters duo.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bebi_Dol",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "K2_(Kovač_sisters_duo)",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Pop_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bebi_Dol",
"property": "associatedBand",
"subject": "Aleksandra_Kovač"
},
{
"object": "K2_(Kovač_sisters_duo)",
"property": "associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Pop_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1463"
}
},
{
"1464": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač, whose musical genre is Pop, is in same band with Kornelije Kovač and is associated with the musical artist Bebi Dol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač, a performer of pop music, is associated with the musical artists Bebi Dol and Kornelije Kovač.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač is associated with the musical artists Bebi Dol and Kornelije Kovac. Her musical genre is Pop.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bebi_Dol",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Kornelije_Kovač",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Pop_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bebi_Dol",
"property": "associatedBand",
"subject": "Aleksandra_Kovač"
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{
"object": "Kornelije_Kovač",
"property": "associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Pop_music",
"property": "genre",
"subject": "Aleksandra_Kovač"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1464"
}
},
{
"1465": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandra Kovač is an artist for the band K2 and has the musical genre of rhythm and blues. She is musically involved with Kristina Kovac.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rhythm and Blues artist Aleksandra Kovač is a member of sister duo, K2, with musician Kristina Kovač.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
},
{
"object": "K2_(Kovač_sisters_duo)",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
},
{
"object": "Kristina_Kovač",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Aleksandra_Kovač"
},
{
"object": "K2_(Kovač_sisters_duo)",
"property": "associatedBand",
"subject": "Aleksandra_Kovač"
},
{
"object": "Kristina_Kovač",
"property": "associatedMusicalArtist",
"subject": "Aleksandra_Kovač"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1465"
}
},
{
"1466": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Alex Day, is Synthpop, which originated from Disco and has its stylistic origins in pop music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Alex Day is Synthpop, a form of pop music which originated from Disco.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Day performs Synthpop music which originated from Disco and stylistically is a type of pop music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Synthpop",
"property": "genre",
"subject": "Alex_Day"
},
{
"object": "Pop_music",
"property": "stylisticOrigin",
"subject": "Synthpop"
},
{
"object": "Disco",
"property": "stylisticOrigin",
"subject": "Synthpop"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Synthpop",
"property": "genre",
"subject": "Alex_Day"
},
{
"object": "Pop_music",
"property": "stylisticOrigin",
"subject": "Synthpop"
},
{
"object": "Disco",
"property": "stylisticOrigins",
"subject": "Synthpop"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1466"
}
},
{
"1467": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in Manchester,England and he passed away in Sidcup.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Born in Manchester, England, Alfred Garth Jones, died in Sidcup.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in Manchester, England and died in Sidcup.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "England",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Manchester",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Sidcup",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Manchester",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Sidcup",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "England",
"property": "placeOfBirth",
"subject": "Alfred_Garth_Jones"
}
],
[
{
"object": "England",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Manchester",
"property": "placeOfBirth",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Sidcup",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1467"
}
},
{
"1468": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in London in the United Kingdom. He died in London.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones, born in England in the U.K., died in London.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in England and died in London.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "London",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "England",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "United_Kingdom",
"property": "nationality",
"subject": "Alfred_Garth_Jones"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "London",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "England",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "United_Kingdom",
"property": "nationality",
"subject": "Alfred_Garth_Jones"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1468"
}
},
{
"1469": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in Manchester in the UK, and died in London.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones, was born in Manchester, in the United Kingdom and died in London.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in Manchester,United Kingdom and he passed away in London.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "London",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
},
{
"object": "United_Kingdom",
"property": "nationality",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Manchester",
"property": "birthPlace",
"subject": "Alfred_Garth_Jones"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "London",
"property": "deathPlace",
"subject": "Alfred_Garth_Jones"
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{
"object": "United_Kingdom",
"property": "nationality",
"subject": "Alfred_Garth_Jones"
},
{
"object": "Manchester",
"property": "birthPlace",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1469"
}
},
{
"1470": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Born in Manchester, Alfred Garth Jones, died in Sidcup. In Manchester The Labour Party leads.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfred Garth Jones was born in Manchester and died in Sidcup. The majority in Manchester is the Labour Party.",
"xml_id": "Id2"
},
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"property": "genre",
"subject": "Andra_(singer)"
},
{
"object": "\"solo_singer\"@en",
"property": "background",
"subject": "Andra_(singer)"
},
{
"object": "Disco",
"property": "derivative",
"subject": "Rhythm_and_blues"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1483"
}
},
{
"1484": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel plays trance music and is associated with Christian Burns and musician, Jonathan Mendelsohn.",
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},
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Andrew Rayel is trance music. He is associated with the musical artists Jonathan Mendelsohn and Christian Burns.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel, whose musical genre is Trance, is associated with the musical artist Jonathan Mendelsohn. The musicians Andrew Rayel and Christian Burns are associates.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "associatedBand/associatedMusicalArtist",
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},
{
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},
{
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}
],
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{
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{
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{
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]
},
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}
},
{
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{
"comment": "good",
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"lex": "Trance musician Andrew Rayel is associated with musical artists Jonathan Mendelsohn and Bobina.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel, of the music genre trance music, is associated with the musical artists Jonathan Mendelsohn and Bobina.",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew Rayel ( a Trance music artiste) is an associate of musical artists Bobina and Jonathan Mendelsohn.",
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}
],
"links": [],
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{
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"property": "associatedBand/associatedMusicalArtist",
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{
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},
{
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}
],
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{
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{
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"property": "associatedMusicalArtist",
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{
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]
]
},
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}
},
{
"1486": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Christian Burns plays house music and is associated with Andrew Rayel. Mr. Rayel is connected with Jwaydan Moyine, musician.",
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},
{
"comment": "good",
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"lex": "Musicians Andrew Rayel and Christian Burns are associates and while the latter plays house music, the former is associated with musician Jwaydan Moyine.",
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}
],
"links": [],
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},
{
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"property": "genre",
"subject": "Christian_Burns"
},
{
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}
],
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{
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},
{
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}
],
[
{
"object": "House_music",
"property": "genre",
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},
{
"object": "Jwaydan_Moyine",
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"subject": "Andrew_Rayel"
},
{
"object": "Christian_Burns",
"property": "associatedMusicalArtist",
"subject": "Andrew_Rayel"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1486"
}
},
{
"1487": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrew White, member of the band Kaiser Chiefs, is also associated with artist Marry Banilow. His record label was Universal Records, which is now defunct.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrew white was once a member of Kaiser Chiefs and is associated with Marry Banilow. His record label is the now defunct Universal Records.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The band Kaiser Chiefs' musician Andrew White is associated with the musical artist Marry Banilow. His record label used to be Universal records which is now a defunct label.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Marry_Banilow",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
},
{
"object": "Kaiser_Chiefs",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
},
{
"object": "Universal_Records_(defunct_record_label)",
"property": "recordLabel",
"subject": "Andrew_White_(musician)"
}
],
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{
"object": "Kaiser_Chiefs",
"property": "associatedBand",
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},
{
"object": "Marry_Banilow",
"property": "associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
},
{
"object": "Universal_Records_(defunct_record_label)",
"property": "recordLabel",
"subject": "Andrew_White_(musician)"
}
],
[
{
"object": "Marry_Banilow",
"property": "associatedBand",
"subject": "Andrew_White_(musician)"
},
{
"object": "Kaiser_Chiefs",
"property": "associatedMusicalArtist",
"subject": "Andrew_White_(musician)"
},
{
"object": "Universal_Records_(defunct_record_label)",
"property": "recordLabel",
"subject": "Andrew_White_(musician)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1487"
}
},
{
"1488": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Bertram, whose musical genre is Ska punk, plays for the Suburban Legends band. Ska punk originated from ska music.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Bertram plays for the Suburban Legends band and performs ska punk music, which has its origins in Ska.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ska punk musician Aaron Bertram is in the band Suburban Legends. Ska punk derives from Ska.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ska",
"property": "stylisticOrigin",
"subject": "Ska_punk"
},
{
"object": "Suburban_Legends",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Bertram"
},
{
"object": "Ska_punk",
"property": "genre",
"subject": "Aaron_Bertram"
}
],
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[
{
"object": "Ska",
"property": "stylisticOrigin",
"subject": "Ska_punk"
},
{
"object": "Suburban_Legends",
"property": "associatedActs",
"subject": "Aaron_Bertram"
},
{
"object": "Ska_punk",
"property": "genre",
"subject": "Aaron_Bertram"
}
],
[
{
"object": "Suburban_Legends",
"property": "associatedMusicalArtist",
"subject": "Aaron_Bertram"
},
{
"object": "Ska",
"property": "stylisticOrigin",
"subject": "Ska_punk"
},
{
"object": "Ska_punk",
"property": "genre",
"subject": "Aaron_Bertram"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1488"
}
},
{
"1489": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones was born in Errata Mississippi in the USA, a country where the African Americans are an ethnic group.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones was born in Errata, Mississippi, United States, where African Americans are one of the ethnic groups.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Albennie Jones was born in Errata, Mississippi, United States where african americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Errata,_Mississippi",
"property": "birthPlace",
"subject": "Albennie_Jones"
},
{
"object": "United_States",
"property": "birthPlace",
"subject": "Albennie_Jones"
}
],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
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{
"object": "United_States",
"property": "placeOfBirth",
"subject": "Albennie_Jones"
},
{
"object": "Errata,_Mississippi",
"property": "birthPlace",
"subject": "Albennie_Jones"
}
],
[
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Errata,_Mississippi",
"property": "placeOfBirth",
"subject": "Albennie_Jones"
},
{
"object": "United_States",
"property": "birthPlace",
"subject": "Albennie_Jones"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1489"
}
},
{
"1490": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge, with an alternative name of Dodge coronet, has a V8 engine and 3-speed transmission.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge, also named the Dodge Coronet, has a 3-speed automatic transmission and a V8 engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge, which has the alternative name of Dodge Coronet, has a V8 engine and uses a 3 speed automatic transmission.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "V8_engine",
"property": "engine",
"subject": "1955_Dodge"
},
{
"object": "\"3-speed automatic\"",
"property": "transmission",
"subject": "1955_Dodge"
},
{
"object": "\"Dodge Coronet\"",
"property": "alternativeName",
"subject": "1955_Dodge"
}
],
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[
{
"object": "1955_Dodge__2",
"property": "engine",
"subject": "1955_Dodge"
},
{
"object": "\"3-speed automatic\"",
"property": "transmission",
"subject": "1955_Dodge"
},
{
"object": "\"Dodge Coronet\"@en",
"property": "aka",
"subject": "1955_Dodge"
}
],
[
{
"object": "1955_Dodge__5",
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"subject": "1955_Dodge"
},
{
"object": "\"3-speed automatic\"",
"property": "transmission",
"subject": "1955_Dodge"
},
{
"object": "\"Dodge Coronet\"@en",
"property": "aka",
"subject": "1955_Dodge"
}
],
[
{
"object": "1955_Dodge__4",
"property": "engine",
"subject": "1955_Dodge"
},
{
"object": "\"3-speed automatic\"",
"property": "transmission",
"subject": "1955_Dodge"
},
{
"object": "\"Dodge Coronet\"@en",
"property": "aka",
"subject": "1955_Dodge"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1490"
}
},
{
"1491": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ordered on January 22nd, 2004, the A-Rosa Luna, is classed as a Cruise ship and has a length of, 125.8 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A-Rosa-Luna, a cruise ship 125800.0 millimetres in length, was ordered on January 22, 2004.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cruise_ship",
"property": "shipClass",
"subject": "A-Rosa_Luna"
},
{
"object": "125800.0 (millimetres)",
"property": "length",
"subject": "A-Rosa_Luna"
},
{
"object": "2004-01-22",
"property": "shipOrdered",
"subject": "A-Rosa_Luna"
}
],
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[
{
"object": "Cruise_ship",
"property": "shipClass",
"subject": "A-Rosa_Luna"
},
{
"object": "\"125800.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
"subject": "A-Rosa_Luna"
},
{
"object": "\"2004-01-22\"^^xsd:date",
"property": "orderDate",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1491"
}
},
{
"1492": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "With a top speed of 24.0, the ship A Rosa Luna is 125800.00 millimetres long and weighs 1850 tonnes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A-Rosa Luna has a ship displacement of 1850 tonnes and a top speed of 24 km/h. Its length is 125.8 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa Luna which is 125.8 metres long has a top speed of 24.0 and weighs 1850 tonnes.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1850.0 (tonnes)",
"property": "shipDisplacement",
"subject": "A-Rosa_Luna"
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{
"object": "24.0",
"property": "topSpeed",
"subject": "A-Rosa_Luna"
},
{
"object": "125800.0 (millimetres)",
"property": "length",
"subject": "A-Rosa_Luna"
}
],
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[
{
"object": "1.85e+09",
"property": "shipDisplacement",
"subject": "A-Rosa_Luna"
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{
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"property": "topSpeed",
"subject": "A-Rosa_Luna"
},
{
"object": "\"125800.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
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}
],
[
{
"object": "\"24.0\"^^xsd:double",
"property": "topSpeed",
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{
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"property": "shipDisplacement",
"subject": "A-Rosa_Luna"
},
{
"object": "\"125800.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1492"
}
},
{
"1493": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The operator of AIDAstella is German baased AIDA Cruises. The AIDAstella is owned by Costa Crociere.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella is owned by Cost Crociere and it's operator is German based AIDA Cruises.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises are based in Germany and owned by Costa Crociere. They operate the AIDAstella.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germany",
"property": "location",
"subject": "AIDA_Cruises"
},
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "owner",
"subject": "AIDAstella"
}
],
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"originaltripleset": [
[
{
"object": "Germany",
"property": "location",
"subject": "AIDA_Cruises"
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{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
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}
],
[
{
"object": "Germany",
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{
"object": "AIDA_Cruises",
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},
{
"object": "Costa_Crociere",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1493"
}
},
{
"1494": {
"category": "MeanOfTransportation",
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{
"comment": "good",
"lang": "",
"lex": "The AIDAstella, 253260.0 millimetres long, was christened on March 16, 2013 and put into service the next day.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was christened on the 16th of March, 2013, put in service on March 17, 2013, and is 253260.0 millimetres in length.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was named on 16th March 2013 and launched on 17th. It is 253.26m long.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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{
"object": "2013-03-17",
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},
{
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],
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id1494"
}
},
{
"1495": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The American Locomotive Company built the ALCO RS-3 which has a 4-stroke engine and is 17068.8 millimeters long.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3 has a four-stroke engine, a length of 17068.8 millimetres and was built by the American Locomotive Company.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3, built by the American Locomotive Company, is 17068.8 mm long with a four-stroke engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "American_Locomotive_Company",
"property": "builder",
"subject": "ALCO_RS-3"
},
{
"object": "Four-stroke_engine",
"property": "engine",
"subject": "ALCO_RS-3"
},
{
"object": "17068.8 (millimetres)",
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"subject": "ALCO_RS-3"
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],
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[
{
"object": "American_Locomotive_Company",
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"subject": "ALCO_RS-3"
},
{
"object": "Four-stroke_engine",
"property": "enginetype",
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},
{
"object": "\"17068.8\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
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]
},
"shape": "NA",
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"size": "3",
"xml_id": "Id1495"
}
},
{
"1496": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3, built by the American Locomotive Company, has a V12 engine and is 17068.8 millimetres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3 was built by the American Locomotive Company. It is 17068.8mm long and has a V12 engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The American Locomotive Company built the ALCO RS-3 which is 17068.8mm long and has a V12 engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "American_Locomotive_Company",
"property": "builder",
"subject": "ALCO_RS-3"
},
{
"object": "V12_engine",
"property": "engine",
"subject": "ALCO_RS-3"
},
{
"object": "17068.8 (millimetres)",
"property": "length",
"subject": "ALCO_RS-3"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "American_Locomotive_Company",
"property": "builder",
"subject": "ALCO_RS-3"
},
{
"object": "V12_engine",
"property": "enginetype",
"subject": "ALCO_RS-3"
},
{
"object": "\"17068.8\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
"subject": "ALCO_RS-3"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1496"
}
},
{
"1497": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The American Locomotive Company was founded in Schenectady, New York and built the ALCO RS-3 which is 17068.8 mms in length.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The American Locomotive Company, founded in Schenectady, New York, were the builders of the ALCO RS-3, which has a length of 17068.8 mm.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The American Locomotive Company, founded in Schenectady, New York, built the 17068.8 millimetre ALCO RS-3.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "American_Locomotive_Company",
"property": "builder",
"subject": "ALCO_RS-3"
},
{
"object": "17068.8 (millimetres)",
"property": "length",
"subject": "ALCO_RS-3"
},
{
"object": "Schenectady,_New_York",
"property": "foundationPlace",
"subject": "American_Locomotive_Company"
}
],
"originaltriplesets": {
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[
{
"object": "American_Locomotive_Company",
"property": "builder",
"subject": "ALCO_RS-3"
},
{
"object": "\"17068.8\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
"subject": "ALCO_RS-3"
},
{
"object": "Schenectady,_New_York",
"property": "foundationPlace",
"subject": "American_Locomotive_Company"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1497"
}
},
{
"1498": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Originating in the United States and by Americans, some of African decent is the ALVX-1.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "ALVX-1 comes from the US where Americans live and African Americans are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The country of origin of the ALV X-1 is the United States, home to Americans and where African Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "countryOrigin",
"subject": "ALV_X-1"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "countryOrigin",
"subject": "ALV_X-1"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1498"
}
},
{
"1499": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States has an ethnic group called Native Americans, the capital is Washington D.C. and the ALV X-1 hails from the US.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ALV X-1 originated in the United States, a country whose capital is Washington DC and has Native Americans as an ethnic group.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "countryOrigin",
"subject": "ALV_X-1"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "countryOrigin",
"subject": "ALV_X-1"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1499"
}
},
{
"1500": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ALV X-1, with 2 rocket stages and one launch, has a 1.524 m diameter.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ALV X-1 rocket which has a diameter of 1.524 meters and has 2 stages was launched one.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ALV X-1 has a diameter of 1.524 m and has 2 rocket stages. It's been launched 1 times.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1.524 (metres)",
"property": "diameter",
"subject": "ALV_X-1"
},
{
"object": "2",
"property": "rocketStages",
"subject": "ALV_X-1"
},
{
"object": "1",
"property": "totalLaunches",
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}
],
"originaltriplesets": {
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[
{
"object": "\"5.0\"^^<http://dbpedia.org/datatype/foot>",
"property": "diameter",
"subject": "ALV_X-1"
},
{
"object": "\"2\"^^xsd:nonNegativeInteger",
"property": "rocketStages",
"subject": "ALV_X-1"
},
{
"object": "\"1\"^^xsd:nonNegativeInteger",
"property": "totalLaunches",
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}
],
[
{
"object": "\"1.524\"^^<http://dbpedia.org/datatype/metre>",
"property": "MeanOfTransportation/diameter",
"subject": "ALV_X-1"
},
{
"object": "\"2\"^^xsd:nonNegativeInteger",
"property": "rocketStages",
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},
{
"object": "\"1\"^^xsd:nonNegativeInteger",
"property": "totalLaunches",
"subject": "ALV_X-1"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1500"
}
},
{
"1501": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupe, with a straight-four engine, had its final production in 1958.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé, with its straight four engine, completed production in 1958.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé has a straight-four engine and a Coupé body style. The last of this model, rolled off the production line in 1958.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1958",
"property": "productionEndYear",
"subject": "Abarth_1000_GT_Coupé"
},
{
"object": "Coupé",
"property": "bodyStyle",
"subject": "Abarth_1000_GT_Coupé"
},
{
"object": "Straight-four_engine",
"property": "engine",
"subject": "Abarth_1000_GT_Coupé"
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],
"originaltriplesets": {
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[
{
"object": "\"1958\"^^xsd:gYear",
"property": "productionEndYear",
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},
{
"object": "Coupé",
"property": "bodyStyle",
"subject": "Abarth_1000_GT_Coupé"
},
{
"object": "Abarth_1000_GT_Coupé__1",
"property": "engine",
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]
]
},
"shape": "NA",
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"xml_id": "Id1501"
}
},
{
"1502": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé is a two door coupé with a wheelbase of 2160 millimetres and a with of 1.55 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 Gt Coupe is a 2 door coupe which is 1.55m wide with a wheelbase of 2160mm.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Abarth 1000 GT Coupé is 1.55 metres wide, has a wheelbase of 2160 millimetres, and the body style is two door coupe.",
"xml_id": "Id3"
}
],
"links": [],
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{
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"property": "wheelbase",
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{
"object": "1.55",
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"subject": "Abarth_1000_GT_Coupé"
},
{
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{
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{
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]
},
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"xml_id": "Id1502"
}
},
{
"1503": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The icebreaker Aleksey Chirikov ship, built in Helsinki at the Arctech Helsinki Shipyard, has a 21.2 ship beam.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The icebreaker Aleksey Chirikov which has a ship beam of 21.2, was built at the Arctech Helsinki Shipyard, in Helsinki.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arctech Helsinki Shipyard built the icebreaker, Aleksey Chirikov with a 21.2 m long shipbeam in Helsinki.",
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}
],
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"modifiedtripleset": [
{
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{
"object": "Helsinki",
"property": "builder",
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},
{
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],
[
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{
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1503"
}
},
{
"1504": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Fiat Croma and the Alfa Romeo 164 are both cars. The latter was built in Italy where the capital is Rome.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfa Romeo 164, asembled in Italy (capital:Rome), is a related mode of transportation as the Fiat Croma.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, assembled in Italy (capital:Rome) is a similar mode of transport as the Fiat Croma.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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{
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},
{
"object": "Fiat_Croma",
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],
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{
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{
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},
{
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],
[
{
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{
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{
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]
]
},
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"xml_id": "Id1504"
}
},
{
"1505": {
"category": "MeanOfTransportation",
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{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, which was assembled in Italy, and the Lancia Thema are related types of transportation. The capital of Italy is Rome.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Lancia Thema and the Alfa Romeo 164 are related. The latter is built in Italy where the capital is Rome.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 was made in Italy and is related to the Lancia Thema vehicle. Rome is the capital of Italy.",
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}
],
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"modifiedtripleset": [
{
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},
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],
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{
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],
[
{
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{
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},
{
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]
},
"shape": "NA",
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"xml_id": "Id1505"
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},
{
"1506": {
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"lex": "The Alfa Romeo 164 was assembled in Arese and is a similar means of transport to the Fiat Croma and also related to the Lancia Thema.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Arese assembled, Alfa Romeo 164 and the Fiat Croma as well as the Lancia Thema, are related types of transportation.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "The Arese assembled Alfa Romeo 164, the Fiat Croma and the Lancia Thema are all related.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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{
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],
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],
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{
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]
},
"shape": "NA",
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"xml_id": "Id1506"
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},
{
"1507": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, made in Milan, along with the Fiat Croma and the Saab 9000 are related cars.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, which was assembled in Milan, is a similar means of transport to the Saab 9000 and the Fiat Croma.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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{
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},
{
"object": "Saab_9000",
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],
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{
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{
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{
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{
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{
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},
{
"object": "Saab_9000",
"property": "relatedMeanOfTransportation",
"subject": "Alfa_Romeo_164"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1507"
}
},
{
"1508": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ship Alhambra, launched on the 31st May 1855, has a ship beam of 8.3m and is 63800.0 millimetres long.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra has a ship beam of 8.3m, is 63800.0 millimetres long, and was launched on 1855-05-31.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra has a ship beam of 8.3m and was 63800.0 millimetres long. It was launched on 31st May 1855.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "8.3 m",
"property": "shipBeam",
"subject": "Alhambra"
},
{
"object": "63800.0 (millimetres)",
"property": "length",
"subject": "Alhambra"
},
{
"object": "1855-05-31",
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],
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{
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{
"object": "\"1855-05-31\"^^xsd:date",
"property": "shipLaunch",
"subject": "Alhambra_(1855)"
}
],
[
{
"object": "\"63800.0\"^^<http://dbpedia.org/datatype/millimetre>",
"property": "MeanOfTransportation/length",
"subject": "Alhambra_(1855)"
},
{
"object": "\"8.3\"^^xsd:double",
"property": "shipBeam",
"subject": "Alhambra_(1855)"
},
{
"object": "\"1855-05-31\"^^xsd:date",
"property": "shipLaunch",
"subject": "Alhambra_(1855)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1508"
}
},
{
"1509": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alhambra, with its 8.3 m ship beam, launched 5/31/1855, was wrecked.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra, which was wrecked, had a ship beam of 8.3 metres and was launched on 31 May 1855.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The wrecked Alhambra had a ship beam of 8.3m and was launched on 31st May 1855.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "8.3 m",
"property": "shipBeam",
"subject": "Alhambra"
},
{
"object": "1855-05-31",
"property": "shipLaunch",
"subject": "Alhambra"
},
{
"object": "\"Wrecked\"",
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"subject": "Alhambra"
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],
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{
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{
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{
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],
[
{
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{
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{
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"subject": "Alhambra_(1855)"
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1509"
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},
{
"1510": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alvis Speed 25 has a petrol engine and was manufactured by the Alvis Car and Engineering Company which was founded in Coventry.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alvis Car and Engineering Company was founded in Coventry and made the Alvis Speed 25. Alvis Speed 25 has a petrol engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alvis Car and Engineering Company was founded in Coventry where the Alvis Speed 25 was manufactured with a petrol engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alvis_Car_and_Engineering_Company",
"property": "manufacturer",
"subject": "Alvis_Speed_25"
},
{
"object": "Coventry",
"property": "foundationPlace",
"subject": "Alvis_Car_and_Engineering_Company"
},
{
"object": "\"Petrol\"",
"property": "engine",
"subject": "Alvis_Speed_25"
}
],
"originaltriplesets": {
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{
"object": "Alvis_Car_and_Engineering_Company",
"property": "manufacturer",
"subject": "Alvis_Speed_25"
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{
"object": "Coventry",
"property": "foundationPlace",
"subject": "Alvis_Car_and_Engineering_Company"
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{
"object": "\"Petrol\"@en",
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]
},
"shape": "NA",
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"xml_id": "Id1510"
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},
{
"1511": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AMC Matador is manufactured by American Motors where Roy D Chapin Jr is a key person and was succeeded ny Eagle.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador is made by American Motors who has a key person called Roy D Chapin. The AMC was succeeded by the Eagle Co.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Roy D Chapin Jr is a key person at American Motors, which was succeeded by Eagle and built the AMC Matador.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Eagle_(automobile)",
"property": "fate",
"subject": "American_Motors"
},
{
"object": "American_Motors",
"property": "manufacturer",
"subject": "AMC_Matador"
},
{
"object": "Roy_D._Chapin,_Jr.",
"property": "keyPerson",
"subject": "American_Motors"
}
],
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[
{
"object": "Eagle_(automobile)",
"property": "fate",
"subject": "American_Motors"
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{
"object": "American_Motors",
"property": "manufacturer",
"subject": "AMC_Matador"
},
{
"object": "Roy_D._Chapin,_Jr.",
"property": "keyPeople",
"subject": "American_Motors"
}
],
[
{
"object": "Eagle_(automobile)",
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"subject": "American_Motors"
},
{
"object": "American_Motors",
"property": "manufacturer",
"subject": "AMC_Matador"
},
{
"object": "Roy_D._Chapin,_Jr.",
"property": "keyPerson",
"subject": "American_Motors"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1511"
}
},
{
"1512": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket, manufactured at the Yuzhnoye Design Office, is comparable to the US Delta II.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket, manufactured at the Yuzhnoye Design Office, is relative to the United States based, Delta II.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Yuzhnoye_Design_Office",
"property": "manufacturer",
"subject": "Antares_(rocket)"
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{
"object": "Delta_II",
"property": "comparable",
"subject": "Antares_(rocket)"
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{
"object": "United_States",
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"subject": "Delta_II"
}
],
"originaltriplesets": {
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{
"object": "Yuzhnoye_Design_Office",
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{
"object": "Delta_II",
"property": "comparable",
"subject": "Antares_(rocket)"
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{
"object": "United_States",
"property": "countryOrigin",
"subject": "Delta_II"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1512"
}
},
{
"1513": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket function is a medium expendable launch system manufactured at the Yuzhnoye Design Office and launched at the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket function is a medium expendable launch system manufactured at the Yuzhnoye Design Office and was launched at the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Yuzhnoye_Design_Office",
"property": "manufacturer",
"subject": "Antares_(rocket)"
},
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
"property": "launchSite",
"subject": "Antares_(rocket)"
},
{
"object": "\"Medium expendable launch system\"",
"property": "function",
"subject": "Antares_(rocket)"
}
],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
"property": "sites",
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},
{
"object": "\"Medium expendable launch system\"@en",
"property": "function",
"subject": "Antares_(rocket)"
},
{
"object": "\"Yuzhnoye Design Bureau\"@en",
"property": "manufacturer",
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],
[
{
"object": "Yuzhnoye_Design_Office",
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},
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
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"subject": "Antares_(rocket)"
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{
"object": "\"Medium expendable launch system\"@en",
"property": "function",
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]
]
},
"shape": "NA",
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"xml_id": "Id1513"
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},
{
"1514": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 was launched at the ELA-3 at the Guiana Space Centre. Arianespace made the Ariane 5.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arianespace is the manufacturer of Ariane 5 which was launched from ELA-3 at the Guiana Space Centre.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 was manufactured by Arianespace and launched at ELA-3 which is located at the Guiana Space Centre.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arianespace",
"property": "manufacturer",
"subject": "Ariane_5"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
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"subject": "Ariane_5"
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],
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"originaltripleset": [
[
{
"object": "Arianespace",
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{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
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{
"object": "ELA-3",
"property": "sites",
"subject": "Ariane_5"
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1514"
}
},
{
"1515": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 is similar to the DBS and has a 5.3 litre engine. It is made in Newport Pagnell, Buckinghamshire, England, UK.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 assembly took place in Newport Pagnell, Buckinghamshire, England, United Kingdom. The Aston Martin V8(has a engine volume of 5.3 litres) and the Aston Martin DBS are related means of transport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 has a 5.3 litre engine and its assembly took place in Newport Pagnell, Buckinghamshire, England, United Kingdom. The Aston Martin V8 and the Aston Martin DBS are related means of transport.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Aston_Martin_DBS",
"property": "relatedMeanOfTransportation",
"subject": "Aston_Martin_V8"
},
{
"object": "\"Newport Pagnell, Buckinghamshire, England, United Kingdom\"",
"property": "assembly",
"subject": "Aston_Martin_V8"
},
{
"object": "5.3 (litres)",
"property": "engine",
"subject": "Aston_Martin_V8"
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],
"originaltriplesets": {
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[
{
"object": "Aston_Martin_DBS",
"property": "related",
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{
"object": "Aston_Martin_V8__1",
"property": "engine",
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},
{
"object": "\"Newport Pagnell, Buckinghamshire, England, United Kingdom\"@en",
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],
[
{
"object": "Aston_Martin_DBS",
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},
{
"object": "\"Newport Pagnell, Buckinghamshire, England, United Kingdom\"@en",
"property": "assembly",
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},
{
"object": "\"5.3\"^^xsd:double",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1515"
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},
{
"1516": {
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"comment": "good",
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"lex": "The Aston Martin V8 is assembled in the United Kingdom and has an engine volume of 5.3 litres. The Aston Martin V8 and Aston Martin DBS are a related means of transport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Aston Martin V8 and the Aston Martin DBS are related means of transport. The V8 model is assembled in the United kingdom with a 5.3 litre engine.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aston Martin V8 was assembled in the United Kingdom and has an engine volume of, 5.3 litres. The Aston Martin V8 and Aston Martin DBS are a related means of transport.",
"xml_id": "Id3"
}
],
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{
"object": "Aston_Martin_DBS",
"property": "relatedMeanOfTransportation",
"subject": "Aston_Martin_V8"
},
{
"object": "5.3 (litres)",
"property": "engine",
"subject": "Aston_Martin_V8"
},
{
"object": "United_Kingdom",
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}
],
"originaltriplesets": {
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{
"object": "Aston_Martin_DBS",
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{
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],
[
{
"object": "Aston_Martin_DBS",
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{
"object": "United_Kingdom",
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]
]
},
"shape": "NA",
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"xml_id": "Id1516"
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},
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"1517": {
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "The US Atlas II was launched from Vandenberg Air Force Base and Cape Canaveral.",
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},
{
"comment": "good",
"lang": "",
"lex": "Originating from the United States, the Atlas II, was launched from both the Vandenberg Air Force Base and Cape Canaveral Air Force Station.",
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}
],
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"modifiedtripleset": [
{
"object": "United_States",
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"object": "Vandenberg_Air_Force_Base",
"property": "launchSite",
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{
"object": "Cape_Canaveral_Air_Force_Station",
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{
"object": "United_States",
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]
},
"shape": "NA",
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},
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"1518": {
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"comment": "good",
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"lex": "The Atlas II comes from the US where Native Americans are an ethnic group and the leader is the President.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Atlas II originated from the United States, where Native Americans are an ethnic group. The leader of the United States has the title President of the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Atlas II originated from the U.S. where the President is the leader and Native Americans are an ethnic group.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "United_States",
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},
{
"object": "Native_Americans_in_the_United_States",
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},
{
"object": "President_of_the_United_States",
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],
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{
"object": "United_States",
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},
{
"object": "President_of_the_United_States",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1518"
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},
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"1519": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Originating from the U.S, the Atlas II was launched from the Spaceport Florida Launch Complex 36, from the Cape Canaveral Air Force Station.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cape_Canaveral_Air_Force_Station",
"property": "launchSite",
"subject": "Atlas_II"
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{
"object": "United_States",
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"subject": "Atlas_II"
},
{
"object": "Spaceport_Florida_Launch_Complex_36",
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"subject": "Atlas_II"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cape_Canaveral_Air_Force_Station",
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},
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"property": "countryOrigin",
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},
{
"object": "Spaceport_Florida_Launch_Complex_36",
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],
[
{
"object": "United_States",
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},
{
"object": "Cape_Canaveral_Air_Force_Station",
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},
{
"object": "Spaceport_Florida_Launch_Complex_36",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1519"
}
},
{
"1520": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is a hatchback assembled by Audi Brussels and has a 1.2 litre engine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1, a hatchback, has a 1.2 liter engine and is assembled by Audi Brussels.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is built at Audi Brussels. It is a hatchback with a 1.2 litre engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1.2 (litres)",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
},
{
"object": "Hatchback",
"property": "bodyStyle",
"subject": "Audi_A1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Audi_A1__3",
"property": "engine",
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},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
},
{
"object": "Hatchback",
"property": "bodyStyle",
"subject": "Audi_A1"
}
],
[
{
"object": "Audi_A1__4",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
},
{
"object": "Hatchback",
"property": "bodyStyle",
"subject": "Audi_A1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1520"
}
},
{
"1521": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is manufactured by Audi. It is a similar means of transport as the Seat Ibiza and Volkswagen Polo since they are all cars.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Audi's A1 is simiilar to the Seat Ibiza and the VW Polo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Audi is the manufacturer of the Audi A1. It, the Seat Ibiza and VW Polo are all cars and therefore related modes of transportation.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Audi",
"property": "manufacturer",
"subject": "Audi_A1"
},
{
"object": "SEAT_Ibiza",
"property": "relatedMeanOfTransportation",
"subject": "Audi_A1"
},
{
"object": "Volkswagen_Polo",
"property": "relatedMeanOfTransportation",
"subject": "Audi_A1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Audi",
"property": "manufacturer",
"subject": "Audi_A1"
},
{
"object": "SEAT_Ibiza",
"property": "relatedMeanOfTransportation",
"subject": "Audi_A1"
},
{
"object": "\"Volkswagen Polo Mk5\"@en",
"property": "related",
"subject": "Audi_A1"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1521"
}
},
{
"1522": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AIDAluna is powered by Caterpillar Inc. which was founded in California and is located in Peoria, Illinois.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc., founded in California and located in Peoria, Illinois, powers the AIDAluna.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAluna is powered by a Caterpillar Inc. engine. Caterpillar Inc was founded in California and is now located in Peoria, Illinois.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "California",
"property": "foundationPlace",
"subject": "Caterpillar_Inc."
},
{
"object": "Peoria,_Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "California",
"property": "foundation",
"subject": "Caterpillar_Inc."
},
{
"object": "Peoria,_Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1522"
}
},
{
"1523": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. of Illinois powers the AIDA Cruise owned, AIDAluna.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. is found in Illinois and they powered the AIDAluna. AIDACruises owns the AIDAluna.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises owns the AIDAluna which is powered by an engine from Illinois based Caterpillar Inc.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "owner",
"subject": "AIDAluna"
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "shipOwner",
"subject": "AIDAluna"
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1523"
}
},
{
"1524": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Costa Crociere is the owner of the AIDAstella and are based in Genoa. The operator of AIDAstella is AIDA Cruises.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ship AIDAstella is operated by AIDA Cruises which is owned by Costa Crociere is in Genoa.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AIDAstella is operated by AIDA Cruises and owned by, Costa Crociere which is located in Genoa.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "owner",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "owner",
"subject": "AIDAstella"
}
],
[
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "shipOwner",
"subject": "AIDAstella"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1524"
}
},
{
"1525": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The origin of the Delta II is the United States and it was launched at the Vandenberg AFB Space Launch Complex 2. The Antares rocket is similar to the Delta II.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The rocket Antares is comparable to the American, Delta II. The launch site of the Delta II was at the Vandenberg AFB Space Launch Complex 2.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Delta II which is similar to the Antares rocket is from the United States and was launched from Vandenberg AFB Space Launch Complex 2.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vandenberg_AFB_Space_Launch_Complex_2",
"property": "launchSite",
"subject": "Delta_II"
},
{
"object": "Delta_II",
"property": "comparable",
"subject": "Antares_(rocket)"
},
{
"object": "United_States",
"property": "countryOrigin",
"subject": "Delta_II"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Vandenberg_AFB_Space_Launch_Complex_2",
"property": "sites",
"subject": "Delta_II"
},
{
"object": "Delta_II",
"property": "comparable",
"subject": "Antares_(rocket)"
},
{
"object": "United_States",
"property": "countryOrigin",
"subject": "Delta_II"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1525"
}
},
{
"1526": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ELA-3 is found at the Guiana Space Centre whose HQ is in French Guiana. One craft launched there was the Ariane 5.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Ariane 5 was launched at ELA-3 at the Guiana Space Centre. The Guiana Space Centre has its headquarters in French Guiana.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "French_Guiana",
"property": "headquarter",
"subject": "Guiana_Space_Centre"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
"property": "launchSite",
"subject": "Ariane_5"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "French_Guiana",
"property": "headquarter",
"subject": "Guiana_Space_Centre"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
"property": "sites",
"subject": "Ariane_5"
}
],
[
{
"object": "French_Guiana",
"property": "headquarters",
"subject": "Guiana_Space_Centre"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
"property": "sites",
"subject": "Ariane_5"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1526"
}
},
{
"1527": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The launch site of the Ariane was ELA-3 launchpad, the site of which, is situated at the Guiana Space Centre. The Guiana Space Centre is located in Kourou,French Guiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The launch site of the Ariane was ELA-3 at the Guiana Space Centre at Kourou, French Guiana.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Guiana Space Centre, in Kourou, French Guiana, is the location of ELA-3, the launchsite of the Ariane 5.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kourou,_French_Guiana",
"property": "location",
"subject": "Guiana_Space_Centre"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
"property": "launchSite",
"subject": "Ariane_5"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Kourou,_French_Guiana",
"property": "location",
"subject": "Guiana_Space_Centre"
},
{
"object": "Guiana_Space_Centre",
"property": "site",
"subject": "ELA-3"
},
{
"object": "ELA-3",
"property": "sites",
"subject": "Ariane_5"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1527"
}
},
{
"1528": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa was built by Neptun Werft which is headquartered in Rostock, Germany.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The builder of the A-Rosa is Neptun Werft, which headquartered in Rostock, Germany.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Neptun Werft is headquartered in Rostock, Germany. They made the A Rosa Luna.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rostock",
"property": "locationCity",
"subject": "Neptun_Werft"
},
{
"object": "Neptun_Werft",
"property": "builder",
"subject": "A-Rosa_Luna"
},
{
"object": "Germany",
"property": "locationCountry",
"subject": "Neptun_Werft"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rostock",
"property": "locationCity",
"subject": "Neptun_Werft"
},
{
"object": "Neptun_Werft",
"property": "builder",
"subject": "A-Rosa_Luna"
},
{
"object": "Germany",
"property": "locationCountry",
"subject": "Neptun_Werft"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1528"
}
},
{
"1529": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Atlas II originated in the United States, where the inhabitants are called Americans and where African Americans are one of the ethnic groups.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Atlas II originated from the U.S, where Americans live and one of the ethnic groups is African Americans.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlas II comes from the US, where Americans live and where African Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "countryOrigin",
"subject": "Atlas_II"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
"property": "demonym",
"subject": "United_States"
},
{
"object": "United_States",
"property": "countryOrigin",
"subject": "Atlas_II"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1529"
}
},
{
"1530": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt plays for Vfl Wolfsburg and SV Werder Bremen. He was a member of the Goslarer SC 08 youth team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt represented the club Goslarer SC 08 at youth level and has since played for both SV Werder Bremen and Vfl Wolfsburg.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Hun's club is VfL Wolfsburg but he previously played for both SV Werder Bremen and Goslarer SC 08 youth team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "VfL_Wolfsburg",
"property": "club",
"subject": "Aaron_Hunt"
},
{
"object": "Goslarer_SC_08",
"property": "youthclub",
"subject": "Aaron_Hunt"
},
{
"object": "SV_Werder_Bremen",
"property": "club",
"subject": "Aaron_Hunt"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "VfL_Wolfsburg",
"property": "team",
"subject": "Aaron_Hunt"
},
{
"object": "Goslarer_SC_08",
"property": "youthclubs",
"subject": "Aaron_Hunt"
},
{
"object": "SV_Werder_Bremen",
"property": "clubs",
"subject": "Aaron_Hunt"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1530"
}
},
{
"1531": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt's club is VfL Wolfsburg who are managed by Dieter Hecking, but he previously played for SV Werder Bremen II.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt plays for the club SV Werder Bremen II and also VfL Wolfsburg which is managed by Dieter Hecking.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "VfL_Wolfsburg",
"property": "club",
"subject": "Aaron_Hunt"
},
{
"object": "Dieter_Hecking",
"property": "manager",
"subject": "VfL_Wolfsburg"
},
{
"object": "SV_Werder_Bremen_II",
"property": "club",
"subject": "Aaron_Hunt"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "VfL_Wolfsburg",
"property": "team",
"subject": "Aaron_Hunt"
},
{
"object": "Dieter_Hecking",
"property": "manager",
"subject": "VfL_Wolfsburg"
},
{
"object": "SV_Werder_Bremen_II",
"property": "clubs",
"subject": "Aaron_Hunt"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1531"
}
},
{
"1532": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández's clubs are Hull City AFC and US Citta di Palermo. He previously played youth football for Central Espanol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez is a member of the Central Espanol youth team and he plays for Hull City AFC but his club is US Citta di Palermo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez's club is US Citta di Palermo. He formerly played youth football for Central Espanol and now plays for Hull City AFC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Central_Español",
"property": "youthclub",
"subject": "Abel_Hernández"
},
{
"object": "Hull_City_A.F.C.",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "club",
"subject": "Abel_Hernández"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Central_Español",
"property": "youthclubs",
"subject": "Abel_Hernández"
},
{
"object": "Hull_City_A.F.C.",
"property": "team",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "clubs",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1532"
}
},
{
"1533": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez played youth football for Central Espanol. He has since been associated with clubs US Citta di Palermo and Penarol.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández played football at youth level for Central Español. At senior level he has played for US Citta di Palermo and Penarol.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández plays football for U.S. Città di Palermo, having previously played for Penarol and at youth level for Central Español.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Central_Español",
"property": "youthclub",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "Peñarol",
"property": "club",
"subject": "Abel_Hernández"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Central_Español",
"property": "youthclubs",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "team",
"subject": "Abel_Hernández"
},
{
"object": "Peñarol",
"property": "clubs",
"subject": "Abel_Hernández"
}
],
[
{
"object": "Central_Español",
"property": "youthclubs",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "clubs",
"subject": "Abel_Hernández"
},
{
"object": "Peñarol",
"property": "team",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1533"
}
},
{
"1534": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Netherlands born Adam Maher's former clubs include PSV Eindhoven and AZ Alkmaar.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher was born in the Netherlands and his clubs are PSV Eindhoven and AZ Alkmaar.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adam Maher was born in the Netherlands and is connected to the AZ Alkmaar club. His current club is PSV Eindhoven.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "PSV_Eindhoven",
"property": "club",
"subject": "Adam_Maher"
},
{
"object": "Netherlands",
"property": "birthPlace",
"subject": "Adam_Maher"
},
{
"object": "AZ_Alkmaar",
"property": "club",
"subject": "Adam_Maher"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "PSV_Eindhoven",
"property": "team",
"subject": "Adam_Maher"
},
{
"object": "Netherlands",
"property": "placeOfBirth",
"subject": "Adam_Maher"
},
{
"object": "AZ_Alkmaar",
"property": "clubs",
"subject": "Adam_Maher"
}
],
[
{
"object": "PSV_Eindhoven",
"property": "clubs",
"subject": "Adam_Maher"
},
{
"object": "Netherlands",
"property": "placeOfBirth",
"subject": "Adam_Maher"
},
{
"object": "AZ_Alkmaar",
"property": "team",
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]
},
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},
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{
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"comment": "good",
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{
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"lex": "Akeem Dent, who was born on 27 September 1987 in Atlanta, Georgia, made his debut with the Atlanta Falcons.",
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{
"comment": "good",
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}
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{
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{
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},
{
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"comment": "good",
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}
],
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{
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{
"object": "Iraq_national_under-20_football_team",
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{
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{
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},
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},
{
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"comment": "good",
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}
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},
{
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{
"comment": "good",
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{
"comment": "good",
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"lex": "Born on 1 January 1989, Alan Martin currently plays for Hamilton Academical FC, having previously played with Clyde FC.",
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{
"comment": "good",
"lang": "",
"lex": "Footballer, Alan Martin, was born on 01/01/1989. He played football for Clyde F.C. and plays for Hamilton Academical FC.",
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],
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{
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{
"object": "1989-01-01",
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{
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{
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{
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},
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},
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{
"comment": "good",
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{
"comment": "good",
"lang": "",
"lex": "Alan Martin's football club is Motherwell FC and he has also played for the Accrington based club Accrington Stanley.",
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{
"object": "Accrington_Stanley_F.C.",
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{
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{
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{
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},
{
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"comment": "good",
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{
"comment": "good",
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"lex": "Born September 2, 1995, Aleksander Barkov Jr of Tampere, Finland, played for the Florida Panthers.",
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{
"comment": "good",
"lang": "",
"lex": "Aleksander Barkov Jr was born on 2 September 1995 in Tampere, Finland and his club is the Florida Panthers.",
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"comment": "good",
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{
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{
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},
{
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"comment": "good",
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{
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},
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},
{
"1546": {
"category": "Athlete",
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{
"comment": "good",
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"lex": "Footballer Aleksandr Prudnikov , (February 24, 1989), plays for FC Kuban Krasnodar and Alania Vladikavkaz.",
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{
"comment": "good",
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"lex": "Born on 24th February 1989, Aleksandr Prudnikov, plays for both, FC Alania Vladikavkaz and FC Kuban Krasnodar club.",
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{
"comment": "good",
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"lex": "Aleksandr Prudnikov who was born on 24 February 1989 plays for FC Kuban Krasnodar and FC Alania Vladikavkaz.",
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],
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"modifiedtripleset": [
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{
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{
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{
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},
{
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{
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"xml_id": "Id2"
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{
"comment": "good",
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},
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"comment": "good",
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"comment": "good",
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"object": "AZAL_PFK",
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{
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},
{
"object": "Parc_Olympique_Lyonnais",
"property": "ground",
"subject": "Olympique_Lyonnais"
},
{
"object": "AZAL_PFK",
"property": "team",
"subject": "Aleksandre_Guruli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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}
},
{
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{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
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"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli is in the US Lesquin Club and also the Olympique Lyonnais who have their home ground at the Parc Olympique Lyonnais stadium.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "club",
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{
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{
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],
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[
{
"object": "Olympique_Lyonnais",
"property": "clubs",
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{
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"subject": "Olympique_Lyonnais"
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{
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"property": "team",
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]
]
},
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}
},
{
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"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays in the club A.C. Milan whose manager is Siniša Mihajlović. He's alos in the Italy national under 16's football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Footballer Alessio Romagnoli, who represented Italy at Under 16 level, plays for C Milan, where the manager is Siniša Mihajlović.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli played for the Italy national under-16 football team. He plays for the club A.C. Milan which is managed by, Siniša Mihajlović.",
"xml_id": "Id3"
}
],
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{
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"property": "club",
"subject": "Alessio_Romagnoli"
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{
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},
{
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}
],
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{
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{
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{
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]
]
},
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}
},
{
"1551": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli, who is connected to UC Sampdoria, plays for A.C. Milan which, is managed by, Siniša Mihajlović.",
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},
{
"comment": "good",
"lang": "",
"lex": "Siniša Mihajlović is the manager for A.C. Milan, of which Alessio Romagnoli is a member. Alessio Romagnoli plays for the club U.C. Sampdoria.",
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}
],
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"property": "club",
"subject": "Alessio_Romagnoli"
},
{
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},
{
"object": "U.C._Sampdoria",
"property": "club",
"subject": "Alessio_Romagnoli"
}
],
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{
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},
{
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"property": "manager",
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},
{
"object": "U.C._Sampdoria",
"property": "team",
"subject": "Alessio_Romagnoli"
}
],
[
{
"object": "A.C._Milan",
"property": "team",
"subject": "Alessio_Romagnoli"
},
{
"object": "Siniša_Mihajlović",
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"subject": "A.C._Milan"
},
{
"object": "\"→ Sampdoria\"@en",
"property": "clubs",
"subject": "Alessio_Romagnoli"
}
]
]
},
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"shape_type": "NA",
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}
},
{
"1552": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli is coached by Daniele Zoratto in the Italy national under 16 football team. He plays for A.S. Roma.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays for A.S. Roma and he use to play for the Italian national under 16 football team, which is coached by Daniele Zoratto.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli plays for A.S. Roma and previously for the Italian national under 16 football team coached by Daniele Zoratto.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy_national_under-16_football_team",
"property": "club",
"subject": "Alessio_Romagnoli"
},
{
"object": "Daniele_Zoratto",
"property": "coach",
"subject": "Italy_national_under-16_football_team"
},
{
"object": "A.S._Roma",
"property": "club",
"subject": "Alessio_Romagnoli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy_national_under-16_football_team",
"property": "nationalteam",
"subject": "Alessio_Romagnoli"
},
{
"object": "Daniele_Zoratto",
"property": "coach",
"subject": "Italy_national_under-16_football_team"
},
{
"object": "A.S._Roma",
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}
]
]
},
"shape": "NA",
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}
},
{
"1553": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Italian born Alessio Romagnoli is a member of the Italian national under 17 football team and plays for the club U.C. Sampdoria.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli played for the club U.C. Sampdoria, and is a member of club Italy national under 17's. He was born in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli who was born in italy plays for U. C. Sampdoria and the Italian national under-17 football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy_national_under-17_football_team",
"property": "club",
"subject": "Alessio_Romagnoli"
},
{
"object": "U.C._Sampdoria",
"property": "club",
"subject": "Alessio_Romagnoli"
},
{
"object": "Italy",
"property": "birthPlace",
"subject": "Alessio_Romagnoli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy_national_under-17_football_team",
"property": "team",
"subject": "Alessio_Romagnoli"
},
{
"object": "\"→ Sampdoria\"@en",
"property": "clubs",
"subject": "Alessio_Romagnoli"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Alessio_Romagnoli"
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1553"
}
},
{
"1554": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in Brandon, Manitoba and is 1.9304 m high. He is in the Anyang Halla club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Plante was born in Brandon, Manitoba and is 1.9304 m tall. He plays for the Anyang Halla club.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Brandon,_Manitoba",
"property": "birthPlace",
"subject": "Alex_Plante"
},
{
"object": "1.9304",
"property": "height",
"subject": "Alex_Plante"
},
{
"object": "Anyang_Halla",
"property": "club",
"subject": "Alex_Plante"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Brandon,_Manitoba",
"property": "placeOfBirth",
"subject": "Alex_Plante"
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{
"object": "\"1.9304\"^^xsd:double",
"property": "height",
"subject": "Alex_Plante"
},
{
"object": "Anyang_Halla",
"property": "team",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1554"
}
},
{
"1555": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1.9304m tall, Alex Plante, was born in Canada and has played for the club Anyang Halla.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Born in Canada, Alex Plante is 1.9304 metres tall and has played for the club Anyang Halla.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Plante's club is Anyang Halla, he is 1.9304m in height and was born in Canada.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1.9304",
"property": "height",
"subject": "Alex_Plante"
},
{
"object": "Anyang_Halla",
"property": "club",
"subject": "Alex_Plante"
},
{
"object": "Canada",
"property": "birthPlace",
"subject": "Alex_Plante"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1.9304\"^^xsd:double",
"property": "height",
"subject": "Alex_Plante"
},
{
"object": "Anyang_Halla",
"property": "team",
"subject": "Alex_Plante"
},
{
"object": "Canada",
"property": "placeOfBirth",
"subject": "Alex_Plante"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1555"
}
},
{
"1556": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays in the Euroleague for the Maccabi Ashdod BC club whose coach is Zvi Sherf.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays for Maccabi Ashdod B.C. in the Euroleague. The coach of the Ashdod team is Zvi Sherf.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays in the Euroleague for Maccabi Ashdod BC whose coach is Zvi Sherf.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Euroleague",
"property": "league",
"subject": "Alex_Tyus"
},
{
"object": "Maccabi_Ashdod_B.C.",
"property": "club",
"subject": "Alex_Tyus"
},
{
"object": "Zvi_Sherf",
"property": "coach",
"subject": "Maccabi_Ashdod_B.C."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Euroleague",
"property": "league",
"subject": "Alex_Tyus"
},
{
"object": "Maccabi_Ashdod_B.C.",
"property": "team",
"subject": "Alex_Tyus"
},
{
"object": "Zvi_Sherf",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1556"
}
},
{
"1557": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The league Alex Tyus plays in is the Turkish Basketball Super League. His club is Maccabi Ashdod BC which is coached by, Zvi Sherf.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus plays for Maccabi Ashdod B.C who are coached by Zvi Sherf and are part of the Turkish Basketball Super League.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
},
{
"object": "Maccabi_Ashdod_B.C.",
"property": "club",
"subject": "Alex_Tyus"
},
{
"object": "Zvi_Sherf",
"property": "coach",
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}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Turkish_Basketball_Super_League",
"property": "league",
"subject": "Alex_Tyus"
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{
"object": "Maccabi_Ashdod_B.C.",
"property": "team",
"subject": "Alex_Tyus"
},
{
"object": "Zvi_Sherf",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1557"
}
},
{
"1558": {
"category": "Athlete",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Tom Tomsk and his club is FC Spartak Moscow, whose grounds are the Otkrytiye Arena.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Tom Tomsk and his club is FC Spartak Moscow, who have their home ground at the Otkrytiye Arena.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Otkrytiye_Arena",
"property": "ground",
"subject": "FC_Spartak_Moscow"
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{
"object": "FC_Tom_Tomsk",
"property": "club",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "FC_Spartak_Moscow",
"property": "club",
"subject": "Aleksandr_Prudnikov"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Otkrytiye_Arena",
"property": "ground",
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{
"object": "FC_Tom_Tomsk",
"property": "team",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "FC_Spartak_Moscow",
"property": "clubs",
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]
]
},
"shape": "NA",
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"xml_id": "Id1558"
}
},
{
"1559": {
"category": "Athlete",
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Otkrytiye Arena is the home ground of FC Spartak Moscow, where Aleksandr Prudnikov played youth football. He later represented FC Anzhi Makhachkala.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Anzhi Makhachkala. His youth football club was FC Spartak Moscow at Otkrytiye Arena.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Otkrytiye_Arena",
"property": "ground",
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{
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"subject": "Aleksandr_Prudnikov"
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{
"object": "FC_Anzhi_Makhachkala",
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"subject": "Aleksandr_Prudnikov"
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],
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{
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{
"object": "FC_Spartak_Moscow",
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"subject": "Aleksandr_Prudnikov"
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{
"object": "\"→ Anzhi Makhachkala\"@en",
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]
]
},
"shape": "NA",
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}
},
{
"1560": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
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"lex": "Aleksandr Chumakov's club was FC Torpedo Moscow. The club, which is managed by Valery Petrakov, played the 2014–15 season in the Russian Premier League.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov plays for FC Torpedo Moscow who are managed by Valery Petrakov and played in the 2014-15 Russian Premier league.",
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},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov's club was FC Torpedo Moscow, the manager of which, is Valery Petrakov. FC Torpedo Moscow played in the 2014-15 Russian Premier League.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Russian_Premier_League",
"property": "season",
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{
"object": "FC_Torpedo_Moscow",
"property": "club",
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{
"object": "Valery_Petrakov",
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],
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{
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{
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],
[
{
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{
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},
{
"object": "FC_Torpedo_Moscow",
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]
]
},
"shape": "NA",
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"xml_id": "Id1560"
}
},
{
"1561": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
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"lex": "Abel Hernandez, who played for the Uruguay Olympic football team, plays for Hull City AFC under their manager Steve Bruce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández plays for the Uruguay Olympic football team and his club is Hull City A.F.C. which is managed by Steve Bruce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández plays for the Uruguay Olympic football team and also for Hull City AFC which is managed by Steve Bruce.",
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}
],
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"modifiedtripleset": [
{
"object": "Steve_Bruce",
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"subject": "Hull_City_A.F.C."
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{
"object": "Hull_City_A.F.C.",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "Uruguay_Olympic_football_team",
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],
"originaltriplesets": {
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{
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{
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"property": "clubs",
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},
{
"object": "Uruguay_Olympic_football_team",
"property": "team",
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]
},
"shape": "NA",
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}
},
{
"1562": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov was born in Moscow, Russia and he died in Russia. The leader of Moscow is Sergey Sobyanin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov was born in Moscow and died in Russia. Sergey Sobyanin is Moscow's leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sergey Sobyanin is the leader of Moscow, which is where Aleksandr Chumakov was born, he died in Russia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sergey_Sobyanin",
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},
{
"object": "Russia",
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"subject": "Aleksandr_Chumakov"
},
{
"object": "Moscow",
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"subject": "Aleksandr_Chumakov"
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],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "Sergey_Sobyanin",
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{
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{
"object": "Moscow",
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]
},
"shape": "NA",
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"xml_id": "Id1562"
}
},
{
"1563": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Footballer, Abner, played for the Brazilian national under 20 football team. He also for the football club Real Madrid Castilla, the manager of which, is Luis Miguel Ramis.",
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},
{
"comment": "good",
"lang": "",
"lex": "Abner plays football for the Brazilian national under 20 football team and also for Real Madrid Castilla who are managed by Luis Miguel Ramis.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abner is a footballer who has represented the Brazil national under 20 team and plays for Real Madrid Castilla under their manager Luis Miguel Ramis.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Luis_Miguel_Ramis",
"property": "manager",
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{
"object": "Brazil_national_under-20_football_team",
"property": "club",
"subject": "Abner_(footballer)"
},
{
"object": "Real_Madrid_Castilla",
"property": "club",
"subject": "Abner_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Luis_Miguel_Ramis",
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{
"object": "Brazil_national_under-20_football_team",
"property": "team",
"subject": "Abner_(footballer)"
},
{
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]
]
},
"shape": "NA",
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"xml_id": "Id1563"
}
},
{
"1564": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
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"lex": "Footballer, Abner plays for the club, Real Madrid Castilla and belongs to C.D. FAS club. Luis Miguel Ramis manages the Real Madrid Castilla.",
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},
{
"comment": "good",
"lang": "",
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}
],
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{
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{
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],
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{
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},
{
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]
]
},
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"xml_id": "Id1564"
}
},
{
"1565": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "St Louis, part of Greater St Louis, is led by Francis G Slay and is the birthplace of Alex Tyus.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus was born in St Louis, (part of greater St. Louis), where Francis G. Slay is the leader.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alex Tyus was born in St Louis, part of Greater St Louis, the leader of which, is Francis G. Slay.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greater_St._Louis",
"property": "isPartOf",
"subject": "St._Louis"
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{
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{
"object": "Francis_G._Slay",
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"subject": "Alex_Tyus"
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{
"object": "Francis_G._Slay",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1565"
}
},
{
"1566": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers's former team is the New England Patriots and his debut team was the Tennessee Titans who are based in the city of Nashville, Tennessee.",
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},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers, whose former team is the New England Patriots, made his debut with the American football team Tennessee Titans based in the city of Nashville.",
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},
{
"comment": "good",
"lang": "",
"lex": "Akeem Ayers' debut team was the Tennessee Titans, who are based in the city of Nashville, Tennessee. He later played for the New England Patriots.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Nashville,_Tennessee",
"property": "city",
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{
"object": "New_England_Patriots",
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{
"object": "Tennessee_Titans",
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],
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{
"object": "Nashville,_Tennessee",
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"object": "New_England_Patriots",
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{
"object": "Tennessee_Titans",
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[
{
"object": "Nashville,_Tennessee",
"property": "city",
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{
"object": "New_England_Patriots",
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{
"object": "Tennessee_Titans",
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]
},
"shape": "NA",
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"xml_id": "Id1566"
}
},
{
"1567": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Akeem Adams, who has represented Trinidad and Tobago at under 20 level, played for United Petrotrin FC, whose ground is Palo Seco.",
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},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams, who plays for the Trinidad and Tobago national under-20 football team previously played for United Petrotin FC whose ground is at Palo Seco.",
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},
{
"comment": "good",
"lang": "",
"lex": "Akeem Adams played for United Petrotrin F.C., who play their home games at Palo Seco ground. He also played for the Trinidad and Tobago national under 20 team.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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{
"object": "Trinidad_and_Tobago_national_under-20_football_team",
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"subject": "Akeem_Adams"
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{
"object": "United_Petrotrin_F.C.",
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"subject": "Akeem_Adams"
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{
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"property": "team",
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{
"object": "United_Petrotrin_F.C.",
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]
},
"shape": "NA",
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"xml_id": "Id1567"
}
},
{
"1568": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Aaron Hunt played for SV Werder Bremen and SV Werder Bremen II. The latter is managed by Alexander Nouri and is in the league 3 Liga.",
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},
{
"comment": "good",
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"lex": "Alexander Nouri is the manager of SV Werder Bremen, which Aaron Hunt plays for. He also plays for, SV Werder Bremen II which is in the league 3 Liga.",
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},
{
"comment": "good",
"lang": "",
"lex": "SV Werder Bremen is managed by Alexander Nouri and SV Werder Bremen II are players in 3 Liga. Aaron Hunt plays for both clubs.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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{
"object": "SV_Werder_Bremen_II",
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{
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},
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"xml_id": "Id1568"
}
},
{
"1569": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez belongs to Abel Hernandezand club Penarol. He also plays for, Steve Bruce managed, Hull City A.F.C.",
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{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez, who played for Penarol, was a member of the Central Espanol youth team. Abel Hernández's club is Hull City A.F.C. which is managed by, Steve Bruce.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez, whose club is Penarol, played youth football for Central Español. his club is also Hull City A.F.C., the manager of which, is Steve Bruce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"property": "club",
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"subject": "Abel_Hernández"
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{
"object": "Steve_Bruce",
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"subject": "Hull_City_A.F.C."
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{
"object": "Hull_City_A.F.C.",
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"subject": "Abel_Hernández"
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],
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{
"object": "Steve_Bruce",
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{
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]
},
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"xml_id": "Id1569"
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},
{
"1570": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Abel Hernández plays for the club, U.S. Città di Palermo and played for the Central Espanol club. He was a member of youth club Peñarol which is managed by, Jorge Orosmán da Silva.",
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},
{
"comment": "good",
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"lex": "Abel Hernández was a member of youth club Peñarol, managed by, Jorge Orosmán da Silva. Abel Hernandezalso played for the Central Espanol club and now his club is US Citta di Palermo.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Abel Hernández has played for Central Español and plays for the club, U.S. Città di Palermo. In his youth, he played for Peñarol, the manager of which, is Jorge Orosmán da Silva.",
"xml_id": "Id3"
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],
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{
"object": "Jorge_Orosmán_da_Silva",
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],
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},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1570"
}
},
{
"1571": {
"category": "Athlete",
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"lexicalisations": [
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"comment": "good",
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"lex": "Abel Hernández, who was a member of youth club Peñaro, plays for the club, U.S. Città di Palermo. The club is managed by Giuseppe Iachini and play in the Serie A league.",
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{
"comment": "good",
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"lex": "When he was younger, Abel Hernández played for Peñarol. He now plays for US Citta di Palermo in the Serie A. League and managed by Giuseppe Iachini.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Abel Hernández plays for, Giuseppe Iachini managed, U.S. Città di Palermo. in the Serie A league. He has played for Peñarol.",
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],
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]
},
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"xml_id": "Id1571"
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},
{
"1572": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid was born October 12th 1986 in Prince Edward Island and now weighs 94.8024kg.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid was born October 12th 1986 on Prince Edward Island. He weighs 94.8024 kilograms.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Born on Oct 12, 1986 in P.E.I. is Adam McQuaid. He weighs 94.8024 Kg.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Prince_Edward_Island",
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{
"object": "1986-10-12",
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{
"object": "94.8024 (kilograms)",
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{
"object": "1986",
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],
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{
"object": "\"1986\"^^xsd:gYear",
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]
},
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"xml_id": "Id1572"
}
},
{
"1573": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adam McQuaid was originally drafted to the Columbus Blue Jackets out of Columbus, Ohio. He nows plays with the Boston Bruins whose general manager is Don Sweeney.",
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},
{
"comment": "good",
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"lex": "Don Sweeney is general manager for the Boston Bruins, the team Adam McQuaid played for. McQuaid was drafted to the Columbus Blue Jackets, from Columbus, Ohio.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Adam McQuaid was drafted with the Columbus Blue Jackets in Columbus, Ohio. He played for the Boston Bruins whose manager is Don Sweeney.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Columbus_Blue_Jackets",
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{
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{
"object": "Columbus,_Ohio",
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{
"object": "Boston_Bruins",
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],
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{
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[
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]
},
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}
},
{
"1574": {
"category": "Athlete",
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"comment": "good",
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},
{
"comment": "good",
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"lex": "Akeem Ayers; was born on 10th July 1989, made his debut for the Tennessee Titans and used to play for the St. Louis Rams.",
"xml_id": "Id2"
},
{
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},
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},
{
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"comment": "good",
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{
"comment": "good",
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"lex": "Alex Plante, who played for Anyang Halla, was born on 1989-05-09, in Canada.",
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{
"comment": "good",
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"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "1097 Vicia, with the epoch date December 31st 2006, has a mass of 98 kgs, an apoapsis of 511592000.0 km and an orbital period of 135589000.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The epoch of 1097 Vicia is on 13 January 2016 and has a mass of 9.8kg. The orbital period for 1097 Vicia is 135589000.0 and its apoapsis is 511592000.0 km.",
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}
],
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"property": "epoch",
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"property": "orbitalPeriod",
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}
},
{
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"lexicalisations": [
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
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"xml_id": "Id2"
},
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"comment": "good",
"lang": "",
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}
],
"links": [],
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"property": "periapsis",
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},
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}
},
{
"1611": {
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"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "10 Hygiea was formerly known as A900 GA and has an epoch date of 27 June 2015. The orbital period is 5.57 days and the periapsis measurement is 416136000000.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The asteroid called 10 Hygiea (formerly A900 GA); has an epoch date of 27th June 2015, a periapsis of 416136000000.0 and an orbital period of 5.57 days.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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},
{
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The celestial body known as 1101 Clematis has the epoch date of 31 July 2016 and an apoapsis of 520906000.0 (kilometres). It has an orbital period of 183309000.0 and an escape velocity of 0.02 kmps.",
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],
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},
{
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},
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},
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],
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},
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}
},
{
"1614": {
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"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1101 Clematis has an epoch date of December 31st 2006. It has an escape velocity of 0.02 kilometres per second, a periapsis of 445895000000, and an orbital period of 183309000.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1101 Clematis's escape velocity is 0.02 km/s, prbital period of 183309000, periapsis of 445895000000, and an epoch on the 31st of December.",
"xml_id": "Id2"
}
],
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"subject": "1101_Clematis"
},
{
"object": "445895000000.0",
"property": "periapsis",
"subject": "1101_Clematis"
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{
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],
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},
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"1615": {
"category": "MeanOfTransportation",
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"lexicalisations": [
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"lex": "The 1955 Dodge is made by the Dodge company and has a V8 engine, a 3-speed automatic transmission and a wheelbase of 120 inches.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge is manufactured by Dodge and it has a V8 engine. It has a wheelbase of 120 inches and has a 3 speed automatic transmission.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Dodge is the manufacturer of the 1955 Dodge which has a V8 engine, a 3 speed automatic transmission and a wheelbase of 120 inches.",
"xml_id": "Id3"
}
],
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"subject": "1955_Dodge"
},
{
"object": "\"3-speed automatic\"",
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{
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},
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"comment": "good",
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"lex": "The 1955 Dodge, the Chrysler Newport and the DeSoto Custom are related means of transportation. DeSoto are the manufacturers of the DeSoto Custom, the successor of which, is the DeSoto Firedome.",
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},
{
"comment": "good",
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"lex": "The 1955 Dodge is related to the Chrysler Newport and the DeSoto Custom which was made by DeSoto and succeeded by the DeSoto Firedome.",
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},
{
"comment": "good",
"lang": "",
"lex": "The 1955 Dodge is related to the Chrysler Newport and the DeSoto Custom. The DeSoto Firedome succeeded the Desoto Custom which is manufactured by DeSoto .",
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}
],
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},
{
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{
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]
},
"shape": "NA",
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"size": "4",
"xml_id": "Id1616"
}
},
{
"1617": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The ship AIDAstella is operated by Rostock based AIDA Cruises. It was built by Meyer Werft and is owned by Costa Crociere.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Located at Rostock, AIDA Cruises operate AIDAstella ship which was built by Meyer Werft and owned by Costa Crociere.",
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},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was built by Meyer Werft . It is owned by Costa Crociere and operated by AIDA Cruise Line based in Rostock.",
"xml_id": "Id3"
}
],
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{
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{
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{
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]
},
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},
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},
{
"comment": "good",
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},
{
"comment": "good",
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}
],
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},
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},
{
"comment": "good",
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},
{
"comment": "good",
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"xml_id": "Id1619"
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},
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"1620": {
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"comment": "good",
"lang": "",
"lex": "The American Locomotive Company built the ALCO RS-3 and it was produced between May 1950 and August 1956. It's length is 17068.8 millimetres and it has a V12 engine.",
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},
{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3 which was built by the American Locomotive Company, was produced between May 1950 and August 1956. It has a V12 engine and is 17068.8 (millimetres) in length.",
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}
],
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},
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"1621": {
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"comment": "good",
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"lex": "The American Locomotive Co. was founded in Schenectady, New York and is located in the US. It built the ALCO RS-3 which is 17068.8 mms in length.",
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},
{
"comment": "good",
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},
{
"comment": "good",
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"lex": "The ALCO RS-3 whose length is 17068.8mm, was built by the American Locomotive Company in Schenectady, New York, US.",
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}
],
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},
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"1622": {
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"comment": "good",
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"lex": "Montreal Locomotive Works built the ALCO RS-3 between May 1950 and Aug. 1956. It has a V12 engine and Diesel-Electric transmission.",
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},
{
"comment": "good",
"lang": "",
"lex": "The ALCO RS-3 was built by the Montreal Locomotive Works and produced between May 1950 and August 1956. It has a V12 engine and a diesel-electric transmission.",
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},
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"comment": "good",
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}
],
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},
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"1623": {
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"comment": "good",
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"lex": "The ALCO RS-3 has 12 cylinders and a four-stroke engine. It was produced by the Montreal Locomotive Works between May 1950 and August 1956.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
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"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
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}
],
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},
{
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{
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},
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},
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"1624": {
"category": "MeanOfTransportation",
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"comment": "good",
"lang": "",
"lex": "The AMC Matador is also known as American Motors Matador and is assembled in Mexico City. It has a station wagon style body and an AMC V8 engine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador which is assembled in Mexico City is also known as the American Motors Matador. It is in the style of a station wagon and has an AMC V8 engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Made in Mexico City, the AMC Matador has an AMC V8 engine and a station wagon body style. It is also referred to as the American Motors Matador.",
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}
],
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{
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},
{
"object": "Station_wagon",
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},
{
"object": "AMC_V8_engine",
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"object": "\"American Motors Matador\"@en",
"property": "aka",
"subject": "AMC_Matador"
},
{
"object": "Mexico_City",
"property": "assembly",
"subject": "AMC_Matador"
},
{
"object": "Station_wagon",
"property": "bodyStyle",
"subject": "AMC_Matador"
},
{
"object": "\"V8\"@en",
"property": "engine",
"subject": "AMC_Matador"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1624"
}
},
{
"1625": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The mid-size AMC Matador, or VAM Classic, has an AMC straight-6 engine and is assembled in Kenosha, Wisconsin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador (alternative name, VAM Classic) was assembled in Kenosha, Wisconsin. It is classed as a mid-size car and it has an AMC straight-6 engine.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AMC Matador which is also known as the VAM Classic was assembled in Kenosha, Wisconsin. It is classed as a mid-size car and has an AMC straight-6 engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"VAM Classic\"",
"property": "alternativeName",
"subject": "AMC_Matador"
},
{
"object": "Kenosha,_Wisconsin",
"property": "assembly",
"subject": "AMC_Matador"
},
{
"object": "Mid-size_car",
"property": "class",
"subject": "AMC_Matador"
},
{
"object": "AMC_straight-6_engine",
"property": "engine",
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],
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[
{
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"property": "aka",
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},
{
"object": "Kenosha,_Wisconsin",
"property": "assembly",
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},
{
"object": "Mid-size_car",
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},
{
"object": "\"I6\"@en",
"property": "engine",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1625"
}
},
{
"1626": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The mid-size Acura TLX car was assembled in Marysville Ohio with a front-wheel/all-wheel drive and a Honda K engine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX is classed as a mid-size car, with a Honda K engine and front-wheel and all-wheel drive. It is assembled in Marysville, Ohio.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Acura TLX is a mid-size car which was assembled in Marysville, Ohio. It has front-wheel and all-wheel drive and a Honda K engine.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "assembly",
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},
{
"object": "Mid-size_car",
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},
{
"object": "\"front-wheel drive / all-wheel drive\"",
"property": "layout",
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},
{
"object": "Honda_K_engine",
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],
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{
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{
"object": "\"front-wheel drive / all-wheel drive\"@en",
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{
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]
},
"shape": "NA",
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"xml_id": "Id1626"
}
},
{
"1627": {
"category": "MeanOfTransportation",
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"comment": "good",
"lang": "",
"lex": "Acura TLX is a luxury class of vehicle with a V6 front engine design. It is made at the Marysville Auto Plant.",
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},
{
"comment": "good",
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"lex": "The Acura TLX is a luxury vehicle with a V6 front engine design. It is assembled at the Marysville Auto Plant.",
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},
{
"comment": "good",
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"lex": "The Acura TLX is a luxury class of vehicle made at the Marysville Auto Plant with a V6 engine and a front engine design.",
"xml_id": "Id3"
}
],
"links": [],
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{
"object": "Marysville_Auto_Plant",
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"subject": "Acura_TLX"
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{
"object": "V6_engine",
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},
{
"object": "Front-engine_design",
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},
{
"object": "Luxury_vehicle",
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],
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{
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{
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},
{
"object": "Front-engine_design",
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1627"
}
},
{
"1628": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The in service icebreaker Aleksey Chirikov was built at the Arctech Helsinki Shipyard. in Finland. It has a ship beam of 21.2 m.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Finnish built Aleksey Chirikov is an icebreaker and currently in service. It was built by the Archtech Helsinki Shipyard and has a ship beam of 21.2m.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arctech Helsinki Shipyard built the icebreaker Aleksey Chirikov in Finland. It has a 21.2 m long shipbeam and is in service.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "builder",
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},
{
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"property": "shipBeam",
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},
{
"object": "\"In service\"",
"property": "status",
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},
{
"object": "Arctech_Helsinki_Shipyard",
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{
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},
{
"object": "Arctech_Helsinki_Shipyard",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1628"
}
},
{
"1629": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The icebreaker ship Aleksey Chirikov was built in Helsinki, Finland. Juha Sipila is the leader of Finland and the people of Finland who are known as Finns.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The icebreaker Aleksey Chirikov was built in Helsinki, Finland. Finland is led by Juha Sipila and the demonym is Finns.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The natives of Finland are known as Finns and are led by Juha Sipila. The icebreaker Aleksey Chirikov was built in the country at Helsinki.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
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},
{
"object": "Finns",
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},
{
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},
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{
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},
{
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[
{
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},
{
"object": "Finland",
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{
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]
},
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}
},
{
"1630": {
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"comment": "good",
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"lex": "The icebreaker Aleksey Chirikov was built at the Arctech Helsinki Shipyard in Helsinki. The Aleksey Chirikov (icebreaker) is in service and its ship beam is 21.2.",
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},
{
"comment": "good",
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"lex": "The icebreaker Aleksey Chirikov was built in Helsinki by the Arctech Helsinki Shipyard It has a 21.2 m long shipbeam and is in service.",
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}
],
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{
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},
{
"object": "Helsinki",
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},
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],
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{
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},
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"xml_id": "Id1630"
}
},
{
"1631": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, which is assembled in Italy and similar to the Saab 9000 and the Lancia Thema, has a straight-four engine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is related to the Lancia Thema and the to the Saab 9000 and in that they are both cars. The Alfa Romeo 164 has a Straight four engine and was assembled in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "The Alfa Romeo 164, assembled in Italy, has a straight four engine and is relative to the Saab 9000 and Lancia Thema.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "assembly",
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},
{
"object": "Saab_9000",
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},
{
"object": "Straight-four_engine",
"property": "engine",
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},
{
"object": "Lancia_Thema",
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],
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{
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},
{
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},
{
"object": "Alfa_Romeo_164__5",
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},
{
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},
"shape": "NA",
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"xml_id": "Id1631"
}
},
{
"1632": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "The Alfa Romeo 164, assembled in Italy, has a V6 engine. It is a related mode of transportation with the Saab 9000 and the Fiat Croma.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Fiat Croma, Saab 9000 and Alfa Romeo 164 are all cars and therefore are related forms of transportation. The Alfa Romeo 164, has a V6 engine and its assembly line is in Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is similar to the Saab 9000 and related to the Fiat Croma. The Alfa Romeo 164's assembly line is in Italy and it has a V6 engine.",
"xml_id": "Id3"
}
],
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{
"object": "Fiat_Croma",
"property": "relatedMeanOfTransportation",
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},
{
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},
{
"object": "Saab_9000",
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},
{
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],
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},
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}
},
{
"1633": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "The Alhambra is 63800.0 millimetres long and its ship beam is 8.3m. It was launched on 1855-05-31and had wrecked.",
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},
{
"comment": "good",
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"lex": "The Alhambra has a ship beam of 8.3m and is 63800.0 millimetres long. It was launched on 1855-05-31 and had wrecked.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alhambra, which was wrecked, was launched on 31 May 1855 with a ship beam of 8.3 metres and a length of 63800.0 millimetres.",
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}
],
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"modifiedtripleset": [
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},
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},
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},
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"1634": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "The Alvis Car and Engineering Company was founded in Coventry and was succeeded by the Rover Company. The Alvis Speed 25 has a petrol engine and was manufactured by the Alvis Car and Engineering Company.",
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},
{
"comment": "good",
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},
{
"comment": "good",
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}
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},
{
"object": "\"Petrol\"",
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},
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},
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"xml_id": "Id1634"
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},
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"1635": {
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"comment": "good",
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},
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"xml_id": "Id2"
},
{
"comment": "good",
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"xml_id": "Id3"
}
],
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},
{
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},
{
"object": "Straight-six_engine",
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},
{
"object": "Rover_Company",
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}
],
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{
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},
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},
{
"object": "Alvis_Speed_25__3",
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},
{
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],
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{
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},
{
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{
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},
{
"1645": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Classed as a supermini, the Audi A1 (assembled at Audi Brussels), has a 1.2 litre front-engine with front-wheel-drive.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Audi A1 belongs to the supermini class and is assembled at Audi Brussels. It has a 1.2 litre engine and a front engine, front-wheel drive layout .",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Audi A1 is assembled at Audi Brussels and is classed as a super mini. It has a 1.2 litre engine and a front engine, front-wheel drive layout.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Supermini",
"property": "class",
"subject": "Audi_A1"
},
{
"object": "1.2 (litres)",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Front-engine,_front-wheel-drive_layout",
"property": "layout",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Supermini",
"property": "class",
"subject": "Audi_A1"
},
{
"object": "Audi_A1__3",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Front-engine,_front-wheel-drive_layout",
"property": "layout",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
}
],
[
{
"object": "Audi_A1__2",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Front-engine,_front-wheel-drive_layout",
"property": "layout",
"subject": "Audi_A1"
},
{
"object": "\"Supermini\"@en",
"property": "class",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
}
],
[
{
"object": "Supermini",
"property": "class",
"subject": "Audi_A1"
},
{
"object": "Audi_A1__5",
"property": "engine",
"subject": "Audi_A1"
},
{
"object": "Front-engine,_front-wheel-drive_layout",
"property": "layout",
"subject": "Audi_A1"
},
{
"object": "Audi_Brussels",
"property": "assembly",
"subject": "Audi_A1"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1645"
}
},
{
"1646": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Audi, owned by Ducati, was founded by August Horch and includes Audi e-tron. It makes the Audi A1.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "August Horch founded Audi, which makes the Audi A1. Audi e-tron is a division of Audi and the company Ducati is owned by them.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "August Horch founded the Audi company who make the Audi A1. The company own Ducati and have a sub-division known as Audi e-tron.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Audi",
"property": "manufacturer",
"subject": "Audi_A1"
},
{
"object": "August_Horch",
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},
{
"object": "Audi_e-tron",
"property": "division",
"subject": "Audi"
},
{
"object": "Ducati",
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"subject": "Audi"
}
],
"originaltriplesets": {
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[
{
"object": "Audi",
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"subject": "Audi_A1"
},
{
"object": "August_Horch",
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"subject": "Audi"
},
{
"object": "Audi_e-tron",
"property": "division",
"subject": "Audi"
},
{
"object": "Ducati",
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"subject": "Audi"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1646"
}
},
{
"1647": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. was founded in California and located in Illinois.AIDA Cruises is the owner of the AIDAluna which is powered by Caterpillar Inc.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc was founded in California and is located in Illinois. It powers the AIDAluna which is owned by AIDA Cruises.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc was founded in California and is located in Illinois. AIDAluna, owned by AIDA Cruises, is powered by Caterpillar Inc.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "California",
"property": "foundationPlace",
"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "owner",
"subject": "AIDAluna"
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "California",
"property": "foundationPlace",
"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "shipOwner",
"subject": "AIDAluna"
},
{
"object": "Caterpillar_Inc.",
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"subject": "AIDAluna"
}
],
[
{
"object": "Illinois",
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"subject": "Caterpillar_Inc."
},
{
"object": "California",
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"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "shipOwner",
"subject": "AIDAluna"
},
{
"object": "Caterpillar_Inc.",
"property": "powerType",
"subject": "AIDAluna"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1647"
}
},
{
"1648": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Costa Crociere, located in Genoa, is the owner of AIDAstella which was built by Meyer Werft is operated by AIDAstella cruises.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella was built by Meyer Werft and is operated by AIDA Cruise Line. The AIDAstella is owned by Costa Crociere, who are located in Genoa.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AIDAstella, owned by Costa Crociere (Genoa) and built by Meyer Werft, is operated by AIDA Cruises.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
"property": "operator",
"subject": "AIDAstella"
},
{
"object": "Meyer_Werft",
"property": "builder",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "owner",
"subject": "AIDAstella"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Genoa",
"property": "location",
"subject": "Costa_Crociere"
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{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
},
{
"object": "Meyer_Werft",
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},
{
"object": "Costa_Crociere",
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],
[
{
"object": "Genoa",
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"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
"property": "shipOperator",
"subject": "AIDAstella"
},
{
"object": "Meyer_Werft",
"property": "shipBuilder",
"subject": "AIDAstella"
},
{
"object": "Costa_Crociere",
"property": "shipOwner",
"subject": "AIDAstella"
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]
]
},
"shape": "NA",
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"xml_id": "Id1648"
}
},
{
"1649": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Delta II is from the United States and was launched at Cape Canaveral Air Force Station. It is comparable to the rocket Antares which launched from the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Delta II originated in the United States and launched from the Cape Canaveral Air Force Station. It is similar to the Antares rocket which launched from the Mid-Atlantic Regional Spaceport Launch Pad 0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Antares rocket, whose launch site was the Mid-Atlantic Regional Spaceport Launch Pad 0, is similar to the the Delta II, which originated in the United States and was launched at Cape Canaveral Air Force Station.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cape_Canaveral_Air_Force_Station",
"property": "launchSite",
"subject": "Delta_II"
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{
"object": "Delta_II",
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"subject": "Antares_(rocket)"
},
{
"object": "United_States",
"property": "countryOrigin",
"subject": "Delta_II"
},
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
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],
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{
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"subject": "Antares_(rocket)"
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{
"object": "United_States",
"property": "countryOrigin",
"subject": "Delta_II"
},
{
"object": "Mid-Atlantic_Regional_Spaceport_Launch_Pad_0",
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"subject": "Antares_(rocket)"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1649"
}
},
{
"1650": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The launch site of the Delta II was at the Vandenberg AFB Space Launch Complex 2, based at Cape Canaveral Air Force Station. The Antares rocket is comparable to Delta 11 which originated in the US>.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Launched at Cape Canaveral Air Force Station and the Vandenberg AFB Space Launch Complex 2 was the Delta II US rocket. The Antares rocket is similar to the Delta.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Delta II, from the United States, was launched at Cape Canaveral Air Force Station, Vandenberg AFB Space Launch Complex 2. It is similar to the Antares rocket.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vandenberg_AFB_Space_Launch_Complex_2",
"property": "launchSite",
"subject": "Delta_II"
},
{
"object": "Cape_Canaveral_Air_Force_Station",
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},
{
"object": "Delta_II",
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{
"object": "United_States",
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],
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{
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id1650"
}
},
{
"1651": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is assembled in Italy and has a sedan body style. It is a related means of transportation to the Fiat Croma and Opel Vectra.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is a sedan that is assembled in Italy. It is a similar means of transport to the Fiat Croma and also related to the Opel Vectra.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo sedan 164 is made in Italy and related to the Fiat Croma which is related to the Opel Vectra.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Opel_Vectra",
"property": "relatedMeanOfTransportation",
"subject": "Fiat_Croma"
},
{
"object": "Italy",
"property": "assembly",
"subject": "Alfa_Romeo_164"
},
{
"object": "Fiat_Croma",
"property": "relatedMeanOfTransportation",
"subject": "Alfa_Romeo_164"
},
{
"object": "Sedan_(automobile)",
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],
"originaltriplesets": {
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{
"object": "Italy",
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},
{
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},
{
"object": "Sedan_(automobile)",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1651"
}
},
{
"1652": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, Fiat Croma and Opel Vectra are related forms of transportation. The body style of the Alfa Romeo 164 is Sedan and it's assembly line is in Milan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164, Opel Vectra and Fiat Croma are similar means of transport. The Alfa Romeo 164 is a sedan which was assembled in Milan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Alfa Romeo 164 is assembled in Milan and has a sedan type body. It is a related and similar means of transport to the Fiat Croma and the Opel Vectra.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Opel_Vectra",
"property": "relatedMeanOfTransportation",
"subject": "Fiat_Croma"
},
{
"object": "Fiat_Croma",
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"subject": "Alfa_Romeo_164"
},
{
"object": "Milan",
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},
{
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],
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{
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{
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1652"
}
},
{
"1653": {
"category": "MeanOfTransportation",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The American Submarine NR-1 has a ship beam of 3.8 m and was built by General Dynamics Electric Boat. General Dynamics is the parent company of the General Dynamics Electric Boat Co which is located in the city of Groton, Connecticut.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "General Dynamics is the parent company of the General Dynamics Electric Boat Co. out of Groton, CT. The GD Electric Boat Co built the American sub called NR-1 which has a 3.8 m beam.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "General Dynamics is the parent company of General Dynamics Electric Boat who are located in the city of Groton, Connecticut and built the American submarine NR-1 with a ship beam of 3.8 m.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "General_Dynamics",
"property": "parentCompany",
"subject": "General_Dynamics_Electric_Boat"
},
{
"object": "3.8 m",
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},
{
"object": "General_Dynamics_Electric_Boat",
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{
"object": "Groton,_Connecticut",
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],
"originaltriplesets": {
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{
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],
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{
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{
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],
[
{
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{
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},
{
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{
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"subject": "General_Dynamics_Electric_Boat"
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],
[
{
"object": "\"4.8\"^^xsd:double",
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{
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{
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},
{
"object": "Groton,_Connecticut",
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"subject": "General_Dynamics_Electric_Boat"
}
],
[
{
"object": "\"*\\n* at stern stabilizers.\"@en",
"property": "shipBeam",
"subject": "American_submarine_NR-1"
},
{
"object": "General_Dynamics_Electric_Boat",
"property": "shipBuilder",
"subject": "American_submarine_NR-1"
},
{
"object": "General_Dynamics",
"property": "parent",
"subject": "General_Dynamics_Electric_Boat"
},
{
"object": "Groton,_Connecticut",
"property": "locationCity",
"subject": "General_Dynamics_Electric_Boat"
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],
[
{
"object": "\"3.8\"^^xsd:double",
"property": "shipBeam",
"subject": "American_submarine_NR-1"
},
{
"object": "General_Dynamics_Electric_Boat",
"property": "shipBuilder",
"subject": "American_submarine_NR-1"
},
{
"object": "General_Dynamics",
"property": "parent",
"subject": "General_Dynamics_Electric_Boat"
},
{
"object": "Groton,_Connecticut",
"property": "locationCity",
"subject": "General_Dynamics_Electric_Boat"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1653"
}
},
{
"1654": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Stanislaw Tillich is a leader of Germany where Rostock is located. The city is the location of Neptun Werft who built the A-Rosa Luna.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Neptun Werft built the A-Rosa Luna at Rostock Germany where Stanislaw Tillich is leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The builder of the A-Rosa, is Neptun Werft which is located in Rostock, Germany, where the leader is Stanislaw Tillich.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stanislaw_Tillich",
"property": "leaderName",
"subject": "Germany"
},
{
"object": "Rostock",
"property": "locationCity",
"subject": "Neptun_Werft"
},
{
"object": "Germany",
"property": "country",
"subject": "Rostock"
},
{
"object": "Neptun_Werft",
"property": "builder",
"subject": "A-Rosa_Luna"
}
],
"originaltriplesets": {
"originaltripleset": [
[
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},
{
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}
],
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},
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},
{
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},
{
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],
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{
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{
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{
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},
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"category": "Artist",
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"comment": "good",
"lang": "",
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},
{
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"lang": "",
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},
{
"comment": "good",
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"xml_id": "Id3"
}
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{
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{
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},
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},
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},
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"comment": "good",
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],
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"lex": "Aleksandra Kovac, who sang rhythm and blues, was born in the Socialist Republic of Serbia in 1972.",
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}
],
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},
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"comment": "good",
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{
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},
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],
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{
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{
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],
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{
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{
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{
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},
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"comment": "good",
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{
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[
{
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},
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"comment": "good",
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}
],
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{
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{
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{
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{
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{
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{
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{
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},
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},
{
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"comment": "good",
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},
{
"comment": "good",
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],
[
{
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"subject": "Andra_(singer)"
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{
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{
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{
"object": "Disco",
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},
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},
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{
"comment": "good",
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},
{
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{
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{
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{
"comment": "good",
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{
"comment": "good",
"lang": "",
"lex": "Andrew White started his musical career in 2003 and is associated with the musical artist, Marry Banilow. He plays for the band Kaiser Chiefs and his genre is alternative rock.",
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],
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}
},
{
"1691": {
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"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Andrew White, who began his musical career in 2003 and was associated with Marry Banilow, was signed to the now defunct record label Universal Records. He was a member of the band The Kaiser Chiefs.",
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},
{
"comment": "good",
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"lex": "Musician Andrew White began in 2003 and is associated with artists Marry Banilow and the Kaiser Chiefs. He was signed to the defunct Universal records.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kaiser_Chiefs",
"property": "associatedBand/associatedMusicalArtist",
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{
"object": "Marry_Banilow",
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{
"object": "Universal_Records_(defunct_record_label)",
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{
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{
"object": "Kaiser_Chiefs",
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{
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{
"object": "Universal_Records_(defunct_record_label)",
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{
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[
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{
"object": "Kaiser_Chiefs",
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{
"object": "Universal_Records_(defunct_record_label)",
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{
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},
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"comment": "good",
"lang": "",
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"comment": "good",
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"lex": "Agustin Barboza was born in Paraguay which is led by Juan Afra. He died in Asuncion which is part of Gran Asuncion.",
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{
"comment": "good",
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"lex": "Agustin Barboza was born in Paraguay and died in Asuncion, part of Gran Asunción, The leader of Paraguay is Juan Afara.",
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{
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{
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{
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{
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{
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]
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"comment": "good",
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},
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{
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{
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},
{
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"comment": "good",
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{
"comment": "good",
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"lex": "Allen Forrest performs hip hop music, which has its stylistic origins in disco and funk. Drum and bass is a derivative of hip hop.",
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},
{
"comment": "good",
"lang": "",
"lex": "The musical genre of Allen Forrest is hip hop music which has its origins in Disco and its stylistic origins in funk. Drum and bass is a derivative of Hip hop music.",
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}
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"property": "genre",
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{
"object": "Funk",
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{
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},
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"comment": "good",
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"comment": "good",
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"lex": "The birthplace of Alfred Garth Jones is Manchester, (part of Greater Manchester), England. In Manchester the Labour Party is in the majority.",
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{
"comment": "good",
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"lex": "Alfred Garth Jones was born in Manchester, Great Manchester, England, where the majority is the Labour Party.",
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],
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}
},
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"comment": "good",
"lang": "",
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},
{
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"lex": "Aaron Turner played post-metal music with the Twilight band. He also played with the House of Low Culture. Cello is a post-metal instrument.",
"xml_id": "Id2"
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{
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"xml_id": "Id3"
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{
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{
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"subject": "Aaron_Turner"
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{
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},
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner plays electric guitar and played with the band Twilight. Twilight performs black metal music which when part of a musical fusion is called death metal.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Black metal electric guitarist Aaron Turner was a member of Twilight. Black metal is the basis for death metal fusion.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Black_metal",
"property": "genre",
"subject": "Twilight_(band)"
},
{
"object": "Twilight_(band)",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Electric_guitar",
"property": "instrument",
"subject": "Aaron_Turner"
},
{
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"subject": "Black_metal"
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],
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{
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"property": "genre",
"subject": "Twilight_(band)"
},
{
"object": "Twilight_(band)",
"property": "associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Electric_guitar",
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{
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]
]
},
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}
},
{
"1698": {
"category": "Artist",
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{
"comment": "good",
"lang": "",
"lex": "Rhythm and blues artist Albennie Jones was born in the U.S., home to African Americans. R & B has its roots in blues.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rhythm and blues artist Albennie Jones was born in the United States. R and B originated from the blues and among the ethnic groups in the U.S. are African Americans.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "African Americans are an ethnic group in the United States, where Albennie Jones was born. Albennie Jones performs in the rhythm and blues genre which originated from blues music.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Rhythm_and_blues",
"property": "genre",
"subject": "Albennie_Jones"
},
{
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"subject": "Rhythm_and_blues"
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{
"object": "United_States",
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"subject": "Albennie_Jones"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "African_Americans",
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{
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{
"object": "Blues",
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{
"object": "United_States",
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[
{
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{
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},
{
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},
{
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],
[
{
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},
{
"object": "United_States",
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"subject": "Albennie_Jones"
},
{
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1698"
}
},
{
"1699": {
"category": "Politician",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron S Daggett was born in Maine and died in West Roxbury. He fought in the American Civil War and was awarded the Purple Heart.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Maine born Aaron S Daggett fought in the American Civil War, was awarded the Purple Heart and died in West Roxbury.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "American Civil War soldier and Purple Heart recipient Aaron S. Daggett, was born in Maine and died in West Roxbury.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "American_Civil_War",
"property": "battles",
"subject": "Aaron_S._Daggett"
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{
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"property": "award",
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{
"object": "West_Roxbury",
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{
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"originaltriplesets": {
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{
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"property": "battles",
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]
},
"shape": "NA",
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"xml_id": "Id1699"
}
},
{
"1700": {
"category": "Politician",
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{
"comment": "good",
"lang": "",
"lex": "Born in Maine, Aaron S. Daggett; fought in the Battle of the Wilderness, was awarded the Purple Heart, and died in West Roxbury.",
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},
{
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},
{
"comment": "good",
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"lex": "Aaron S Daggett was born in Maine and died in West Roxbury. He fought in the Battle of the Wilderness and was awarded the Purple Heart.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Battle_of_the_Wilderness",
"property": "battles",
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{
"object": "Purple_Heart",
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{
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{
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{
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"xml_id": "Id1700"
}
},
{
"1701": {
"category": "Politician",
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{
"comment": "good",
"lang": "",
"lex": "Ab Klink was born in Netherlands and worked for his country's Ministry of Health, Welfare and Sport office. He attended Erasmus University in Rotterdam which is affiliated with the Association of MBAs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ab Klink worked at the Ministry of Health, Welfare and Sport in Netherlands, the country where he was born. He was a student of Erasmus University in Rotterdam which is affiliated with the Association of MBAs.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Erasmus University Rotterdam (Assoc. MBAs) alum Ab Klink, born in the Netherlands, is employed at the Netherlands Ministry of Health, Welfare and Sport.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Erasmus_University_Rotterdam",
"property": "almaMater",
"subject": "Ab_Klink"
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{
"object": "Association_of_MBAs",
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"subject": "Erasmus_University_Rotterdam"
},
{
"object": "Ministry_of_Health,_Welfare_and_Sport_(Netherlands)",
"property": "office (workedAt, workedAs)",
"subject": "Ab_Klink"
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{
"object": "Netherlands",
"property": "birthPlace",
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{
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"property": "almaMater",
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{
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{
"object": "Ministry_of_Health,_Welfare_and_Sport_(Netherlands)",
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},
{
"object": "Netherlands",
"property": "birthPlace",
"subject": "Ab_Klink"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1701"
}
},
{
"1702": {
"category": "Politician",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud was born in Miri, Malaysia and he lives in Kuching. His party is the Parti Pesaka Bumiputera Bersatu and he represented Asajaya.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Mirir, Malaysia born Abdul Taib Mahmud, who lives in Kuching, represented the Asajaya region and was a member of the Parti Pesaka Bumiputera Bersatu.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud, who was born in Miri, Malaysia and lives in Kuching, was a member of the Parti Pesaka Bumiputera Bersatu and represented Asajaya.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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{
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"property": "region",
"subject": "Abdul_Taib_Mahmud"
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{
"object": "Kuching",
"property": "residence",
"subject": "Abdul_Taib_Mahmud"
},
{
"object": "Miri,_Malaysia",
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],
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{
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{
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{
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{
"object": "Miri,_Malaysia",
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]
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},
"shape": "NA",
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"size": "4",
"xml_id": "Id1702"
}
},
{
"1703": {
"category": "Politician",
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{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud's birthplace was Kingdom of Sarawak and he resides in Sarawak. His party is the Parti Pesaka Bumiputera Bersatu and he represented Asajaya.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud was born and resided in the Kingdom of Sarawak. He was in the Parti Pesaka Bumiputera Bersatu and represented the Asajaya region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud represented the Asajaya region and was a member of the Parti Pesaka Bumiputera Bersatu. He was born in the Kingdom of Sarawak and still lives there.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sarawak",
"property": "residence",
"subject": "Abdul_Taib_Mahmud"
},
{
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"property": "party",
"subject": "Abdul_Taib_Mahmud"
},
{
"object": "Asajaya",
"property": "region",
"subject": "Abdul_Taib_Mahmud"
},
{
"object": "Kingdom_of_Sarawak",
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],
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[
{
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{
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{
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1703"
}
},
{
"1704": {
"category": "Politician",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adenan Satem was born in Japanese Occupied British Borneo. His successor was Abdul Taib Mahmud, who, resides in Sarawak and is a member of the \"Barisan Ra'ayat Jati Sarawak\" party.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Japanese occupied, British Borneo, was the birthplace of Adenan Satem, who succeeded Abdul Taib Mahmud. Abdul Taid Mahmud is a member of Barisan Ra'ayat Jati Sarawak party and resides in Sarawak.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud, a member of the Barisan Ra'ayat Jati Sarawak party, lives in Sarawak. He was succeeded by Adenan Satem, who was born in the Japanese Occupation of British Borneo.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Adenan_Satem",
"property": "successor",
"subject": "Abdul_Taib_Mahmud"
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{
"object": "Sarawak",
"property": "residence",
"subject": "Abdul_Taib_Mahmud"
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{
"object": "\"Barisan Ra'ayat Jati Sarawak\"",
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],
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{
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"object": "Sarawak",
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{
"object": "\"Barisan Ra'ayat Jati Sarawak\"@en",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id1704"
}
},
{
"1705": {
"category": "Politician",
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{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud(who lives in Sarawak)'s successor was Adenan Satem who was born in Japanese Occupation of British Borneo. Abdul Taib Mahmud's party is the Parti Pesaka Bumiputera Bersatu.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud, who lives in Sarawak, belongs to the Parti Pesaka Bumiputera Bersatu. He was succeeded by Adenan Satem, who was born in the Japanese occupation Of British Borneo.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdul Taib Mahmud resides in Sarawak and is a member of the Parti Pesaka Bumiputera Bersatu. His successor was Adenan Satem who was born in British Borneo during the Japanese occupation.",
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}
],
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"modifiedtripleset": [
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{
"object": "Sarawak",
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{
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],
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{
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{
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},
"shape": "NA",
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"xml_id": "Id1705"
}
},
{
"1706": {
"category": "Politician",
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"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar served in the Nigerian Army and was in office whilst Mike Akhigbe was Vice President. Oladipo Diya was the predecessor of Mike Akhigbe and his successor was Atiku Abubakar.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Abdulsalami Abubakar served in the Nigerian Army and was in office while Mike Akhigbe was Vice President. Oladipo Diya was the predecessor of Mike Akhigbe and Atiku Abubakar was his successor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar, who served in the Nigerian Army, was in office while Mike Akhigbe was Vice President. Akhigbe succeeded Oladipo Diya and then he himself was succeeded by Atiku Abubakar.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mike_Akhigbe",
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{
"object": "Nigerian_Army",
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},
{
"object": "Oladipo_Diya",
"property": "predecessor",
"subject": "Mike_Akhigbe"
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{
"object": "Atiku_Abubakar",
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"subject": "Mike_Akhigbe"
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],
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[
{
"object": "Mike_Akhigbe",
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{
"object": "Nigerian_Army",
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{
"object": "Oladipo_Diya",
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{
"object": "Atiku_Abubakar",
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},
"shape": "NA",
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"xml_id": "Id1706"
}
},
{
"1707": {
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{
"comment": "good",
"lang": "",
"lex": "Oladipo Diya was Mike Akhigbe's predecessor, Abdulsalami Abubakar was in office whilst Mike Akhigbe was Vice President. Abdulsalami Abubakar, was a member of the Nigerian Air Force, and his succcessor was Al-Amin Daggash.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Abdulsalami Abubakar was a member of the Nigerian Air Force. He was in office while Mike Akhigbe was Vice President. Oladipo Diya was the predecessor of Mike Akhigbe and Al-Amin Daggash was the successor of Abdulsalami Abubakar.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abdulsalami Abubakar, who served in the Nigerian Air Force, was in office whilst Mike Akhigbe was Vice President and was preceded by Al-Amin Daggash. Mike Akhigbe succeeded Oladipo Diya.",
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}
],
"links": [],
"modifiedtripleset": [
{
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{
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},
{
"object": "Al-Amin_Daggash",
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},
{
"object": "Nigerian_Air_Force",
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],
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[
{
"object": "Mike_Akhigbe",
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{
"object": "Al-Amin_Daggash",
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{
"object": "Nigerian_Air_Force",
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]
]
},
"shape": "NA",
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"xml_id": "Id1707"
}
},
{
"1708": {
"category": "Politician",
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{
"comment": "good",
"lang": "",
"lex": "Abel Caballero, born in Galicia, Spain, held office during the monarchy of Juan Carlos I of Spain (preceeded by Francisco Franco) and Prime Minister Felipe Gonzalez' leadership.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Caballero, born in Galicia, Spain, served office during Prime Minister Felipe Gonzalez' leadership and Juan Carlos I of Spain's Monarchy. Francisco Franco preceded Juan Carlos I.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Juan_Carlos_I_of_Spain",
"property": "inOfficeWhileMonarch",
"subject": "Abel_Caballero"
},
{
"object": "Francisco_Franco",
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"subject": "Juan_Carlos_I_of_Spain"
},
{
"object": "Felipe_González",
"property": "inOfficeWhilePrimeMinister",
"subject": "Abel_Caballero"
},
{
"object": "Galicia_(Spain)",
"property": "birthPlace",
"subject": "Abel_Caballero"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Juan_Carlos_I_of_Spain",
"property": "monarch",
"subject": "Abel_Caballero"
},
{
"object": "Francisco_Franco",
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"subject": "Juan_Carlos_I_of_Spain"
},
{
"object": "Felipe_González",
"property": "primeminister",
"subject": "Abel_Caballero"
},
{
"object": "Galicia_(Spain)",
"property": "birthPlace",
"subject": "Abel_Caballero"
}
]
]
},
"shape": "NA",
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"size": "4",
"xml_id": "Id1708"
}
},
{
"1709": {
"category": "Politician",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "Abel Caballero was born in Galicia in Spain and he was the Minister of Transport. He is now a member of the Spanish Socialist Workers' Party.",
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"lex": "Albert B. White was born in Cleveland and he died in Parkersburg, West Virginia. His successor was William M. O. Dawson who was born in Bloomington, Maryland.",
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},
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"comment": "good",
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],
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}
],
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{
"object": "Carnival_Corporation_&_plc",
"property": "parentCompany",
"subject": "Costa_Crociere"
},
{
"object": "AIDA_Cruises",
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},
{
"object": "Meyer_Werft",
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},
{
"object": "Costa_Crociere",
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],
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{
"object": "Germany",
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{
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{
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},
{
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},
{
"object": "Costa_Crociere",
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[
{
"object": "Germany",
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{
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{
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{
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}
],
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{
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{
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{
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{
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}
],
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{
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},
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"comment": "good",
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}
],
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{
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},
{
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],
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},
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],
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},
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"comment": "good",
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},
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],
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],
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"comment": "good",
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"lex": "Audi, which was founded by August Horch, owns the company Ducati and manufactures the Audi A1. It also has a sub-division known as Audi e-tron and the subsidiary company Lamborghini.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Audi",
"property": "manufacturer",
"subject": "Audi_A1"
},
{
"object": "August_Horch",
"property": "foundedBy",
"subject": "Audi"
},
{
"object": "Ducati",
"property": "subsidiary",
"subject": "Audi"
},
{
"object": "Audi_e-tron",
"property": "division",
"subject": "Audi"
},
{
"object": "Lamborghini",
"property": "subsidiary",
"subject": "Audi"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Audi",
"property": "manufacturer",
"subject": "Audi_A1"
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{
"object": "August_Horch",
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},
{
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},
{
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},
{
"object": "Lamborghini",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1760"
}
},
{
"1761": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. founded in California in the U.S. is now in Peoria, Ill. That company powered the AIDAluna craft and is owned by AIDA Cruises.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The power type of the AIDAluna, owned by AIDA Cruises, is the Caterpillar Inc. engine. Caterpillar, Inc. was founded in California in the U.S. and is located in Peoria, Illinois.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Caterpillar Inc. was founded in California, United States, and is located in Peoria, Illinois. The company provide the engine to power the AIDAluna which is owned by AIDA Cruises.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"subject": "Caterpillar_Inc."
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{
"object": "United_States",
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"subject": "Caterpillar_Inc."
},
{
"object": "Peoria,_Illinois",
"property": "location",
"subject": "Caterpillar_Inc."
},
{
"object": "AIDA_Cruises",
"property": "owner",
"subject": "AIDAluna"
},
{
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"subject": "AIDAluna"
}
],
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"originaltripleset": [
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{
"object": "California",
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{
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},
{
"object": "Peoria,_Illinois",
"property": "location",
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},
{
"object": "AIDA_Cruises",
"property": "shipOwner",
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},
{
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],
[
{
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{
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{
"object": "Peoria,_Illinois",
"property": "location",
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},
{
"object": "AIDA_Cruises",
"property": "shipOwner",
"subject": "AIDAluna"
},
{
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]
},
"shape": "NA",
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},
{
"1762": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AIDA Cruises, located at Rostock, is owned by AIDAluna. AIDAluna is powered by Caterpillar Inc., which was founded in the U.S. and is led by Douglas R. Oberhelman.",
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},
{
"comment": "good",
"lang": "",
"lex": "Douglas R. Oberhelman is the key person at Caterpillar Inc which was founded in the United states. The company powers the AIDAluna which is owned by AIDA Cruises located in Rostock.",
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},
{
"comment": "good",
"lang": "",
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}
],
"links": [],
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},
{
"object": "Rostock",
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"subject": "AIDA_Cruises"
},
{
"object": "AIDA_Cruises",
"property": "owner",
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{
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],
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{
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{
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]
},
"shape": "NA",
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"xml_id": "Id1762"
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},
{
"1763": {
"category": "MeanOfTransportation",
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{
"comment": "good",
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{
"comment": "good",
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"lex": "Douglas R Oberhelman is a key person at Caterpillar Inc. which was founded in the United States and located in Peoria, Illinois. The AIDAluna, which is owned by AIDA Cruises is powered by the company.",
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],
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{
"object": "Peoria,_Illinois",
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{
"object": "AIDA_Cruises",
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{
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{
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{
"object": "AIDA_Cruises",
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},
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},
{
"1764": {
"category": "MeanOfTransportation",
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"comment": "good",
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},
{
"comment": "good",
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"lex": "The AIDAstella, which was built by Meyer Werft, is operated by AIDA Cruises. It's owner are Costa Crociere who are located in Genoa and a subsidiary of Carnival Corporation & Plc.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Meyer Werft built the AIDAstella which is operated by the AIDA Cruise Line. It is owned by Costa Crociere who are located in Genoa and have Carnival Corporation & plc as their parent company.",
"xml_id": "Id3"
}
],
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{
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{
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{
"object": "AIDA_Cruises",
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{
"object": "Meyer_Werft",
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{
"object": "Costa_Crociere",
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],
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{
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{
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{
"object": "AIDA_Cruises",
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{
"object": "Meyer_Werft",
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{
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{
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{
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{
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{
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{
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]
},
"shape": "NA",
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"xml_id": "Id1764"
}
},
{
"1765": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "Finland's Arctech Helsinki Shipyard built the icebreaker, Aleksey Chirikov. Juha Sipila leads Finland, where the people are known as Finns and Sami languages are spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Finland is led by Juha Sipila and is home to the Finns. Sami languages are spoken there and the icebreaker ship, Aleksey Chirikov, was built there in the Arctech Helsinki Shipyard.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The icebreaker Aleksey Chirikov was built at the Arctech Helsinki Shipyard in Finland. The country is led by Juha Sipila, uses the Sami language and the inhabitants are known as Finns.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Finns",
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{
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{
"object": "Juha_Sipilä",
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},
{
"object": "Finland",
"property": "builder",
"subject": "Aleksey_Chirikov_(icebreaker)"
},
{
"object": "Arctech_Helsinki_Shipyard",
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],
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{
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],
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{
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{
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{
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{
"object": "Arctech_Helsinki_Shipyard",
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]
]
},
"shape": "NA",
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"xml_id": "Id1765"
}
},
{
"1766": {
"category": "MeanOfTransportation",
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"lexicalisations": [
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"comment": "good",
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"lex": "The A Rosa Luna was built on the Neptun Werft, located in Rostock, Germany. Germany (whose capital city is Berlin) is led by Joachim Gauck.",
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},
{
"comment": "good",
"lang": "",
"lex": "The builder of the A-Rosa is Neptun Werft whose HQ is in Rostock, Germany. Berlin is the capital in that country and Joachim Gauck is the name of one of its leaders.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A-Rosa was built by Neptun Werft in Rostock, Germany. Joachim Gauck is a leader of Germany, with its capital at Berlin.",
"xml_id": "Id3"
}
],
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{
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{
"object": "Joachim_Gauck",
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},
{
"object": "Germany",
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},
{
"object": "Neptun_Werft",
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],
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{
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{
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},
{
"object": "Germany",
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},
{
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]
},
"shape": "NA",
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"xml_id": "Id1766"
}
},
{
"1767": {
"category": "MeanOfTransportation",
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"comment": "good",
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"lex": "Neptun Werft of Rostock, Germany made the A-Rosa Luna. Berlin is the capital of Germany and Andreas Voßkuhle is a leader there.",
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},
{
"comment": "good",
"lang": "",
"lex": "The A Rosa Luna was built on the Neptun Werft which is found in Rostock, Germany. Berlin is its capital and Andreas Vosskuhle is one leader there.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A Rosa Luna was built on the Neptun Werft which is headquartered in Rostock, Germany. Andreas Vosskuhle is a leader in Germany and Berlin is the capital. .",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "Andreas_Voßkuhle",
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{
"object": "Berlin",
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},
{
"object": "Rostock",
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},
{
"object": "Germany",
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{
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],
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},
"shape": "NA",
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"xml_id": "Id1767"
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},
{
"1768": {
"category": "MeanOfTransportation",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Atlas II is from the United States, whose capital is Washington, D.C. Inhabitants of the United States are called Americans, and english is a spoken language there. An ethnic group called Native Americans is also located in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States, has a capital city of Washington D.C., it's inhabitants are known as Americans and an ethnic group that lives there are Native Americans. The language of the US is English and it is the creator of the Atlas II.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The, English speaking, United States, with its capital in Washington D.C., is home to American, Native Americans and the Atlas II.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
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},
{
"object": "Washington,_D.C.",
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},
{
"object": "English_language",
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},
{
"object": "United_States",
"property": "countryOrigin",
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},
{
"object": "Native_Americans_in_the_United_States",
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],
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{
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{
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{
"object": "Native_Americans_in_the_United_States",
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]
},
"shape": "NA",
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"xml_id": "Id1768"
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},
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"1769": {
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"comment": "good",
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"lex": "Penarol, where Abel Hernandez played youth football, is managed by Jorge Orosmán da Silva. Hernandez later played for Uruguayan Segunda División side Central Español and then moved to U.S. Città di Palermo.",
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},
{
"comment": "good",
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"lex": "Abel Hernandez is a member of Penarol's youth club and the club is managed by Jorge Orosmán da Silva. US Citta di Palermo and the Central Espanol are also Mr. Hernandez's clubs. The latter club is part of the Uruguayan Segunda División league.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "In his youth, Abel Hernández played for Peñarol where Jorge Orosman da Silva is the manager. He currently plays for US Citta di Palermo and also in the Uruguayan Segunda Division league with Central Espanol.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "Uruguayan_Segunda_División",
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{
"object": "Central_Español",
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{
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{
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],
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],
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{
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"object": "Jorge_Orosmán_da_Silva",
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"subject": "Peñarol"
},
{
"object": "U.S._Città_di_Palermo",
"property": "team",
"subject": "Abel_Hernández"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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}
},
{
"1770": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
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},
{
"comment": "good",
"lang": "",
"lex": "Former Soviet Union international footballer Aleksandr Chumakov died in Russia, where the leader is Valentina Matviyenko. FC Torpedo Moscow, managed by Valery Petrakov, once signed Aleksandr Chumakov.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov died in Russia where Valentina Matviyenko is a leader. He belongs to the Soviet Union national football team and has played for FC Torpedo Moscow under the manager Valery Petrakov.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Russia",
"property": "deathPlace",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valentina_Matviyenko",
"property": "leaderName",
"subject": "Russia"
},
{
"object": "FC_Torpedo_Moscow",
"property": "club",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Soviet_Union_national_football_team",
"property": "club",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valery_Petrakov",
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}
],
"originaltriplesets": {
"originaltripleset": [
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{
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{
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},
{
"object": "FC_Torpedo_Moscow",
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"subject": "Aleksandr_Chumakov"
},
{
"object": "Soviet_Union_national_football_team",
"property": "team",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valery_Petrakov",
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}
],
[
{
"object": "Russia",
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"subject": "Aleksandr_Chumakov"
},
{
"object": "Valentina_Matviyenko",
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},
{
"object": "FC_Torpedo_Moscow",
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"subject": "Aleksandr_Chumakov"
},
{
"object": "Soviet_Union_national_football_team",
"property": "team",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Valery_Petrakov",
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]
]
},
"shape": "NA",
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}
},
{
"1771": {
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"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Aleksandre Guruli played for the Olympique Lyonnais club who play their home games at the Parc Olympique Lyonnais and have the chairman, Jean-Michel Aulas. His current club is FC Karpaty Lviv managed by Oleh Luzhny.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandre Guruli played for FC Karpaty Lviv who are managed by Oleh Luzhny. Guruli has also played for Olympique Lyonnais whose home ground is the Parc Olympique Lyonnais and whose chairman is Jean-Michel Aulas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "FC_Karpaty_Lviv",
"property": "club",
"subject": "Aleksandre_Guruli"
},
{
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"property": "manager",
"subject": "FC_Karpaty_Lviv"
},
{
"object": "Jean-Michel_Aulas",
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},
{
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"property": "club",
"subject": "Aleksandre_Guruli"
},
{
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],
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{
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{
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},
{
"object": "Jean-Michel_Aulas",
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},
{
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},
{
"object": "Parc_Olympique_Lyonnais",
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}
],
[
{
"object": "Oleh_Luzhny",
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},
{
"object": "Olympique_Lyonnais",
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},
{
"object": "Jean-Michel_Aulas",
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},
{
"object": "Parc_Olympique_Lyonnais",
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},
{
"object": "FC_Karpaty_Lviv",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1771"
}
},
{
"1772": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The defender, Alessio Romagnoli is a member of the Serie A league club A.C. Milan who are managed by Sinisa Mihajlovic. He is also connected to UC Sampdoria.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Defender Alessio Romagnoli, whose former clubs include UC Sampdoria, has played for the Serie A side AC Milan, which is managed by Siniša Mihajlović.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli is a defender who plays for UC Sampdoria. He also plays in Serie A for AC Milan who are managed by Sinisa Mihajlovic.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "A.C._Milan",
"property": "club",
"subject": "Alessio_Romagnoli"
},
{
"object": "Serie_A",
"property": "league",
"subject": "A.C._Milan"
},
{
"object": "Siniša_Mihajlović",
"property": "manager",
"subject": "A.C._Milan"
},
{
"object": "Defender_(football)",
"property": "position",
"subject": "Alessio_Romagnoli"
},
{
"object": "U.C._Sampdoria",
"property": "club",
"subject": "Alessio_Romagnoli"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "A.C._Milan",
"property": "team",
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},
{
"object": "Serie_A",
"property": "league",
"subject": "A.C._Milan"
},
{
"object": "Siniša_Mihajlović",
"property": "manager",
"subject": "A.C._Milan"
},
{
"object": "Defender_(association_football)",
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"subject": "Alessio_Romagnoli"
},
{
"object": "\"→ Sampdoria\"@en",
"property": "clubs",
"subject": "Alessio_Romagnoli"
}
],
[
{
"object": "Serie_A",
"property": "league",
"subject": "A.C._Milan"
},
{
"object": "A.C._Milan",
"property": "clubs",
"subject": "Alessio_Romagnoli"
},
{
"object": "Siniša_Mihajlović",
"property": "manager",
"subject": "A.C._Milan"
},
{
"object": "Defender_(association_football)",
"property": "position",
"subject": "Alessio_Romagnoli"
},
{
"object": "U.C._Sampdoria",
"property": "team",
"subject": "Alessio_Romagnoli"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id1772"
}
},
{
"1773": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alessio Romagnoli is a member of the Italian national under 17 football team and has represented the club U.C. Sampdoria. He is presently with the AC Milan that is managed by Siniša Mihajlović and whose chairman is Silvio Berlusconi.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "A.C._Milan",
"property": "currentclub",
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},
{
"object": "Siniša_Mihajlović",
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"subject": "A.C._Milan"
},
{
"object": "Silvio_Berlusconi",
"property": "chairman",
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},
{
"object": "Italy_national_under-17_football_team",
"property": "club",
"subject": "Alessio_Romagnoli"
},
{
"object": "U.C._Sampdoria",
"property": "club",
"subject": "Alessio_Romagnoli"
}
],
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[
{
"object": "A.C._Milan",
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{
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},
{
"object": "Silvio_Berlusconi",
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{
"object": "Italy_national_under-17_football_team",
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"subject": "Alessio_Romagnoli"
},
{
"object": "\"→ Sampdoria\"@en",
"property": "clubs",
"subject": "Alessio_Romagnoli"
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]
]
},
"shape": "NA",
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"xml_id": "Id1773"
}
},
{
"1774": {
"category": "Athlete",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abel Hernández played youth football for Central Español part of the Uruguayan Segunda División league. Mr. Hernandez plays for the US Citta di Palermo and the Uruguay National football team. Giuseppe Iachini manages the U.S. Città di Palermo team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Central Español, where Abel Hernandez played youth football, are part of the Uruguayan Segunda División league. Hernández has represented Uruguay at international level and also played for the club U.S. Città di Palermo, where the manager is Giuseppe Iachini.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Abel Hernandez played youth football for Central Espanol, part of the Uruguayan Segunda División league. He plays for the US Citta di Palermo, managed by Giuseppe Iachini, and the Uruguay National football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Uruguayan_Segunda_División",
"property": "league",
"subject": "Central_Español"
},
{
"object": "Central_Español",
"property": "youthclub",
"subject": "Abel_Hernández"
},
{
"object": "Giuseppe_Iachini",
"property": "manager",
"subject": "U.S._Città_di_Palermo"
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{
"object": "Uruguay_national_football_team",
"property": "club",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "club",
"subject": "Abel_Hernández"
}
],
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{
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{
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{
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{
"object": "Uruguay_national_football_team",
"property": "team",
"subject": "Abel_Hernández"
},
{
"object": "U.S._Città_di_Palermo",
"property": "clubs",
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]
]
},
"shape": "NA",
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"xml_id": "Id1774"
}
},
{
"1775": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov's club is FC Spartak Moscow who have Sergey Rodionov as their chairman and play at the Otkrytiye arena. He also plays for FC Amkar Perm who are managed by Gadzhi Gadzhiyev.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov plays for FC Spartak Moscow who have their home ground at Otkrytiye Arena and are chaired by Sergey Rodionov. He also plays for FC Amkar Perm under the management of Gadzhi Gadzhiyev.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sergey Rodionov is the chairman of FC Spartak Moscow, who play at the Otkrytiye Arena and count Aleksandr Prudnikov as one of their former players. Prudnikov has also played for FC Amkar Perm, where the manager was Gadzhi Gadzhiyev.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sergey_Rodionov",
"property": "chairman",
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{
"object": "Otkrytiye_Arena",
"property": "ground",
"subject": "FC_Spartak_Moscow"
},
{
"object": "FC_Spartak_Moscow",
"property": "club",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "FC_Amkar_Perm",
"property": "club",
"subject": "Aleksandr_Prudnikov"
},
{
"object": "Gadzhi_Gadzhiyev",
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}
],
"originaltriplesets": {
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[
{
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{
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{
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{
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],
[
{
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{
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{
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{
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{
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]
},
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}
},
{
"1776": {
"category": "Athlete",
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{
"comment": "good",
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"lex": "Aleksandr Prudnikov has played for the clubs FC Terek Grozny (based in Grozny), where the manager was Rashid Rakhimov and FC Spartak Moscow, whose chairman is Sergey Rodionov.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Aleksandr Prydnikov belongs to FC Terek Grozny, which is managed by Rashid Rakhimov, and has its grounds at Grozny. Prudnikov plays for FC Spartak Moscow, which includes chairman Sergey Rodionov.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Aleksandr Prudnikov belongs to FC Terek Grozny, which is managed by Rashid Rakhimov and has it grounds at Grozny. He played for FC Spartak Moscow, which includes Sergey Rodionov.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Sergey_Rodionov",
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{
"object": "Grozny",
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{
"object": "FC_Spartak_Moscow",
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{
"object": "FC_Terek_Grozny",
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{
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],
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{
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{
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{
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{
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]
]
},
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}
},
{
"1777": {
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"comment": "good",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov once played for FC Spartak Moscow's youth team but now plays for the FC Dynamo Moscow and the FC Terek Grozny out of Grozny. The FC Terek Grozny sees a manager in Rashid Rakhimov.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Prudnikov's club is FC Dynamo Moscow although he previously played for FC Spartak Moscow's youth team. He is currently in the Grozny based FC Terek Grozny club under their manager, Rashid Rakhimov.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Grozny",
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{
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},
{
"object": "FC_Terek_Grozny",
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"subject": "Aleksandr_Prudnikov"
},
{
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},
{
"object": "FC_Dynamo_Moscow",
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"subject": "Aleksandr_Prudnikov"
}
],
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{
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{
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{
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{
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},
{
"object": "FC_Dynamo_Moscow",
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]
]
},
"shape": "NA",
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"xml_id": "Id1777"
}
},
{
"1778": {
"category": "Athlete",
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{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aleksandr Chumakov was born in the Soviet Union and was a member of the Soviet Union national football team. He has also played for FC Torpedo Moscow who are managed by Valery Petrakov and participated in the 2014-15 Russian Premier League.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Born in the Soviet Union and representing his country at international football, Aleksandr Chumakov played for FC Torpedo Moscow, who competed in the 2014-15 Russian Premier League and whose manager is Valery Petrakov.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Russian_Premier_League",
"property": "season",
"subject": "FC_Torpedo_Moscow"
},
{
"object": "Soviet_Union",
"property": "birthPlace",
"subject": "Aleksandr_Chumakov"
},
{
"object": "FC_Torpedo_Moscow",
"property": "club",
"subject": "Aleksandr_Chumakov"
},
{
"object": "Soviet_Union_national_football_team",
"property": "club",
"subject": "Aleksandr_Chumakov"
},
{
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"lex": "1101 Clematis has a mass of 5.7 kgs,an orbital period of 183309000.0 and its epoch date was on 31st July 2016.Its apoapsis is 520906000.0 Kilometers and it has an escape velocity of 0.02 k.p.s.",
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"property": "instrument",
"subject": "Country_music"
},
{
"object": "Rock_music",
"property": "genre",
"subject": "Al_Anderson_(NRBQ)"
},
{
"object": "Country_music",
"property": "stylisticOrigins",
"subject": "Rock_music"
},
{
"object": "Blues",
"property": "stylisticOrigin",
"subject": "Rock_music"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id1860"
}
},
{
"1861": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner is an electric guitar player who has played with the black metal band Twilight and with Old Man Gloom. Death metal is a musical fusion of black metal.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aaron Turner plays electric guitar and played with the black metal Twilight band and the Old Man Gloom band. A fusion of black metal is called Death metal.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Electric guitarist Aaron Turner played with black metal band Twilight and Old Man Gloom. Death metal is a fusion of blck metal.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Black_metal",
"property": "genre",
"subject": "Twilight_(band)"
},
{
"object": "Twilight_(band)",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Old_Man_Gloom",
"property": "associatedBand/associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Electric_guitar",
"property": "instrument",
"subject": "Aaron_Turner"
},
{
"object": "Death_metal",
"property": "musicFusionGenre",
"subject": "Black_metal"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Black_metal",
"property": "genre",
"subject": "Twilight_(band)"
},
{
"object": "Twilight_(band)",
"property": "associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Old_Man_Gloom",
"property": "associatedBand",
"subject": "Aaron_Turner"
},
{
"object": "Electric_guitar",
"property": "instrument",
"subject": "Aaron_Turner"
},
{
"object": "Death_metal",
"property": "musicFusionGenre",
"subject": "Black_metal"
}
],
[
{
"object": "Twilight_(band)",
"property": "associatedBand",
"subject": "Aaron_Turner"
},
{
"object": "Black_metal",
"property": "genre",
"subject": "Twilight_(band)"
},
{
"object": "Old_Man_Gloom",
"property": "associatedMusicalArtist",
"subject": "Aaron_Turner"
},
{
"object": "Electric_guitar",
"property": "instrument",
"subject": "Aaron_Turner"
},
{
"object": "Death_metal",
"property": "musicFusionGenre",
"subject": "Black_metal"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id1861"
}
},
{
"1862": {
"category": "Artist",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa died in Montevideo, Uruguay. Daniel Martinez is a political leader in Montevideo, and Raul Fernando Sendic Rodriguez is a leader in Uruguay, where Spanish is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alfredo Zitarrosa died in Montevideo, the leader of which, is Daniel Martinez. Montevideo is in Uruguay, where Spanish is the language and where Raúl Fernando Sendic Rodríguez is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Raúl Fernando Sendic Rodríguez is the leader of Spanish speaking, Uruguay. Daniel Martinez is the leader of Montevideo, the final resting place of Alfredo Zitarrosa.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Raúl_Fernando_Sendic_Rodríguez",
"property": "leaderName",
"subject": "Uruguay"
},
{
"object": "Montevideo",
"property": "deathPlace",
"subject": "Alfredo_Zitarrosa"
},
{
"object": "Uruguay",
"property": "country",
"subject": "Montevideo"
},
{
"object": "Daniel_Martínez_(politician)",
"property": "leaderName",
"subject": "Montevideo"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Uruguay"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Raúl_Fernando_Sendic_Rodríguez",
"property": "leader",
"subject": "Uruguay"
},
{
"object": "Montevideo",
"property": "deathPlace",
"subject": "Alfredo_Zitarrosa"
},
{
"object": "Uruguay",
"property": "country",
"subject": "Montevideo"
},
{
"object": "Daniel_Martínez_(politician)",
"property": "leaderName",
"subject": "Montevideo"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Uruguay"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id1862"
}
}
]
}
|
AdaMix/NLG/data/webnlg_challenge_2017/test.json/0
|
{
"file_path": "AdaMix/NLG/data/webnlg_challenge_2017/test.json",
"repo_id": "AdaMix",
"token_count": 3935629
}
| 27 |
. ./venv/bin/activate
sudo apt install default-jre -y
seed=110
vv=lora_only
while [[ $# -gt 0 ]]
do
key="$1"
case $key in
--seed)
seed=$2
shift
shift
;;
--vv)
vv=$2
shift
shift
;;
esac
done
python -m torch.distributed.launch --nproc_per_node=16 src/gpt2_beam.py \
--data ./data/webnlg_challenge_2017/test.jsonl \
--batch_size 1 \
--seq_len 256 \
--eval_len 64 \
--model_card gpt2.md \
--init_checkpoint ./trained_models/GPT2_M/webnlg/$seed/$vv/model.final.pt \
--platform local \
--lora_dim 4 \
--lora_alpha 32 \
--beam 10 \
--length_penalty 0.8 \
--no_repeat_ngram_size 4 \
--repetition_penalty 1.0 \
--eos_token_id 628 \
--work_dir ./trained_models/GPT2_M/webnlg/$seed/$vv \
--output_file predict.jsonl \
--lora_only 1
python src/gpt2_decode.py \
--vocab ./vocab \
--sample_file ./trained_models/GPT2_M/webnlg/$seed/$vv/predict.jsonl \
--input_file ./data/webnlg_challenge_2017/test_formatted.jsonl \
--ref_type webnlg \
--ref_num 6 \
--output_ref_file eval/GenerationEval/data/references_webnlg \
--output_pred_file eval/GenerationEval/data/hypothesis_webnlg \
--tokenize --lower
cd ./eval/GenerationEval/
python eval.py \
-R data/references_webnlg/reference \
-H data/hypothesis_webnlg \
-nr 6 \
-m bleu,meteor,ter
cd ../..
|
AdaMix/NLG/run_eval_webnlg_lora_only.sh/0
|
{
"file_path": "AdaMix/NLG/run_eval_webnlg_lora_only.sh",
"repo_id": "AdaMix",
"token_count": 650
}
| 28 |
# ------------------------------------------------------------------------------------------
# Copyright (c). All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import argparse
import time
import math
import os, sys
import json
import itertools
from typing import Callable, Dict, Iterable, List, Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from torch.utils.data import DataLoader
import torch.nn.functional as F
torch.set_printoptions(threshold=100000)
import numpy as np
from gpu import (
add_gpu_params,
parse_gpu,
distributed_opt,
distributed_gather,
distributed_sync,
cleanup
)
from exp_utils import create_exp_dir
from data_utils import FT_Dataset
from model import GPT2Config, GPT2LMModel
parser = argparse.ArgumentParser(description='PyTorch GPT2 beam decoding')
add_gpu_params(parser)
parser.add_argument('--data', type=str, default='../data/wikitext-103',
help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=10,
help='batch size')
parser.add_argument('--seq_len', type=int, default=512,
help='number of tokens to predict')
parser.add_argument('--eval_len', type=int, default=256,
help='evaluation length')
parser.add_argument('--min_length', type=int, default=0,
help='minimum generation length')
parser.add_argument('--model_card', default='gpt2.sm', choices=['gpt2.sm', 'gpt2.md', 'gpt2.lg'],
help='model names')
parser.add_argument('--init_checkpoint', default=None, type=str, help='initial checkpoint')
parser.add_argument('--lora_dim', type=int, default=0, help='lora attn dimension')
parser.add_argument('--lora_alpha', type=int, default=128, help='lora attn alpha')
parser.add_argument('--work_dir', type=str, default=os.getenv('PT_OUTPUT_DIR', 'gpt2_model'),
help='working folder')
parser.add_argument('--beam', type=int, default=1, help='beam search size')
parser.add_argument('--length_penalty', type=float, default=1.0, help='length penalty')
parser.add_argument('--no_repeat_ngram_size', type=int, default=4, help='no_repeat_ngram_size')
parser.add_argument('--repetition_penalty', type=float, default=1.0, help='repetition_penalty')
parser.add_argument('--eos_token_id', action='append', type=int, default=[50256],
help='eos token id')
parser.add_argument('--output_file', type=str, default='beam_prediction.jsonl',
help='output file name')
parser.add_argument('--n_experts', type=int, default=1, help='number of experts')
parser.add_argument('--share_A', type=int, default=0, help='share LoRA A')
parser.add_argument('--share_B', type=int, default=0, help='share LoRA B')
parser.add_argument('--lora_only', type=int, default=0, help='Run Lora without Adamix')
parser.add_argument('--adamix_only', type=int, default=0, help='Run Adamix without Lora')
def print_args(args):
if args.rank == 0:
print('=' * 100)
for k, v in args.__dict__.items():
print(' - {} : {}'.format(k, v))
print('=' * 100)
def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:
return tuple(layer_past.index_select(1, beam_idx).contiguous().detach() for layer_past in past)
def _calc_banned_ngram_tokens(
prev_input_ids: Tensor,
num_hypos: int,
no_repeat_ngram_size: int,
cur_len: int
) -> None:
"""Copied from fairseq for no_repeat_ngram in beam_search"""
if cur_len + 1 < no_repeat_ngram_size:
# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return [[] for _ in range(num_hypos)]
generated_ngrams = [{} for _ in range(num_hypos)]
for idx in range(num_hypos):
gen_tokens = prev_input_ids[idx].tolist()
generated_ngram = generated_ngrams[idx]
for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
prev_ngram_tuple = tuple(ngram[:-1])
generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
def _get_generated_ngrams(hypo_idx):
# Before decoding the next token, prevent decoding of ngrams that have already appeared
start_idx = cur_len + 1 - no_repeat_ngram_size
ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
return generated_ngrams[hypo_idx].get(ngram_idx, [])
banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
return banned_tokens
def _enforce_repetition_penalty_(
lprobs,
batch_size,
num_beams,
prev_output_tokens,
repetition_penalty
):
"""repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). """
for i in range(batch_size * num_beams):
print('prev_output_tokens.shape', prev_output_tokens.shape)
print('prev_output_tokens[i].shape', prev_output_tokens[i].shape)
for previous_token in set(prev_output_tokens[i].tolist()):
# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if lprobs[i, previous_token] < 0:
lprobs[i, previous_token] *= repetition_penalty
else:
lprobs[i, previous_token] /= repetition_penalty
def _postprocess_next_token_scores(
scores,
history,
cur_len,
batch_size,
num_beams,
repetition_penalty=1.0,
no_repeat_ngram_size=4,
bad_words_ids=None,
min_length=0,
max_length=100,
eos_token_id=None,
):
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0 and history is not None:
_enforce_repetition_penalty_(scores, batch_size, num_beams, history, repetition_penalty)
# score: batch_size * beam, vocab
# set eos token prob to zero if min_length is not reached
if eos_token_id is not None and cur_len < min_length:
for eos in eos_token_id:
scores[:, eos] = -float("inf")
if no_repeat_ngram_size > 0 and history is not None:
# calculate a list of banned tokens to prevent repetitively generating the same ngrams
num_batch_hypotheses = batch_size * num_beams
# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
banned_batch_tokens = _calc_banned_ngram_tokens(
history, num_batch_hypotheses, no_repeat_ngram_size, cur_len
)
for i, banned_tokens in enumerate(banned_batch_tokens):
scores[i, banned_tokens] = -float("inf")
return scores
def _add_beam_candidate(
best_score,
best_sequence,
batch_size,
num_beams,
beam_scores,
history,
eos_token_id=None
):
last_tokens = history[:, -1]
for _i in range(batch_size * num_beams):
if eos_token_id is None or last_tokens[_i] in eos_token_id:
cur_len = history.shape[-1]
_score = beam_scores.view(-1)[_i] / cur_len ** args.length_penalty
batch_id = _i // num_beams
if not batch_id in best_score or best_score[batch_id] < _score:
best_score[batch_id] = _score
best_sequence[batch_id][:cur_len] = history[_i]
beam_scores.view(-1)[_i] = -float("inf")
def beam(model, data_iter, args):
model.eval()
total_loss = 0.
start_time = time.time()
all_predictions = {}
with torch.no_grad():
for idx, data in enumerate(data_iter):
data = {key: value for key, value in data.items()}
_id = data['id'].to(args.device)
_query = data['query'].to(args.device)
_query_len = data['query_len'].to(args.device)
## local adaptation start.
## local adaptation end.
output = None
score = None
batch_size = _id.size(0)
num_beams = args.beam
length_penalty = args.length_penalty
_batch = torch.arange(0, _id.size(0), device=args.device, dtype=torch.long)
past = None
len_past = None
_query = _query.repeat(1, num_beams).view(batch_size * num_beams, -1)
_query_len = _query_len.unsqueeze(-1).repeat(1, num_beams).view(-1)
_bbatch = _batch.unsqueeze(-1).repeat(1, num_beams).view(-1)
# scores for each sentence in the beam
beam_scores = torch.zeros(
(batch_size, num_beams), dtype=torch.float, device=_query.device
)
best_sequence = torch.zeros(
(batch_size, args.eval_len), dtype=torch.long, device=_query.device
)
best_score = {}
history = None
with torch.no_grad():
for i in range(0, args.eval_len):
if i == 0:
logits, past = model(_query)
logits = logits[_bbatch, (_query_len-1).long(), :] # batch_size * beam, vocab
else:
#print('token_id.shape', token_id.shape, token_id)
#print('past.shape', past[0].shape)
#print('len_past.shape', len_past.shape, len_past)
logits, past = model(token_id, past=past, len_past=len_past)
logits = logits[:, -1, :] # batch_size * beam, vocab
logits = _postprocess_next_token_scores(
logits,
history,
i,
batch_size,
num_beams,
repetition_penalty=args.repetition_penalty,
no_repeat_ngram_size=args.no_repeat_ngram_size,
min_length=args.min_length,
eos_token_id=args.eos_token_id,
)
softmax_probs = F.softmax(logits, dim=-1)
##_prob, _w_idx = torch.topk(softmax_probs, num_beams) # batch_size, beam
vocab_size = softmax_probs.shape[-1]
_logprob = torch.log(softmax_probs) # batch_size * beam, vocab
if i == 0:
next_scores = _logprob.view(batch_size, num_beams, -1)[:, 0, :] # batch_size, vocab
else:
next_scores = beam_scores.unsqueeze(-1) + _logprob.view(batch_size, num_beams, -1)
next_scores = next_scores.view(batch_size, -1) # batch_size, beam * vocab
next_scores, next_tokens = torch.topk(
next_scores, num_beams, dim=1, largest=True, sorted=True
) # batch_size, num_beams
beam_id = (next_tokens // vocab_size).view(-1) # batch_size * num_beams
token_id = (next_tokens % vocab_size).view(-1).unsqueeze(-1) # batch_size, num_beams
beam_idx = beam_id.view(batch_size, num_beams) + (_batch * num_beams).unsqueeze(-1)
past = _reorder_cache(past, beam_idx.view(-1))
beam_scores = next_scores # batch_size, num_beams
len_past = (_query_len + i).long()
if history is None:
history = token_id.detach()
else:
history = torch.cat((history[beam_idx.view(-1)], token_id.detach()), dim=1).detach()
_add_beam_candidate(
best_score, best_sequence, batch_size, num_beams, beam_scores, history,
eos_token_id=args.eos_token_id
)
_add_beam_candidate(
best_score, best_sequence, batch_size, num_beams, beam_scores, history
)
with torch.no_grad():
_id = distributed_gather(args, _id)
output = distributed_gather(args, best_sequence)
#score = distributed_gather(args, score)
distributed_sync(args)
if args.rank == 0:
_id = _id.view(-1).cpu()
output = output.view(-1, output.shape[-1]).cpu()
#score = score.view(-1, score.shape[-1]).cpu()
for _b in range(0, _id.shape[-1]):
_i = int(_id[_b].item())
all_predictions[_i] = {}
all_predictions[_i]['id'] = _i
all_predictions[_i]['predict'] = output[_b].tolist()
#all_predictions[_i]['score'] = score[_b].tolist()
if idx % 10 == 0:
print('inference samples', idx)
if args.rank == 0:
pred_file = os.path.join(args.work_dir, args.output_file)
print('saving prediction file', pred_file)
with open(pred_file, 'w') as writer:
for _i in all_predictions:
writer.write(json.dumps(all_predictions[_i]) + '\n')
if __name__ == '__main__':
args = parser.parse_args()
parse_gpu(args)
print_args(args)
if args.rank == 0:
args.logging = create_exp_dir(args.work_dir)
valid_data = FT_Dataset(
args.data, args.batch_size, args.seq_len, args.eval_len,
)
valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_data)
valid_loader = DataLoader(
valid_data, batch_size=args.batch_size, num_workers=0, shuffle=False,
pin_memory=False, drop_last=False, sampler=valid_sampler
)
if args.model_card == 'gpt2.sm':
config = GPT2Config(
n_embd=768, n_layer=12, n_head=12,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
n_experts=args.n_experts,
share_A=args.share_A,
share_B=args.share_B,
lora_only=args.lora_only,
adamix_only=args.adamix_only
)
elif args.model_card == 'gpt2.md':
config = GPT2Config(
n_embd=1024, n_layer=24, n_head=16,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
n_experts=args.n_experts,
share_A=args.share_A,
share_B=args.share_B,
lora_only=args.lora_only,
adamix_only=args.adamix_only
)
elif args.model_card == 'gpt2.lg':
config = GPT2Config(
n_embd=1280, n_layer=36, n_head=20,
lora_attn_dim=args.lora_dim, lora_attn_alpha=args.lora_alpha,
n_experts=args.n_experts,
share_A=args.share_A,
share_B=args.share_B,
lora_only=args.lora_only,
adamix_only=args.adamix_only
)
lm_net = GPT2LMModel(config)
if args.init_checkpoint is not None:
print('loading model pretrained weight.')
if args.model_card == 'gpt2.md':
lm_net.load_weight(torch.load("./pretrained_checkpoints/gpt2-medium-pytorch_model.bin", map_location=torch.device('cpu')))
elif args.model_card == 'gpt2.lg':
lm_net.load_weight(torch.load("./pretrained_checkpoints/gpt2-large-pytorch_model.bin", map_location=torch.device('cpu')))
cp = torch.load(args.init_checkpoint, map_location=torch.device('cpu'))
lm_net.load_weight(cp)
lm_net = lm_net.cuda()
print('model sampling ...')
beam(lm_net, valid_loader, args)
distributed_sync(args)
print('cleanup dist ...')
cleanup(args)
|
AdaMix/NLG/src/gpt2_beam.py/0
|
{
"file_path": "AdaMix/NLG/src/gpt2_beam.py",
"repo_id": "AdaMix",
"token_count": 7930
}
| 29 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Glossary
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
General terms
-----------------------------------------------------------------------------------------------------------------------
- autoencoding models: see MLM
- autoregressive models: see CLM
- CLM: causal language modeling, a pretraining task where the model reads the texts in order and has to predict the
next word. It's usually done by reading the whole sentence but using a mask inside the model to hide the future
tokens at a certain timestep.
- deep learning: machine learning algorithms which uses neural networks with several layers.
- MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
by masking some tokens randomly, and has to predict the original text.
- multimodal: a task that combines texts with another kind of inputs (for instance images).
- NLG: natural language generation, all tasks related to generating text (for instance talk with transformers,
translation).
- NLP: natural language processing, a generic way to say "deal with texts".
- NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
the whole text, individual words).
- pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
masking some words and trying to predict them (see MLM).
- RNN: recurrent neural network, a type of model that uses a loop over a layer to process texts.
- self-attention: each element of the input finds out which other elements of the input they should attend to.
- seq2seq or sequence-to-sequence: models that generate a new sequence from an input, like translation models, or
summarization models (such as :doc:`Bart </model_doc/bart>` or :doc:`T5 </model_doc/t5>`).
- token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
or a punctuation symbol.
- transformer: self-attention based deep learning model architecture.
Model inputs
-----------------------------------------------------------------------------------------------------------------------
Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
detailed here alongside usage examples.
.. _input-ids:
Input IDs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The input ids are often the only required parameters to be passed to the model as input. *They are token indices,
numerical representations of tokens building the sequences that will be used as input by the model*.
Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
tokenizer, which is a `WordPiece <https://arxiv.org/pdf/1609.08144.pdf>`__ tokenizer:
.. code-block::
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence = "A Titan RTX has 24GB of VRAM"
The tokenizer takes care of splitting the sequence into tokens available in the tokenizer vocabulary.
.. code-block::
>>> tokenized_sequence = tokenizer.tokenize(sequence)
The tokens are either words or subwords. Here for instance, "VRAM" wasn't in the model vocabulary, so it's been split
in "V", "RA" and "M". To indicate those tokens are not separate words but parts of the same word, a double-hash prefix
is added for "RA" and "M":
.. code-block::
>>> print(tokenized_sequence)
['A', 'Titan', 'R', '##T', '##X', 'has', '24', '##GB', 'of', 'V', '##RA', '##M']
These tokens can then be converted into IDs which are understandable by the model. This can be done by directly feeding
the sentence to the tokenizer, which leverages the Rust implementation of `huggingface/tokenizers
<https://github.com/huggingface/tokenizers>`__ for peak performance.
.. code-block::
>>> inputs = tokenizer(sequence)
The tokenizer returns a dictionary with all the arguments necessary for its corresponding model to work properly. The
token indices are under the key "input_ids":
.. code-block::
>>> encoded_sequence = inputs["input_ids"]
>>> print(encoded_sequence)
[101, 138, 18696, 155, 1942, 3190, 1144, 1572, 13745, 1104, 159, 9664, 2107, 102]
Note that the tokenizer automatically adds "special tokens" (if the associated model relies on them) which are special
IDs the model sometimes uses.
If we decode the previous sequence of ids,
.. code-block::
>>> decoded_sequence = tokenizer.decode(encoded_sequence)
we will see
.. code-block::
>>> print(decoded_sequence)
[CLS] A Titan RTX has 24GB of VRAM [SEP]
because this is the way a :class:`~transformers.BertModel` is going to expect its inputs.
.. _attention-mask:
Attention mask
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The attention mask is an optional argument used when batching sequences together. This argument indicates to the model
which tokens should be attended to, and which should not.
For example, consider these two sequences:
.. code-block::
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence_a = "This is a short sequence."
>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
>>> encoded_sequence_a = tokenizer(sequence_a)["input_ids"]
>>> encoded_sequence_b = tokenizer(sequence_b)["input_ids"]
The encoded versions have different lengths:
.. code-block::
>>> len(encoded_sequence_a), len(encoded_sequence_b)
(8, 19)
Therefore, we can't put them together in the same tensor as-is. The first sequence needs to be padded up to the length
of the second one, or the second one needs to be truncated down to the length of the first one.
In the first case, the list of IDs will be extended by the padding indices. We can pass a list to the tokenizer and ask
it to pad like this:
.. code-block::
>>> padded_sequences = tokenizer([sequence_a, sequence_b], padding=True)
We can see that 0s have been added on the right of the first sentence to make it the same length as the second one:
.. code-block::
>>> padded_sequences["input_ids"]
[[101, 1188, 1110, 170, 1603, 4954, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1188, 1110, 170, 1897, 1263, 4954, 119, 1135, 1110, 1120, 1655, 2039, 1190, 1103, 4954, 138, 119, 102]]
This can then be converted into a tensor in PyTorch or TensorFlow. The attention mask is a binary tensor indicating the
position of the padded indices so that the model does not attend to them. For the :class:`~transformers.BertTokenizer`,
:obj:`1` indicates a value that should be attended to, while :obj:`0` indicates a padded value. This attention mask is
in the dictionary returned by the tokenizer under the key "attention_mask":
.. code-block::
>>> padded_sequences["attention_mask"]
[[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
.. _token-type-ids:
Token Type IDs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Some models' purpose is to do sequence classification or question answering. These require two different sequences to
be joined in a single "input_ids" entry, which usually is performed with the help of special tokens, such as the
classifier (``[CLS]``) and separator (``[SEP]``) tokens. For example, the BERT model builds its two sequence input as
such:
.. code-block::
>>> # [CLS] SEQUENCE_A [SEP] SEQUENCE_B [SEP]
We can use our tokenizer to automatically generate such a sentence by passing the two sequences to ``tokenizer`` as two
arguments (and not a list, like before) like this:
.. code-block::
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
>>> sequence_a = "HuggingFace is based in NYC"
>>> sequence_b = "Where is HuggingFace based?"
>>> encoded_dict = tokenizer(sequence_a, sequence_b)
>>> decoded = tokenizer.decode(encoded_dict["input_ids"])
which will return:
.. code-block::
>>> print(decoded)
[CLS] HuggingFace is based in NYC [SEP] Where is HuggingFace based? [SEP]
This is enough for some models to understand where one sequence ends and where another begins. However, other models,
such as BERT, also deploy token type IDs (also called segment IDs). They are represented as a binary mask identifying
the two types of sequence in the model.
The tokenizer returns this mask as the "token_type_ids" entry:
.. code-block::
>>> encoded_dict['token_type_ids']
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
The first sequence, the "context" used for the question, has all its tokens represented by a :obj:`0`, whereas the
second sequence, corresponding to the "question", has all its tokens represented by a :obj:`1`.
Some models, like :class:`~transformers.XLNetModel` use an additional token represented by a :obj:`2`.
.. _position-ids:
Position IDs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Contrary to RNNs that have the position of each token embedded within them, transformers are unaware of the position of
each token. Therefore, the position IDs (``position_ids``) are used by the model to identify each token's position in
the list of tokens.
They are an optional parameter. If no ``position_ids`` are passed to the model, the IDs are automatically created as
absolute positional embeddings.
Absolute positional embeddings are selected in the range ``[0, config.max_position_embeddings - 1]``. Some models use
other types of positional embeddings, such as sinusoidal position embeddings or relative position embeddings.
.. _labels:
Labels
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The labels are an optional argument which can be passed in order for the model to compute the loss itself. These labels
should be the expected prediction of the model: it will use the standard loss in order to compute the loss between its
predictions and the expected value (the label).
These labels are different according to the model head, for example:
- For sequence classification models (e.g., :class:`~transformers.BertForSequenceClassification`), the model expects a
tensor of dimension :obj:`(batch_size)` with each value of the batch corresponding to the expected label of the
entire sequence.
- For token classification models (e.g., :class:`~transformers.BertForTokenClassification`), the model expects a tensor
of dimension :obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual
token.
- For masked language modeling (e.g., :class:`~transformers.BertForMaskedLM`), the model expects a tensor of dimension
:obj:`(batch_size, seq_length)` with each value corresponding to the expected label of each individual token: the
labels being the token ID for the masked token, and values to be ignored for the rest (usually -100).
- For sequence to sequence tasks,(e.g., :class:`~transformers.BartForConditionalGeneration`,
:class:`~transformers.MBartForConditionalGeneration`), the model expects a tensor of dimension :obj:`(batch_size,
tgt_seq_length)` with each value corresponding to the target sequences associated with each input sequence. During
training, both `BART` and `T5` will make the appropriate `decoder_input_ids` and decoder attention masks internally.
They usually do not need to be supplied. This does not apply to models leveraging the Encoder-Decoder framework. See
the documentation of each model for more information on each specific model's labels.
The base models (e.g., :class:`~transformers.BertModel`) do not accept labels, as these are the base transformer
models, simply outputting features.
.. _decoder-input-ids:
Decoder input IDs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This input is specific to encoder-decoder models, and contains the input IDs that will be fed to the decoder. These
inputs should be used for sequence to sequence tasks, such as translation or summarization, and are usually built in a
way specific to each model.
Most encoder-decoder models (BART, T5) create their :obj:`decoder_input_ids` on their own from the :obj:`labels`. In
such models, passing the :obj:`labels` is the preferred way to handle training.
Please check each model's docs to see how they handle these input IDs for sequence to sequence training.
.. _feed-forward-chunking:
Feed Forward Chunking
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
``bert-base-uncased``).
For an input of size ``[batch_size, sequence_length]``, the memory required to store the intermediate feed forward
embeddings ``[batch_size, sequence_length, config.intermediate_size]`` can account for a large fraction of the memory
use. The authors of `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ noticed that since the
computation is independent of the ``sequence_length`` dimension, it is mathematically equivalent to compute the output
embeddings of both feed forward layers ``[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n``
individually and concat them afterward to ``[batch_size, sequence_length, config.hidden_size]`` with ``n =
sequence_length``, which trades increased computation time against reduced memory use, but yields a mathematically
**equivalent** result.
For models employing the function :func:`~.transformers.apply_chunking_to_forward`, the ``chunk_size`` defines the
number of output embeddings that are computed in parallel and thus defines the trade-off between memory and time
complexity. If ``chunk_size`` is set to 0, no feed forward chunking is done.
|
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|
{
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"repo_id": "AdaMix",
"token_count": 4043
}
| 30 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Custom Layers and Utilities
-----------------------------------------------------------------------------------------------------------------------
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
Pytorch custom modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_utils.Conv1D
.. autoclass:: transformers.modeling_utils.PoolerStartLogits
:members: forward
.. autoclass:: transformers.modeling_utils.PoolerEndLogits
:members: forward
.. autoclass:: transformers.modeling_utils.PoolerAnswerClass
:members: forward
.. autoclass:: transformers.modeling_utils.SquadHeadOutput
.. autoclass:: transformers.modeling_utils.SQuADHead
:members: forward
.. autoclass:: transformers.modeling_utils.SequenceSummary
:members: forward
PyTorch Helper Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.apply_chunking_to_forward
.. autofunction:: transformers.modeling_utils.find_pruneable_heads_and_indices
.. autofunction:: transformers.modeling_utils.prune_layer
.. autofunction:: transformers.modeling_utils.prune_conv1d_layer
.. autofunction:: transformers.modeling_utils.prune_linear_layer
TensorFlow custom layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_tf_utils.TFConv1D
.. autoclass:: transformers.modeling_tf_utils.TFSharedEmbeddings
:members: call
.. autoclass:: transformers.modeling_tf_utils.TFSequenceSummary
:members: call
TensorFlow loss functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.modeling_tf_utils.TFCausalLanguageModelingLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFMultipleChoiceLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFQuestionAnsweringLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFSequenceClassificationLoss
:members:
.. autoclass:: transformers.modeling_tf_utils.TFTokenClassificationLoss
:members:
TensorFlow Helper Functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.modeling_tf_utils.get_initializer
.. autofunction:: transformers.modeling_tf_utils.keras_serializable
.. autofunction:: transformers.modeling_tf_utils.shape_list
|
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{
"file_path": "AdaMix/docs/source/internal/modeling_utils.rst",
"repo_id": "AdaMix",
"token_count": 926
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| 31 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
ALBERT
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The ALBERT model was proposed in `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
<https://arxiv.org/abs/1909.11942>`__ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma,
Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
speed of BERT:
- Splitting the embedding matrix into two smaller matrices.
- Using repeating layers split among groups.
The abstract from the paper is the following:
*Increasing model size when pretraining natural language representations often results in improved performance on
downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
SQuAD benchmarks while having fewer parameters compared to BERT-large.*
Tips:
- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
than the left.
- ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
number of (repeating) layers.
The original code can be found `here <https://github.com/google-research/ALBERT>`__.
AlbertConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertConfig
:members:
AlbertTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertTokenizer
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
create_token_type_ids_from_sequences, save_vocabulary
AlbertTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertTokenizerFast
:members:
Albert specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.models.albert.modeling_albert.AlbertForPreTrainingOutput
:members:
.. autoclass:: transformers.models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput
:members:
AlbertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertModel
:members: forward
AlbertForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForPreTraining
:members: forward
AlbertForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForMaskedLM
:members: forward
AlbertForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForSequenceClassification
:members: forward
AlbertForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForMultipleChoice
:members:
AlbertForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForTokenClassification
:members: forward
AlbertForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForQuestionAnswering
:members: forward
TFAlbertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertModel
:members: call
TFAlbertForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForPreTraining
:members: call
TFAlbertForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForMaskedLM
:members: call
TFAlbertForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForSequenceClassification
:members: call
TFAlbertForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForMultipleChoice
:members: call
TFAlbertForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForTokenClassification
:members: call
TFAlbertForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForQuestionAnswering
:members: call
|
AdaMix/docs/source/model_doc/albert.rst/0
|
{
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"repo_id": "AdaMix",
"token_count": 1444
}
| 32 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
DistilBERT
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The DistilBERT model was proposed in the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__, and the paper `DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__. DistilBERT is a
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
`bert-base-uncased`, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
understanding benchmark.
The abstract from the paper is the following:
*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
study.*
Tips:
- DistilBERT doesn't have :obj:`token_type_ids`, you don't need to indicate which token belongs to which segment. Just
separate your segments with the separation token :obj:`tokenizer.sep_token` (or :obj:`[SEP]`).
- DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
necessary though, just let us know if you need this option.
The original code can be found `here
<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
DistilBertConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertConfig
:members:
DistilBertTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertTokenizer
:members:
DistilBertTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertTokenizerFast
:members:
DistilBertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertModel
:members: forward
DistilBertForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertForMaskedLM
:members: forward
DistilBertForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertForSequenceClassification
:members: forward
DistilBertForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertForMultipleChoice
:members: forward
DistilBertForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertForTokenClassification
:members: forward
DistilBertForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.DistilBertForQuestionAnswering
:members: forward
TFDistilBertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertModel
:members: call
TFDistilBertForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertForMaskedLM
:members: call
TFDistilBertForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertForSequenceClassification
:members: call
TFDistilBertForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertForMultipleChoice
:members: call
TFDistilBertForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertForTokenClassification
:members: call
TFDistilBertForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFDistilBertForQuestionAnswering
:members: call
|
AdaMix/docs/source/model_doc/distilbert.rst/0
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"repo_id": "AdaMix",
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}
| 33 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
MarianMT
-----------------------------------------------------------------------------------------------------------------------
**Bugs:** If you see something strange, file a `Github Issue
<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
and assign @patrickvonplaten.
Translations should be similar, but not identical to output in the test set linked to in each model card.
Implementation Notes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Each model is about 298 MB on disk, there are more than 1,000 models.
- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
- Models were originally trained by `Jörg Tiedemann
<https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian
<https://marian-nmt.github.io/>`__ C++ library, which supports fast training and translation.
- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented
in a model card.
- The 80 opus models that require BPE preprocessing are not supported.
- The modeling code is the same as :class:`~transformers.BartForConditionalGeneration` with a few minor modifications:
- static (sinusoid) positional embeddings (:obj:`MarianConfig.static_position_embeddings=True`)
- no layernorm_embedding (:obj:`MarianConfig.normalize_embedding=False`)
- the model starts generating with :obj:`pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
:obj:`<s/>`),
- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``.
Naming
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- All model names use the following format: :obj:`Helsinki-NLP/opus-mt-{src}-{tgt}`
- The language codes used to name models are inconsistent. Two digit codes can usually be found `here
<https://developers.google.com/admin-sdk/directory/v1/languages>`__, three digit codes require googling "language
code {code}".
- Codes formatted like :obj:`es_AR` are usually :obj:`code_{region}`. That one is Spanish from Argentina.
- The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second
group use a combination of ISO-639-5 codes and ISO-639-2 codes.
Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Since Marian models are smaller than many other translation models available in the library, they can be useful for
fine-tuning experiments and integration tests.
- `Fine-tune on GPU
<https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_enro_teacher.sh>`__
- `Fine-tune on GPU with pytorch-lightning
<https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_no_teacher.sh>`__
Multilingual Models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- All model names use the following format: :obj:`Helsinki-NLP/opus-mt-{src}-{tgt}`:
- If a model can output multiple languages, and you should specify a language code by prepending the desired output
language to the :obj:`src_text`.
- You can see a models's supported language codes in its model card, under target constituents, like in `opus-mt-en-roa
<https://huggingface.co/Helsinki-NLP/opus-mt-en-roa>`__.
- Note that if a model is only multilingual on the source side, like :obj:`Helsinki-NLP/opus-mt-roa-en`, no language
codes are required.
New multi-lingual models from the `Tatoeba-Challenge repo <https://github.com/Helsinki-NLP/Tatoeba-Challenge>`__
require 3 character language codes:
.. code-block:: python
>>> from transformers import MarianMTModel, MarianTokenizer
>>> src_text = [
... '>>fra<< this is a sentence in english that we want to translate to french',
... '>>por<< This should go to portuguese',
... '>>esp<< And this to Spanish'
>>> ]
>>> model_name = 'Helsinki-NLP/opus-mt-en-roa'
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
>>> print(tokenizer.supported_language_codes)
['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']
>>> model = MarianMTModel.from_pretrained(model_name)
>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
>>> [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
["c'est une phrase en anglais que nous voulons traduire en français",
'Isto deve ir para o português.',
'Y esto al español']
Here is the code to see all available pretrained models on the hub:
.. code-block:: python
from transformers.hf_api import HfApi
model_list = HfApi().model_list()
org = "Helsinki-NLP"
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
suffix = [x.split('/')[1] for x in model_ids]
old_style_multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
Old Style Multi-Lingual Models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
group:
.. code-block:: python
['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
'Helsinki-NLP/opus-mt-ROMANCE-en',
'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
'Helsinki-NLP/opus-mt-de-ZH',
'Helsinki-NLP/opus-mt-en-CELTIC',
'Helsinki-NLP/opus-mt-en-ROMANCE',
'Helsinki-NLP/opus-mt-es-NORWAY',
'Helsinki-NLP/opus-mt-fi-NORWAY',
'Helsinki-NLP/opus-mt-fi-ZH',
'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
'Helsinki-NLP/opus-mt-sv-NORWAY',
'Helsinki-NLP/opus-mt-sv-ZH']
GROUP_MEMBERS = {
'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
}
Example of translating english to many romance languages, using old-style 2 character language codes
.. code-block::python
>>> from transformers import MarianMTModel, MarianTokenizer
>>> src_text = [
... '>>fr<< this is a sentence in english that we want to translate to french',
... '>>pt<< This should go to portuguese',
... '>>es<< And this to Spanish'
>>> ]
>>> model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
>>> model = MarianMTModel.from_pretrained(model_name)
>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
>>> tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
["c'est une phrase en anglais que nous voulons traduire en français",
'Isto deve ir para o português.',
'Y esto al español']
MarianConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianConfig
:members:
MarianTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianTokenizer
:members: as_target_tokenizer
MarianModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianModel
:members: forward
MarianMTModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianMTModel
:members: forward
MarianForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianForCausalLM
:members: forward
TFMarianModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFMarianModel
:members: call
TFMarianMTModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFMarianMTModel
:members: call
|
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|
{
"file_path": "AdaMix/docs/source/model_doc/marian.rst",
"repo_id": "AdaMix",
"token_count": 3273
}
| 34 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Transformer XL
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Transformer-XL model was proposed in `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
<https://arxiv.org/abs/1901.02860>`__ by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
inputs and outputs (tied).
The abstract from the paper is the following:
*Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a
novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the
context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450%
longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+
times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of
bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn
Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
coherent, novel text articles with thousands of tokens.*
Tips:
- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right. The
original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
- Transformer-XL is one of the few models that has no sequence length limit.
The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`__.
TransfoXLConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TransfoXLConfig
:members:
TransfoXLTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TransfoXLTokenizer
:members: save_vocabulary
TransfoXL specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.models.transfo_xl.modeling_transfo_xl.TransfoXLModelOutput
:members:
.. autoclass:: transformers.models.transfo_xl.modeling_transfo_xl.TransfoXLLMHeadModelOutput
:members:
.. autoclass:: transformers.models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLModelOutput
:members:
.. autoclass:: transformers.models.transfo_xl.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
:members:
TransfoXLModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TransfoXLModel
:members: forward
TransfoXLLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TransfoXLLMHeadModel
:members: forward
TransfoXLForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TransfoXLForSequenceClassification
:members: forward
TFTransfoXLModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFTransfoXLModel
:members: call
TFTransfoXLLMHeadModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFTransfoXLLMHeadModel
:members: call
TFTransfoXLForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFTransfoXLForSequenceClassification
:members: call
Internal Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AdaptiveEmbedding
.. autoclass:: transformers.TFAdaptiveEmbedding
|
AdaMix/docs/source/model_doc/transformerxl.rst/0
|
{
"file_path": "AdaMix/docs/source/model_doc/transformerxl.rst",
"repo_id": "AdaMix",
"token_count": 1282
}
| 35 |
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Exporting transformers models
***********************************************************************************************************************
ONNX / ONNXRuntime
=======================================================================================================================
Projects `ONNX (Open Neural Network eXchange) <http://onnx.ai>`_ and `ONNXRuntime (ORT)
<https://microsoft.github.io/onnxruntime/>`_ are part of an effort from leading industries in the AI field to provide a
unified and community-driven format to store and, by extension, efficiently execute neural network leveraging a variety
of hardware and dedicated optimizations.
Starting from transformers v2.10.0 we partnered with ONNX Runtime to provide an easy export of transformers models to
the ONNX format. You can have a look at the effort by looking at our joint blog post `Accelerate your NLP pipelines
using Hugging Face Transformers and ONNX Runtime
<https://medium.com/microsoftazure/accelerate-your-nlp-pipelines-using-hugging-face-transformers-and-onnx-runtime-2443578f4333>`_.
Exporting a model is done through the script `convert_graph_to_onnx.py` at the root of the transformers sources. The
following command shows how easy it is to export a BERT model from the library, simply run:
.. code-block:: bash
python convert_graph_to_onnx.py --framework <pt, tf> --model bert-base-cased bert-base-cased.onnx
The conversion tool works for both PyTorch and Tensorflow models and ensures:
* The model and its weights are correctly initialized from the Hugging Face model hub or a local checkpoint.
* The inputs and outputs are correctly generated to their ONNX counterpart.
* The generated model can be correctly loaded through onnxruntime.
.. note::
Currently, inputs and outputs are always exported with dynamic sequence axes preventing some optimizations on the
ONNX Runtime. If you would like to see such support for fixed-length inputs/outputs, please open up an issue on
transformers.
Also, the conversion tool supports different options which let you tune the behavior of the generated model:
* **Change the target opset version of the generated model.** (More recent opset generally supports more operators and
enables faster inference)
* **Export pipeline-specific prediction heads.** (Allow to export model along with its task-specific prediction
head(s))
* **Use the external data format (PyTorch only).** (Lets you export model which size is above 2Gb (`More info
<https://github.com/pytorch/pytorch/pull/33062>`_))
Optimizations
-----------------------------------------------------------------------------------------------------------------------
ONNXRuntime includes some transformers-specific transformations to leverage optimized operations in the graph. Below
are some of the operators which can be enabled to speed up inference through ONNXRuntime (*see note below*):
* Constant folding
* Attention Layer fusing
* Skip connection LayerNormalization fusing
* FastGeLU approximation
Some of the optimizations performed by ONNX runtime can be hardware specific and thus lead to different performances if
used on another machine with a different hardware configuration than the one used for exporting the model. For this
reason, when using ``convert_graph_to_onnx.py`` optimizations are not enabled, ensuring the model can be easily
exported to various hardware. Optimizations can then be enabled when loading the model through ONNX runtime for
inference.
.. note::
When quantization is enabled (see below), ``convert_graph_to_onnx.py`` script will enable optimizations on the
model because quantization would modify the underlying graph making it impossible for ONNX runtime to do the
optimizations afterwards.
.. note::
For more information about the optimizations enabled by ONNXRuntime, please have a look at the `ONNXRuntime Github
<https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_.
Quantization
-----------------------------------------------------------------------------------------------------------------------
ONNX exporter supports generating a quantized version of the model to allow efficient inference.
Quantization works by converting the memory representation of the parameters in the neural network to a compact integer
format. By default, weights of a neural network are stored as single-precision float (`float32`) which can express a
wide-range of floating-point numbers with decent precision. These properties are especially interesting at training
where you want fine-grained representation.
On the other hand, after the training phase, it has been shown one can greatly reduce the range and the precision of
`float32` numbers without changing the performances of the neural network.
More technically, `float32` parameters are converted to a type requiring fewer bits to represent each number, thus
reducing the overall size of the model. Here, we are enabling `float32` mapping to `int8` values (a non-floating,
single byte, number representation) according to the following formula:
.. math::
y_{float32} = scale * x_{int8} - zero\_point
.. note::
The quantization process will infer the parameter `scale` and `zero_point` from the neural network parameters
Leveraging tiny-integers has numerous advantages when it comes to inference:
* Storing fewer bits instead of 32 bits for the `float32` reduces the size of the model and makes it load faster.
* Integer operations execute a magnitude faster on modern hardware
* Integer operations require less power to do the computations
In order to convert a transformers model to ONNX IR with quantized weights you just need to specify ``--quantize`` when
using ``convert_graph_to_onnx.py``. Also, you can have a look at the ``quantize()`` utility-method in this same script
file.
Example of quantized BERT model export:
.. code-block:: bash
python convert_graph_to_onnx.py --framework <pt, tf> --model bert-base-cased --quantize bert-base-cased.onnx
.. note::
Quantization support requires ONNX Runtime >= 1.4.0
.. note::
When exporting quantized model you will end up with two different ONNX files. The one specified at the end of the
above command will contain the original ONNX model storing `float32` weights. The second one, with ``-quantized``
suffix, will hold the quantized parameters.
TorchScript
=======================================================================================================================
.. note::
This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with
variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases,
with more code examples, a more flexible implementation, and benchmarks comparing python-based codes with compiled
TorchScript.
According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch
code". Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
their model to be re-used in other programs, such as efficiency-oriented C++ programs.
We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can be reused
in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using
TorchScript.
Exporting a model requires two things:
* a forward pass with dummy inputs.
* model instantiation with the ``torchscript`` flag.
These necessities imply several things developers should be careful about. These are detailed below.
Implications
-----------------------------------------------------------------------------------------------------------------------
TorchScript flag and tied weights
-----------------------------------------------------------------------------------------------------------------------
This flag is necessary because most of the language models in this repository have tied weights between their
``Embedding`` layer and their ``Decoding`` layer. TorchScript does not allow the export of models that have tied
weights, therefore it is necessary to untie and clone the weights beforehand.
This implies that models instantiated with the ``torchscript`` flag have their ``Embedding`` layer and ``Decoding``
layer separate, which means that they should not be trained down the line. Training would de-synchronize the two
layers, leading to unexpected results.
This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
can be safely exported without the ``torchscript`` flag.
Dummy inputs and standard lengths
-----------------------------------------------------------------------------------------------------------------------
The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used to
create the "trace" of the model.
The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
as:
``The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2``
will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest
input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
resulting in more calculations.
It is recommended to be careful of the total number of operations done on each input and to follow performance closely
when exporting varying sequence-length models.
Using TorchScript in Python
-----------------------------------------------------------------------------------------------------------------------
Below is an example, showing how to save, load models as well as how to use the trace for inference.
Saving a model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This snippet shows how to use TorchScript to export a ``BertModel``. Here the ``BertModel`` is instantiated according
to a ``BertConfig`` class and then saved to disk under the filename ``traced_bert.pt``
.. code-block:: python
from transformers import BertModel, BertTokenizer, BertConfig
import torch
enc = BertTokenizer.from_pretrained("bert-base-uncased")
# Tokenizing input text
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = enc.tokenize(text)
# Masking one of the input tokens
masked_index = 8
tokenized_text[masked_index] = '[MASK]'
indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
# Creating a dummy input
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
dummy_input = [tokens_tensor, segments_tensors]
# Initializing the model with the torchscript flag
# Flag set to True even though it is not necessary as this model does not have an LM Head.
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, torchscript=True)
# Instantiating the model
model = BertModel(config)
# The model needs to be in evaluation mode
model.eval()
# If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
# Creating the trace
traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
torch.jit.save(traced_model, "traced_bert.pt")
Loading a model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This snippet shows how to load the ``BertModel`` that was previously saved to disk under the name ``traced_bert.pt``.
We are re-using the previously initialised ``dummy_input``.
.. code-block:: python
loaded_model = torch.jit.load("traced_bert.pt")
loaded_model.eval()
all_encoder_layers, pooled_output = loaded_model(*dummy_input)
Using a traced model for inference
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Using the traced model for inference is as simple as using its ``__call__`` dunder method:
.. code-block:: python
traced_model(tokens_tensor, segments_tensors)
|
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{
"file_path": "AdaMix/docs/source/serialization.rst",
"repo_id": "AdaMix",
"token_count": 3385
}
| 36 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Fine-tuning the library models for question-answering."""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import transformers
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
AutoTokenizer,
DataCollatorWithPadding,
HfArgumentParser,
SquadDataset,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers import Trainer, TrainingArguments
from transformers.trainer_utils import is_main_process
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Prepare Question-Answering task
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=False, # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handeling
)
model = AutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
is_language_sensitive = hasattr(model.config, "lang2id")
train_dataset = (
SquadDataset(
data_args, tokenizer=tokenizer, is_language_sensitive=is_language_sensitive, cache_dir=model_args.cache_dir
)
if training_args.do_train
else None
)
eval_dataset = (
SquadDataset(
data_args,
tokenizer=tokenizer,
mode="dev",
is_language_sensitive=is_language_sensitive,
cache_dir=model_args.cache_dir,
)
if training_args.do_eval
else None
)
# Data collator
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=data_collator,
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
|
AdaMix/examples/legacy/question-answering/run_squad_trainer.py/0
|
{
"file_path": "AdaMix/examples/legacy/question-answering/run_squad_trainer.py",
"repo_id": "AdaMix",
"token_count": 2553
}
| 37 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pathlib import Path
import numpy as np
import pytest
from torch.utils.data import DataLoader
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
BERT_BASE_CASED = "bert-base-cased"
PEGASUS_XSUM = "google/pegasus-xsum"
ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"
MARIAN_TINY = "sshleifer/tiny-marian-en-de"
def _dump_articles(path: Path, articles: list):
content = "\n".join(articles)
Path(path).open("w").writelines(content)
def make_test_data_dir(tmp_dir):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
_dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
return tmp_dir
class TestAll(TestCasePlus):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
],
)
@slow
def test_seq2seq_dataset_truncation(self, tok_name):
tokenizer = AutoTokenizer.from_pretrained(tok_name)
tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
max_src_len = 4
max_tgt_len = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
src_lang, tgt_lang = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
train_dataset = Seq2SeqDataset(
tokenizer,
data_dir=tmp_dir,
type_path="train",
max_source_length=max_src_len,
max_target_length=max_tgt_len, # ignored
src_lang=src_lang,
tgt_lang=tgt_lang,
)
dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert isinstance(batch, dict)
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED])
def test_legacy_dataset_truncation(self, tok):
tokenizer = AutoTokenizer.from_pretrained(tok)
tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
trunc_target = 4
train_dataset = LegacySeq2SeqDataset(
tokenizer,
data_dir=tmp_dir,
type_path="train",
max_source_length=20,
max_target_length=trunc_target,
)
dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def test_pack_dataset(self):
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
orig_examples = tmp_dir.joinpath("train.source").open().readlines()
save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
orig_paths = {x.name for x in tmp_dir.iterdir()}
new_paths = {x.name for x in save_dir.iterdir()}
packed_examples = save_dir.joinpath("train.source").open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(packed_examples) < len(orig_examples)
assert len(packed_examples) == 1
assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
def test_dynamic_batch_size(self):
if not FAIRSEQ_AVAILABLE:
return
ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
required_batch_size_multiple = 64
batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
batch_sizes = [len(x) for x in batch_sampler]
assert len(set(batch_sizes)) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(batch_sizes) == len(ds) # no dropped or added examples
data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
failures = []
num_src_per_batch = []
for batch in data_loader:
src_shape = batch["input_ids"].shape
bs = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
num_src_tokens = np.product(batch["input_ids"].shape)
num_src_per_batch.append(num_src_tokens)
if num_src_tokens > (max_tokens * 1.1):
failures.append(num_src_tokens)
assert num_src_per_batch[0] == max(num_src_per_batch)
if failures:
raise AssertionError(f"too many tokens in {len(failures)} batches")
def test_sortish_sampler_reduces_padding(self):
ds, _, tokenizer = self._get_dataset(max_len=512)
bs = 2
sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)
naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)
pad = tokenizer.pad_token_id
def count_pad_tokens(data_loader, k="input_ids"):
return [batch[k].eq(pad).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
assert len(sortish_dl) == len(naive_dl)
def _get_dataset(self, n_obs=1000, max_len=128):
if os.getenv("USE_REAL_DATA", False):
data_dir = "examples/seq2seq/wmt_en_ro"
max_tokens = max_len * 2 * 64
if not Path(data_dir).joinpath("train.len").exists():
save_len_file(MARIAN_TINY, data_dir)
else:
data_dir = "examples/seq2seq/test_data/wmt_en_ro"
max_tokens = max_len * 4
save_len_file(MARIAN_TINY, data_dir)
tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
ds = Seq2SeqDataset(
tokenizer,
data_dir=data_dir,
type_path="train",
max_source_length=max_len,
max_target_length=max_len,
n_obs=n_obs,
)
return ds, max_tokens, tokenizer
def test_distributed_sortish_sampler_splits_indices_between_procs(self):
ds, max_tokens, tokenizer = self._get_dataset()
ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
assert ids1.intersection(ids2) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
],
)
def test_dataset_kwargs(self, tok_name):
tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
if tok_name == MBART_TINY:
train_dataset = Seq2SeqDataset(
tokenizer,
data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
type_path="train",
max_source_length=4,
max_target_length=8,
src_lang="EN",
tgt_lang="FR",
)
kwargs = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
train_dataset = Seq2SeqDataset(
tokenizer,
data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
type_path="train",
max_source_length=4,
max_target_length=8,
)
kwargs = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0
|
AdaMix/examples/legacy/seq2seq/old_test_datasets.py/0
|
{
"file_path": "AdaMix/examples/legacy/seq2seq/old_test_datasets.py",
"repo_id": "AdaMix",
"token_count": 5148
}
| 38 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from dataclasses import dataclass, field
from typing import Optional
from seq2seq_trainer import arg_to_scheduler
from transformers import TrainingArguments
logger = logging.getLogger(__name__)
@dataclass
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Parameters:
label_smoothing (:obj:`float`, `optional`, defaults to 0):
The label smoothing epsilon to apply (if not zero).
sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to SortishSamler or not. It sorts the inputs according to lenghts in-order to minimizing the padding size.
predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
"""
label_smoothing: Optional[float] = field(
default=0.0, metadata={"help": "The label smoothing epsilon to apply (if not zero)."}
)
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to SortishSamler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
adafactor: bool = field(default=False, metadata={"help": "whether to use adafactor"})
encoder_layerdrop: Optional[float] = field(
default=None, metadata={"help": "Encoder layer dropout probability. Goes into model.config."}
)
decoder_layerdrop: Optional[float] = field(
default=None, metadata={"help": "Decoder layer dropout probability. Goes into model.config."}
)
dropout: Optional[float] = field(default=None, metadata={"help": "Dropout probability. Goes into model.config."})
attention_dropout: Optional[float] = field(
default=None, metadata={"help": "Attention dropout probability. Goes into model.config."}
)
lr_scheduler: Optional[str] = field(
default="linear",
metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys())}"},
)
|
AdaMix/examples/legacy/seq2seq/seq2seq_training_args.py/0
|
{
"file_path": "AdaMix/examples/legacy/seq2seq/seq2seq_training_args.py",
"repo_id": "AdaMix",
"token_count": 889
}
| 39 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A simple launcher script for TPU training
Inspired by https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py
::
>>> python xla_spawn.py --num_cores=NUM_CORES_YOU_HAVE
YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
arguments of your training script)
"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def parse_args():
"""
Helper function parsing the command line options
@retval ArgumentParser
"""
parser = ArgumentParser(
description=(
"PyTorch TPU distributed training launch "
"helper utility that will spawn up "
"multiple distributed processes"
)
)
# Optional arguments for the launch helper
parser.add_argument("--num_cores", type=int, default=1, help="Number of TPU cores to use (1 or 8).")
# positional
parser.add_argument(
"training_script",
type=str,
help=(
"The full path to the single TPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script"
),
)
# rest from the training program
parser.add_argument("training_script_args", nargs=REMAINDER)
return parser.parse_args()
def main():
args = parse_args()
# Import training_script as a module.
script_fpath = Path(args.training_script)
sys.path.append(str(script_fpath.parent.resolve()))
mod_name = script_fpath.stem
mod = importlib.import_module(mod_name)
# Patch sys.argv
sys.argv = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores)]
xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores)
if __name__ == "__main__":
main()
|
AdaMix/examples/legacy/seq2seq/xla_spawn.py/0
|
{
"file_path": "AdaMix/examples/legacy/seq2seq/xla_spawn.py",
"repo_id": "AdaMix",
"token_count": 916
}
| 40 |
# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE."""
import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
from transformers import (
WEIGHTS_NAME,
AdamW,
AlbertConfig,
AlbertTokenizer,
BertConfig,
BertTokenizer,
get_linear_schedule_with_warmup,
)
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from transformers.trainer_utils import is_main_process
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
logger = logging.getLogger(__name__)
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer),
"albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer),
}
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")
):
# Load in optimizer and scheduler states
optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model path
global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(
" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch,
)
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(
epochs_trained,
int(args.num_train_epochs),
desc="Epoch",
disable=args.local_rank not in [-1, 0],
)
set_seed(args) # Added here for reproductibility
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
}
inputs["token_type_ids"] = batch[2]
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
logs = {}
if (
args.local_rank == -1 and args.evaluate_during_training
): # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
print(json.dumps({**logs, **{"step": global_step}}))
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, prefix="", patience=0):
if args.model_type == "albert":
model.albert.set_regression_threshold(args.regression_threshold)
model.albert.set_patience(patience)
model.albert.reset_stats()
elif args.model_type == "bert":
model.bert.set_regression_threshold(args.regression_threshold)
model.bert.set_patience(patience)
model.bert.reset_stats()
else:
raise NotImplementedError()
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu eval
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
}
inputs["token_type_ids"] = batch[2]
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
print(" %s = %s" % (key, str(result[key])))
writer.write("%s = %s\n" % (key, str(result[key])))
if args.eval_all_checkpoints and patience != 0:
if args.model_type == "albert":
model.albert.log_stats()
elif args.model_type == "bert":
model.bert.log_stats()
else:
raise NotImplementedError()
return results
def load_and_cache_examples(args, task, tokenizer, evaluate=False):
if args.local_rank not in [-1, 0] and not evaluate:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
processor = processors[task]()
output_mode = output_modes[task]
# Load data features from cache or dataset file
cached_features_file = os.path.join(
args.data_dir,
"cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train",
list(filter(None, args.model_name_or_path.split("/"))).pop(),
str(args.max_seq_length),
str(task),
),
)
if os.path.exists(cached_features_file) and not args.overwrite_cache:
logger.info("Loading features from cached file %s", cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", args.data_dir)
label_list = processor.get_labels()
if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
# HACK(label indices are swapped in RoBERTa pretrained model)
label_list[1], label_list[2] = label_list[2], label_list[1]
examples = (
processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
)
features = convert_examples_to_features(
examples,
tokenizer,
label_list=label_list,
max_length=args.max_seq_length,
output_mode=output_mode,
)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file)
if args.local_rank == 0 and not evaluate:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
if output_mode == "classification":
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
elif output_mode == "regression":
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
return dataset
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name.",
)
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--patience",
default="0",
type=str,
required=False,
)
parser.add_argument(
"--regression_threshold",
default=0,
type=float,
required=False,
)
# Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from huggingface.co",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Run evaluation during training at each logging step.",
)
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.",
)
parser.add_argument(
"--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.",
)
parser.add_argument(
"--per_gpu_eval_batch_size",
default=1,
type=int,
help="Batch size per GPU/CPU for evaluation.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.",
)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument(
"--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.",
)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save checkpoint every X updates steps.",
)
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
parser.add_argument(
"--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory",
)
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets",
)
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank",
)
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
args = parser.parse_args()
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and args.do_train
and not args.overwrite_output_dir
):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir
)
)
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
if args.patience != "0" and args.per_gpu_eval_batch_size != 1:
raise ValueError("The eval batch size must be 1 with PABEE inference on.")
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
print("Total Model Parameters:", sum(param.numel() for param in model.parameters()))
output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters())
print("Output Layers Parameters:", output_layers_param_num)
single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters())
print(
"Added Output Layers Parameters:",
output_layers_param_num - single_output_layer_param_num,
)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
patience_list = [int(x) for x in args.patience.split(",")]
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
print(f"Evaluation for checkpoint {prefix}")
for patience in patience_list:
result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience)
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
results.update(result)
return results
if __name__ == "__main__":
main()
|
AdaMix/examples/research_projects/bert-loses-patience/run_glue_with_pabee.py/0
|
{
"file_path": "AdaMix/examples/research_projects/bert-loses-patience/run_glue_with_pabee.py",
"repo_id": "AdaMix",
"token_count": 12948
}
| 41 |
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0
PATH_TO_DATA=/h/xinji/projects/GLUE
MODEL_TYPE=bert # bert or roberta
MODEL_SIZE=base # base or large
DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI
MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE}
if [ $MODEL_TYPE = 'bert' ]
then
MODEL_NAME=${MODEL_NAME}-uncased
fi
python -u run_glue_deebert.py \
--model_type $MODEL_TYPE \
--model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
--task_name $DATASET \
--do_eval \
--do_lower_case \
--data_dir $PATH_TO_DATA/$DATASET \
--output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \
--plot_data_dir ./results/ \
--max_seq_length 128 \
--eval_each_highway \
--eval_highway \
--overwrite_cache \
--per_gpu_eval_batch_size=1
|
AdaMix/examples/research_projects/deebert/eval_deebert.sh/0
|
{
"file_path": "AdaMix/examples/research_projects/deebert/eval_deebert.sh",
"repo_id": "AdaMix",
"token_count": 360
}
| 42 |
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocessing script before training DistilBERT.
Specific to BERT -> DistilBERT.
"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation"
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
args = parser.parse_args()
if args.model_type == "bert":
model = BertForMaskedLM.from_pretrained(args.model_name)
prefix = "bert"
else:
raise ValueError('args.model_type should be "bert".')
state_dict = model.state_dict()
compressed_sd = {}
for w in ["word_embeddings", "position_embeddings"]:
compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"]
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]
std_idx = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
]
compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[
f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
]
std_idx += 1
compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"]
compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"]
compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"]
print(f"N layers selected for distillation: {std_idx}")
print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")
print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
torch.save(compressed_sd, args.dump_checkpoint)
|
AdaMix/examples/research_projects/distillation/scripts/extract_distilbert.py/0
|
{
"file_path": "AdaMix/examples/research_projects/distillation/scripts/extract_distilbert.py",
"repo_id": "AdaMix",
"token_count": 1828
}
| 43 |
"""
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2 && Huggingface Co.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.import copy
"""
import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision.ops.boxes import batched_nms, nms
from utils import WEIGHTS_NAME, Config, cached_path, hf_bucket_url, is_remote_url, load_checkpoint
# other:
def norm_box(boxes, raw_sizes):
if not isinstance(boxes, torch.Tensor):
normalized_boxes = boxes.copy()
else:
normalized_boxes = boxes.clone()
normalized_boxes[:, :, (0, 2)] /= raw_sizes[:, 1]
normalized_boxes[:, :, (1, 3)] /= raw_sizes[:, 0]
return normalized_boxes
def pad_list_tensors(
list_tensors,
preds_per_image,
max_detections=None,
return_tensors=None,
padding=None,
pad_value=0,
location=None,
):
"""
location will always be cpu for np tensors
"""
if location is None:
location = "cpu"
assert return_tensors in {"pt", "np", None}
assert padding in {"max_detections", "max_batch", None}
new = []
if padding is None:
if return_tensors is None:
return list_tensors
elif return_tensors == "pt":
if not isinstance(list_tensors, torch.Tensor):
return torch.stack(list_tensors).to(location)
else:
return list_tensors.to(location)
else:
if not isinstance(list_tensors, list):
return np.array(list_tensors.to(location))
else:
return list_tensors.to(location)
if padding == "max_detections":
assert max_detections is not None, "specify max number of detections per batch"
elif padding == "max_batch":
max_detections = max(preds_per_image)
for i in range(len(list_tensors)):
too_small = False
tensor_i = list_tensors.pop(0)
if tensor_i.ndim < 2:
too_small = True
tensor_i = tensor_i.unsqueeze(-1)
assert isinstance(tensor_i, torch.Tensor)
tensor_i = F.pad(
input=tensor_i,
pad=(0, 0, 0, max_detections - preds_per_image[i]),
mode="constant",
value=pad_value,
)
if too_small:
tensor_i = tensor_i.squeeze(-1)
if return_tensors is None:
if location == "cpu":
tensor_i = tensor_i.cpu()
tensor_i = tensor_i.tolist()
if return_tensors == "np":
if location == "cpu":
tensor_i = tensor_i.cpu()
tensor_i = tensor_i.numpy()
else:
if location == "cpu":
tensor_i = tensor_i.cpu()
new.append(tensor_i)
if return_tensors == "np":
return np.stack(new, axis=0)
elif return_tensors == "pt" and not isinstance(new, torch.Tensor):
return torch.stack(new, dim=0)
else:
return list_tensors
def do_nms(boxes, scores, image_shape, score_thresh, nms_thresh, mind, maxd):
scores = scores[:, :-1]
num_bbox_reg_classes = boxes.shape[1] // 4
# Convert to Boxes to use the `clip` function ...
boxes = boxes.reshape(-1, 4)
_clip_box(boxes, image_shape)
boxes = boxes.view(-1, num_bbox_reg_classes, 4) # R x C x 4
# Select max scores
max_scores, max_classes = scores.max(1) # R x C --> R
num_objs = boxes.size(0)
boxes = boxes.view(-1, 4)
idxs = torch.arange(num_objs).to(boxes.device) * num_bbox_reg_classes + max_classes
max_boxes = boxes[idxs] # Select max boxes according to the max scores.
# Apply NMS
keep = nms(max_boxes, max_scores, nms_thresh)
keep = keep[:maxd]
if keep.shape[-1] >= mind and keep.shape[-1] <= maxd:
max_boxes, max_scores = max_boxes[keep], max_scores[keep]
classes = max_classes[keep]
return max_boxes, max_scores, classes, keep
else:
return None
# Helper Functions
def _clip_box(tensor, box_size: Tuple[int, int]):
assert torch.isfinite(tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
tensor[:, 0].clamp_(min=0, max=w)
tensor[:, 1].clamp_(min=0, max=h)
tensor[:, 2].clamp_(min=0, max=w)
tensor[:, 3].clamp_(min=0, max=h)
def _nonempty_boxes(box, threshold: float = 0.0) -> torch.Tensor:
widths = box[:, 2] - box[:, 0]
heights = box[:, 3] - box[:, 1]
keep = (widths > threshold) & (heights > threshold)
return keep
def get_norm(norm, out_channels):
if isinstance(norm, str):
if len(norm) == 0:
return None
norm = {
"BN": BatchNorm2d,
"GN": lambda channels: nn.GroupNorm(32, channels),
"nnSyncBN": nn.SyncBatchNorm, # keep for debugging
"": lambda x: x,
}[norm]
return norm(out_channels)
def _create_grid_offsets(size: List[int], stride: int, offset: float, device):
grid_height, grid_width = size
shifts_x = torch.arange(
offset * stride,
grid_width * stride,
step=stride,
dtype=torch.float32,
device=device,
)
shifts_y = torch.arange(
offset * stride,
grid_height * stride,
step=stride,
dtype=torch.float32,
device=device,
)
shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
shift_x = shift_x.reshape(-1)
shift_y = shift_y.reshape(-1)
return shift_x, shift_y
def build_backbone(cfg):
input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
norm = cfg.RESNETS.NORM
stem = BasicStem(
in_channels=input_shape.channels,
out_channels=cfg.RESNETS.STEM_OUT_CHANNELS,
norm=norm,
caffe_maxpool=cfg.MODEL.MAX_POOL,
)
freeze_at = cfg.BACKBONE.FREEZE_AT
if freeze_at >= 1:
for p in stem.parameters():
p.requires_grad = False
out_features = cfg.RESNETS.OUT_FEATURES
depth = cfg.RESNETS.DEPTH
num_groups = cfg.RESNETS.NUM_GROUPS
width_per_group = cfg.RESNETS.WIDTH_PER_GROUP
bottleneck_channels = num_groups * width_per_group
in_channels = cfg.RESNETS.STEM_OUT_CHANNELS
out_channels = cfg.RESNETS.RES2_OUT_CHANNELS
stride_in_1x1 = cfg.RESNETS.STRIDE_IN_1X1
res5_dilation = cfg.RESNETS.RES5_DILATION
assert res5_dilation in {1, 2}, "res5_dilation cannot be {}.".format(res5_dilation)
num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth]
stages = []
out_stage_idx = [{"res2": 2, "res3": 3, "res4": 4, "res5": 5}[f] for f in out_features]
max_stage_idx = max(out_stage_idx)
for idx, stage_idx in enumerate(range(2, max_stage_idx + 1)):
dilation = res5_dilation if stage_idx == 5 else 1
first_stride = 1 if idx == 0 or (stage_idx == 5 and dilation == 2) else 2
stage_kargs = {
"num_blocks": num_blocks_per_stage[idx],
"first_stride": first_stride,
"in_channels": in_channels,
"bottleneck_channels": bottleneck_channels,
"out_channels": out_channels,
"num_groups": num_groups,
"norm": norm,
"stride_in_1x1": stride_in_1x1,
"dilation": dilation,
}
stage_kargs["block_class"] = BottleneckBlock
blocks = ResNet.make_stage(**stage_kargs)
in_channels = out_channels
out_channels *= 2
bottleneck_channels *= 2
if freeze_at >= stage_idx:
for block in blocks:
block.freeze()
stages.append(blocks)
return ResNet(stem, stages, out_features=out_features)
def find_top_rpn_proposals(
proposals,
pred_objectness_logits,
images,
image_sizes,
nms_thresh,
pre_nms_topk,
post_nms_topk,
min_box_side_len,
training,
):
"""Args:
proposals (list[Tensor]): (L, N, Hi*Wi*A, 4).
pred_objectness_logits: tensors of length L.
nms_thresh (float): IoU threshold to use for NMS
pre_nms_topk (int): before nms
post_nms_topk (int): after nms
min_box_side_len (float): minimum proposal box side
training (bool): True if proposals are to be used in training,
Returns:
results (List[Dict]): stores post_nms_topk object proposals for image i.
"""
num_images = len(images)
device = proposals[0].device
# 1. Select top-k anchor for every level and every image
topk_scores = [] # #lvl Tensor, each of shape N x topk
topk_proposals = []
level_ids = [] # #lvl Tensor, each of shape (topk,)
batch_idx = torch.arange(num_images, device=device)
for level_id, proposals_i, logits_i in zip(itertools.count(), proposals, pred_objectness_logits):
Hi_Wi_A = logits_i.shape[1]
num_proposals_i = min(pre_nms_topk, Hi_Wi_A)
# sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812)
# topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
logits_i, idx = logits_i.sort(descending=True, dim=1)
topk_scores_i = logits_i[batch_idx, :num_proposals_i]
topk_idx = idx[batch_idx, :num_proposals_i]
# each is N x topk
topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 4
topk_proposals.append(topk_proposals_i)
topk_scores.append(topk_scores_i)
level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device))
# 2. Concat all levels together
topk_scores = torch.cat(topk_scores, dim=1)
topk_proposals = torch.cat(topk_proposals, dim=1)
level_ids = torch.cat(level_ids, dim=0)
# if I change to batched_nms, I wonder if this will make a difference
# 3. For each image, run a per-level NMS, and choose topk results.
results = []
for n, image_size in enumerate(image_sizes):
boxes = topk_proposals[n]
scores_per_img = topk_scores[n]
# I will have to take a look at the boxes clip method
_clip_box(boxes, image_size)
# filter empty boxes
keep = _nonempty_boxes(boxes, threshold=min_box_side_len)
lvl = level_ids
if keep.sum().item() != len(boxes):
boxes, scores_per_img, lvl = (
boxes[keep],
scores_per_img[keep],
level_ids[keep],
)
keep = batched_nms(boxes, scores_per_img, lvl, nms_thresh)
keep = keep[:post_nms_topk]
res = (boxes[keep], scores_per_img[keep])
results.append(res)
# I wonder if it would be possible for me to pad all these things.
return results
def subsample_labels(labels, num_samples, positive_fraction, bg_label):
"""
Returns:
pos_idx, neg_idx (Tensor):
1D vector of indices. The total length of both is `num_samples` or fewer.
"""
positive = torch.nonzero((labels != -1) & (labels != bg_label)).squeeze(1)
negative = torch.nonzero(labels == bg_label).squeeze(1)
num_pos = int(num_samples * positive_fraction)
# protect against not enough positive examples
num_pos = min(positive.numel(), num_pos)
num_neg = num_samples - num_pos
# protect against not enough negative examples
num_neg = min(negative.numel(), num_neg)
# randomly select positive and negative examples
perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
pos_idx = positive[perm1]
neg_idx = negative[perm2]
return pos_idx, neg_idx
def add_ground_truth_to_proposals(gt_boxes, proposals):
raise NotImplementedError()
def add_ground_truth_to_proposals_single_image(gt_boxes, proposals):
raise NotImplementedError()
def _fmt_box_list(box_tensor, batch_index: int):
repeated_index = torch.full(
(len(box_tensor), 1),
batch_index,
dtype=box_tensor.dtype,
device=box_tensor.device,
)
return torch.cat((repeated_index, box_tensor), dim=1)
def convert_boxes_to_pooler_format(box_lists: List[torch.Tensor]):
pooler_fmt_boxes = torch.cat(
[_fmt_box_list(box_list, i) for i, box_list in enumerate(box_lists)],
dim=0,
)
return pooler_fmt_boxes
def assign_boxes_to_levels(
box_lists: List[torch.Tensor],
min_level: int,
max_level: int,
canonical_box_size: int,
canonical_level: int,
):
box_sizes = torch.sqrt(torch.cat([boxes.area() for boxes in box_lists]))
# Eqn.(1) in FPN paper
level_assignments = torch.floor(canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8))
# clamp level to (min, max), in case the box size is too large or too small
# for the available feature maps
level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level)
return level_assignments.to(torch.int64) - min_level
# Helper Classes
class _NewEmptyTensorOp(torch.autograd.Function):
@staticmethod
def forward(ctx, x, new_shape):
ctx.shape = x.shape
return x.new_empty(new_shape)
@staticmethod
def backward(ctx, grad):
shape = ctx.shape
return _NewEmptyTensorOp.apply(grad, shape), None
class ShapeSpec(namedtuple("_ShapeSpec", ["channels", "height", "width", "stride"])):
def __new__(cls, *, channels=None, height=None, width=None, stride=None):
return super().__new__(cls, channels, height, width, stride)
class Box2BoxTransform(object):
"""
This R-CNN transformation scales the box's width and height
by exp(dw), exp(dh) and shifts a box's center by the offset
(dx * width, dy * height).
"""
def __init__(self, weights: Tuple[float, float, float, float], scale_clamp: float = None):
"""
Args:
weights (4-element tuple): Scaling factors that are applied to the
(dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set
such that the deltas have unit variance; now they are treated as
hyperparameters of the system.
scale_clamp (float): When predicting deltas, the predicted box scaling
factors (dw and dh) are clamped such that they are <= scale_clamp.
"""
self.weights = weights
if scale_clamp is not None:
self.scale_clamp = scale_clamp
else:
"""
Value for clamping large dw and dh predictions.
The heuristic is that we clamp such that dw and dh are no larger
than what would transform a 16px box into a 1000px box
(based on a small anchor, 16px, and a typical image size, 1000px).
"""
self.scale_clamp = math.log(1000.0 / 16)
def get_deltas(self, src_boxes, target_boxes):
"""
Get box regression transformation deltas (dx, dy, dw, dh) that can be used
to transform the `src_boxes` into the `target_boxes`. That is, the relation
``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless
any delta is too large and is clamped).
Args:
src_boxes (Tensor): source boxes, e.g., object proposals
target_boxes (Tensor): target of the transformation, e.g., ground-truth
boxes.
"""
assert isinstance(src_boxes, torch.Tensor), type(src_boxes)
assert isinstance(target_boxes, torch.Tensor), type(target_boxes)
src_widths = src_boxes[:, 2] - src_boxes[:, 0]
src_heights = src_boxes[:, 3] - src_boxes[:, 1]
src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths
src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights
target_widths = target_boxes[:, 2] - target_boxes[:, 0]
target_heights = target_boxes[:, 3] - target_boxes[:, 1]
target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths
target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights
wx, wy, ww, wh = self.weights
dx = wx * (target_ctr_x - src_ctr_x) / src_widths
dy = wy * (target_ctr_y - src_ctr_y) / src_heights
dw = ww * torch.log(target_widths / src_widths)
dh = wh * torch.log(target_heights / src_heights)
deltas = torch.stack((dx, dy, dw, dh), dim=1)
assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!"
return deltas
def apply_deltas(self, deltas, boxes):
"""
Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.
Args:
deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.
deltas[i] represents k potentially different class-specific
box transformations for the single box boxes[i].
boxes (Tensor): boxes to transform, of shape (N, 4)
"""
boxes = boxes.to(deltas.dtype)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = self.weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=self.scale_clamp)
dh = torch.clamp(dh, max=self.scale_clamp)
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(deltas)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # x1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # y1
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # x2
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h # y2
return pred_boxes
class Matcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be matched to zero or more predicted elements.
The matching is determined by the MxN match_quality_matrix, that characterizes
how well each (ground-truth, prediction)-pair match each other. For example,
if the elements are boxes, this matrix may contain box intersection-over-union
overlap values.
The matcher returns (a) a vector of length N containing the index of the
ground-truth element m in [0, M) that matches to prediction n in [0, N).
(b) a vector of length N containing the labels for each prediction.
"""
def __init__(
self,
thresholds: List[float],
labels: List[int],
allow_low_quality_matches: bool = False,
):
"""
Args:
thresholds (list): a list of thresholds used to stratify predictions
into levels.
labels (list): a list of values to label predictions belonging at
each level. A label can be one of {-1, 0, 1} signifying
{ignore, negative class, positive class}, respectively.
allow_low_quality_matches (bool): if True, produce additional matches or predictions with maximum match quality lower than high_threshold.
For example, thresholds = [0.3, 0.5] labels = [0, -1, 1] All predictions with iou < 0.3 will be marked with 0 and
thus will be considered as false positives while training. All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
thus will be ignored. All predictions with 0.5 <= iou will be marked with 1 and thus will be considered as true positives.
"""
thresholds = thresholds[:]
assert thresholds[0] > 0
thresholds.insert(0, -float("inf"))
thresholds.append(float("inf"))
assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])
assert all([label_i in [-1, 0, 1] for label_i in labels])
assert len(labels) == len(thresholds) - 1
self.thresholds = thresholds
self.labels = labels
self.allow_low_quality_matches = allow_low_quality_matches
def __call__(self, match_quality_matrix):
"""
Args:
match_quality_matrix (Tensor[float]): an MxN tensor, containing the pairwise quality between M ground-truth elements and N predicted
elements. All elements must be >= 0 (due to the us of `torch.nonzero` for selecting indices in :meth:`set_low_quality_matches_`).
Returns:
matches (Tensor[int64]): a vector of length N, where matches[i] is a matched ground-truth index in [0, M)
match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates true or false positive or ignored
"""
assert match_quality_matrix.dim() == 2
if match_quality_matrix.numel() == 0:
default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64)
# When no gt boxes exist, we define IOU = 0 and therefore set labels
# to `self.labels[0]`, which usually defaults to background class 0
# To choose to ignore instead,
# can make labels=[-1,0,-1,1] + set appropriate thresholds
default_match_labels = match_quality_matrix.new_full(
(match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
)
return default_matches, default_match_labels
assert torch.all(match_quality_matrix >= 0)
# match_quality_matrix is M (gt) x N (predicted)
# Max over gt elements (dim 0) to find best gt candidate for each prediction
matched_vals, matches = match_quality_matrix.max(dim=0)
match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
low_high = (matched_vals >= low) & (matched_vals < high)
match_labels[low_high] = l
if self.allow_low_quality_matches:
self.set_low_quality_matches_(match_labels, match_quality_matrix)
return matches, match_labels
def set_low_quality_matches_(self, match_labels, match_quality_matrix):
"""
Produce additional matches for predictions that have only low-quality matches.
Specifically, for each ground-truth G find the set of predictions that have
maximum overlap with it (including ties); for each prediction in that set, if
it is unmatched, then match it to the ground-truth G.
This function implements the RPN assignment case (i)
in Sec. 3.1.2 of Faster R-CNN.
"""
# For each gt, find the prediction with which it has highest quality
highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
# Find the highest quality match available, even if it is low, including ties.
# Note that the matches qualities must be positive due to the use of
# `torch.nonzero`.
of_quality_inds = match_quality_matrix == highest_quality_foreach_gt[:, None]
if of_quality_inds.dim() == 0:
(_, pred_inds_with_highest_quality) = of_quality_inds.unsqueeze(0).nonzero().unbind(1)
else:
(_, pred_inds_with_highest_quality) = of_quality_inds.nonzero().unbind(1)
match_labels[pred_inds_with_highest_quality] = 1
class RPNOutputs(object):
def __init__(
self,
box2box_transform,
anchor_matcher,
batch_size_per_image,
positive_fraction,
images,
pred_objectness_logits,
pred_anchor_deltas,
anchors,
boundary_threshold=0,
gt_boxes=None,
smooth_l1_beta=0.0,
):
"""
Args:
box2box_transform (Box2BoxTransform): :class:`Box2BoxTransform` instance for anchor-proposal transformations.
anchor_matcher (Matcher): :class:`Matcher` instance for matching anchors to ground-truth boxes; used to determine training labels.
batch_size_per_image (int): number of proposals to sample when training
positive_fraction (float): target fraction of sampled proposals that should be positive
images (ImageList): :class:`ImageList` instance representing N input images
pred_objectness_logits (list[Tensor]): A list of L elements. Element i is a tensor of shape (N, A, Hi, W)
pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape (N, A*4, Hi, Wi)
anchors (list[torch.Tensor]): nested list of boxes. anchors[i][j] at (n, l) stores anchor array for feature map l
boundary_threshold (int): if >= 0, then anchors that extend beyond the image boundary by more than boundary_thresh are not used in training.
gt_boxes (list[Boxes], optional): A list of N elements.
smooth_l1_beta (float): The transition point between L1 and L2 lossn. When set to 0, the loss becomes L1. When +inf, it is ignored
"""
self.box2box_transform = box2box_transform
self.anchor_matcher = anchor_matcher
self.batch_size_per_image = batch_size_per_image
self.positive_fraction = positive_fraction
self.pred_objectness_logits = pred_objectness_logits
self.pred_anchor_deltas = pred_anchor_deltas
self.anchors = anchors
self.gt_boxes = gt_boxes
self.num_feature_maps = len(pred_objectness_logits)
self.num_images = len(images)
self.boundary_threshold = boundary_threshold
self.smooth_l1_beta = smooth_l1_beta
def _get_ground_truth(self):
raise NotImplementedError()
def predict_proposals(self):
# pred_anchor_deltas: (L, N, ? Hi, Wi)
# anchors:(N, L, -1, B)
# here we loop over specific feature map, NOT images
proposals = []
anchors = self.anchors.transpose(0, 1)
for anchors_i, pred_anchor_deltas_i in zip(anchors, self.pred_anchor_deltas):
B = anchors_i.size(-1)
N, _, Hi, Wi = pred_anchor_deltas_i.shape
anchors_i = anchors_i.flatten(start_dim=0, end_dim=1)
pred_anchor_deltas_i = pred_anchor_deltas_i.view(N, -1, B, Hi, Wi).permute(0, 3, 4, 1, 2).reshape(-1, B)
proposals_i = self.box2box_transform.apply_deltas(pred_anchor_deltas_i, anchors_i)
# Append feature map proposals with shape (N, Hi*Wi*A, B)
proposals.append(proposals_i.view(N, -1, B))
proposals = torch.stack(proposals)
return proposals
def predict_objectness_logits(self):
"""
Returns:
pred_objectness_logits (list[Tensor]) -> (N, Hi*Wi*A).
"""
pred_objectness_logits = [
# Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A)
score.permute(0, 2, 3, 1).reshape(self.num_images, -1)
for score in self.pred_objectness_logits
]
return pred_objectness_logits
# Main Classes
class Conv2d(torch.nn.Conv2d):
def __init__(self, *args, **kwargs):
norm = kwargs.pop("norm", None)
activation = kwargs.pop("activation", None)
super().__init__(*args, **kwargs)
self.norm = norm
self.activation = activation
def forward(self, x):
if x.numel() == 0 and self.training:
assert not isinstance(self.norm, torch.nn.SyncBatchNorm)
if x.numel() == 0:
assert not isinstance(self.norm, torch.nn.GroupNorm)
output_shape = [
(i + 2 * p - (di * (k - 1) + 1)) // s + 1
for i, p, di, k, s in zip(
x.shape[-2:],
self.padding,
self.dilation,
self.kernel_size,
self.stride,
)
]
output_shape = [x.shape[0], self.weight.shape[0]] + output_shape
empty = _NewEmptyTensorOp.apply(x, output_shape)
if self.training:
_dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
return empty + _dummy
else:
return empty
x = super().forward(x)
if self.norm is not None:
x = self.norm(x)
if self.activation is not None:
x = self.activation(x)
return x
class LastLevelMaxPool(nn.Module):
"""
This module is used in the original FPN to generate a downsampled P6 feature from P5.
"""
def __init__(self):
super().__init__()
self.num_levels = 1
self.in_feature = "p5"
def forward(self, x):
return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
class LastLevelP6P7(nn.Module):
"""
This module is used in RetinaNet to generate extra layers, P6 and P7 from C5 feature.
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.num_levels = 2
self.in_feature = "res5"
self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
def forward(self, c5):
p6 = self.p6(c5)
p7 = self.p7(F.relu(p6))
return [p6, p7]
class BasicStem(nn.Module):
def __init__(self, in_channels=3, out_channels=64, norm="BN", caffe_maxpool=False):
super().__init__()
self.conv1 = Conv2d(
in_channels,
out_channels,
kernel_size=7,
stride=2,
padding=3,
bias=False,
norm=get_norm(norm, out_channels),
)
self.caffe_maxpool = caffe_maxpool
# use pad 1 instead of pad zero
def forward(self, x):
x = self.conv1(x)
x = F.relu_(x)
if self.caffe_maxpool:
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=0, ceil_mode=True)
else:
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
return x
@property
def out_channels(self):
return self.conv1.out_channels
@property
def stride(self):
return 4 # = stride 2 conv -> stride 2 max pool
class ResNetBlockBase(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
def freeze(self):
for p in self.parameters():
p.requires_grad = False
return self
class BottleneckBlock(ResNetBlockBase):
def __init__(
self,
in_channels,
out_channels,
bottleneck_channels,
stride=1,
num_groups=1,
norm="BN",
stride_in_1x1=False,
dilation=1,
):
super().__init__(in_channels, out_channels, stride)
if in_channels != out_channels:
self.shortcut = Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=stride,
bias=False,
norm=get_norm(norm, out_channels),
)
else:
self.shortcut = None
# The original MSRA ResNet models have stride in the first 1x1 conv
# The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have
# stride in the 3x3 conv
stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride)
self.conv1 = Conv2d(
in_channels,
bottleneck_channels,
kernel_size=1,
stride=stride_1x1,
bias=False,
norm=get_norm(norm, bottleneck_channels),
)
self.conv2 = Conv2d(
bottleneck_channels,
bottleneck_channels,
kernel_size=3,
stride=stride_3x3,
padding=1 * dilation,
bias=False,
groups=num_groups,
dilation=dilation,
norm=get_norm(norm, bottleneck_channels),
)
self.conv3 = Conv2d(
bottleneck_channels,
out_channels,
kernel_size=1,
bias=False,
norm=get_norm(norm, out_channels),
)
def forward(self, x):
out = self.conv1(x)
out = F.relu_(out)
out = self.conv2(out)
out = F.relu_(out)
out = self.conv3(out)
if self.shortcut is not None:
shortcut = self.shortcut(x)
else:
shortcut = x
out += shortcut
out = F.relu_(out)
return out
class Backbone(nn.Module, metaclass=ABCMeta):
def __init__(self):
super().__init__()
@abstractmethod
def forward(self):
pass
@property
def size_divisibility(self):
"""
Some backbones require the input height and width to be divisible by a specific integer. This is
typically true for encoder / decoder type networks with lateral connection (e.g., FPN) for which feature maps need to match
dimension in the "bottom up" and "top down" paths. Set to 0 if no specific input size divisibility is required.
"""
return 0
def output_shape(self):
return {
name: ShapeSpec(
channels=self._out_feature_channels[name],
stride=self._out_feature_strides[name],
)
for name in self._out_features
}
@property
def out_features(self):
"""deprecated"""
return self._out_features
@property
def out_feature_strides(self):
"""deprecated"""
return {f: self._out_feature_strides[f] for f in self._out_features}
@property
def out_feature_channels(self):
"""deprecated"""
return {f: self._out_feature_channels[f] for f in self._out_features}
class ResNet(Backbone):
def __init__(self, stem, stages, num_classes=None, out_features=None):
"""
Args:
stem (nn.Module): a stem module
stages (list[list[ResNetBlock]]): several (typically 4) stages, each contains multiple :class:`ResNetBlockBase`.
num_classes (None or int): if None, will not perform classification.
out_features (list[str]): name of the layers whose outputs should be returned in forward. Can be anything in:
"stem", "linear", or "res2" ... If None, will return the output of the last layer.
"""
super(ResNet, self).__init__()
self.stem = stem
self.num_classes = num_classes
current_stride = self.stem.stride
self._out_feature_strides = {"stem": current_stride}
self._out_feature_channels = {"stem": self.stem.out_channels}
self.stages_and_names = []
for i, blocks in enumerate(stages):
for block in blocks:
assert isinstance(block, ResNetBlockBase), block
curr_channels = block.out_channels
stage = nn.Sequential(*blocks)
name = "res" + str(i + 2)
self.add_module(name, stage)
self.stages_and_names.append((stage, name))
self._out_feature_strides[name] = current_stride = int(
current_stride * np.prod([k.stride for k in blocks])
)
self._out_feature_channels[name] = blocks[-1].out_channels
if num_classes is not None:
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.linear = nn.Linear(curr_channels, num_classes)
# Sec 5.1 in "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour":
# "The 1000-way fully-connected layer is initialized by
# drawing weights from a zero-mean Gaussian with std of 0.01."
nn.init.normal_(self.linear.weight, stddev=0.01)
name = "linear"
if out_features is None:
out_features = [name]
self._out_features = out_features
assert len(self._out_features)
children = [x[0] for x in self.named_children()]
for out_feature in self._out_features:
assert out_feature in children, "Available children: {}".format(", ".join(children))
def forward(self, x):
outputs = {}
x = self.stem(x)
if "stem" in self._out_features:
outputs["stem"] = x
for stage, name in self.stages_and_names:
x = stage(x)
if name in self._out_features:
outputs[name] = x
if self.num_classes is not None:
x = self.avgpool(x)
x = self.linear(x)
if "linear" in self._out_features:
outputs["linear"] = x
return outputs
def output_shape(self):
return {
name: ShapeSpec(
channels=self._out_feature_channels[name],
stride=self._out_feature_strides[name],
)
for name in self._out_features
}
@staticmethod
def make_stage(
block_class,
num_blocks,
first_stride=None,
*,
in_channels,
out_channels,
**kwargs,
):
"""
Usually, layers that produce the same feature map spatial size
are defined as one "stage".
Under such definition, stride_per_block[1:] should all be 1.
"""
if first_stride is not None:
assert "stride" not in kwargs and "stride_per_block" not in kwargs
kwargs["stride_per_block"] = [first_stride] + [1] * (num_blocks - 1)
blocks = []
for i in range(num_blocks):
curr_kwargs = {}
for k, v in kwargs.items():
if k.endswith("_per_block"):
assert len(v) == num_blocks, (
f"Argument '{k}' of make_stage should have the " f"same length as num_blocks={num_blocks}."
)
newk = k[: -len("_per_block")]
assert newk not in kwargs, f"Cannot call make_stage with both {k} and {newk}!"
curr_kwargs[newk] = v[i]
else:
curr_kwargs[k] = v
blocks.append(block_class(in_channels=in_channels, out_channels=out_channels, **curr_kwargs))
in_channels = out_channels
return blocks
class ROIPooler(nn.Module):
"""
Region of interest feature map pooler that supports pooling from one or more
feature maps.
"""
def __init__(
self,
output_size,
scales,
sampling_ratio,
canonical_box_size=224,
canonical_level=4,
):
super().__init__()
# assumption that stride is a power of 2.
min_level = -math.log2(scales[0])
max_level = -math.log2(scales[-1])
# a bunch of testing
assert math.isclose(min_level, int(min_level)) and math.isclose(max_level, int(max_level))
assert len(scales) == max_level - min_level + 1, "not pyramid"
assert 0 < min_level and min_level <= max_level
if isinstance(output_size, int):
output_size = (output_size, output_size)
assert len(output_size) == 2 and isinstance(output_size[0], int) and isinstance(output_size[1], int)
if len(scales) > 1:
assert min_level <= canonical_level and canonical_level <= max_level
assert canonical_box_size > 0
self.output_size = output_size
self.min_level = int(min_level)
self.max_level = int(max_level)
self.level_poolers = nn.ModuleList(RoIPool(output_size, spatial_scale=scale) for scale in scales)
self.canonical_level = canonical_level
self.canonical_box_size = canonical_box_size
def forward(self, feature_maps, boxes):
"""
Args:
feature_maps: List[torch.Tensor(N,C,W,H)]
box_lists: list[torch.Tensor])
Returns:
A tensor of shape(N*B, Channels, output_size, output_size)
"""
x = [v for v in feature_maps.values()]
num_level_assignments = len(self.level_poolers)
assert len(x) == num_level_assignments and len(boxes) == x[0].size(0)
pooler_fmt_boxes = convert_boxes_to_pooler_format(boxes)
if num_level_assignments == 1:
return self.level_poolers[0](x[0], pooler_fmt_boxes)
level_assignments = assign_boxes_to_levels(
boxes,
self.min_level,
self.max_level,
self.canonical_box_size,
self.canonical_level,
)
num_boxes = len(pooler_fmt_boxes)
num_channels = x[0].shape[1]
output_size = self.output_size[0]
dtype, device = x[0].dtype, x[0].device
output = torch.zeros(
(num_boxes, num_channels, output_size, output_size),
dtype=dtype,
device=device,
)
for level, (x_level, pooler) in enumerate(zip(x, self.level_poolers)):
inds = torch.nonzero(level_assignments == level).squeeze(1)
pooler_fmt_boxes_level = pooler_fmt_boxes[inds]
output[inds] = pooler(x_level, pooler_fmt_boxes_level)
return output
class ROIOutputs(object):
def __init__(self, cfg, training=False):
self.smooth_l1_beta = cfg.ROI_BOX_HEAD.SMOOTH_L1_BETA
self.box2box_transform = Box2BoxTransform(weights=cfg.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
self.training = training
self.score_thresh = cfg.ROI_HEADS.SCORE_THRESH_TEST
self.min_detections = cfg.MIN_DETECTIONS
self.max_detections = cfg.MAX_DETECTIONS
nms_thresh = cfg.ROI_HEADS.NMS_THRESH_TEST
if not isinstance(nms_thresh, list):
nms_thresh = [nms_thresh]
self.nms_thresh = nms_thresh
def _predict_boxes(self, proposals, box_deltas, preds_per_image):
num_pred = box_deltas.size(0)
B = proposals[0].size(-1)
K = box_deltas.size(-1) // B
box_deltas = box_deltas.view(num_pred * K, B)
proposals = torch.cat(proposals, dim=0).unsqueeze(-2).expand(num_pred, K, B)
proposals = proposals.reshape(-1, B)
boxes = self.box2box_transform.apply_deltas(box_deltas, proposals)
return boxes.view(num_pred, K * B).split(preds_per_image, dim=0)
def _predict_objs(self, obj_logits, preds_per_image):
probs = F.softmax(obj_logits, dim=-1)
probs = probs.split(preds_per_image, dim=0)
return probs
def _predict_attrs(self, attr_logits, preds_per_image):
attr_logits = attr_logits[..., :-1].softmax(-1)
attr_probs, attrs = attr_logits.max(-1)
return attr_probs.split(preds_per_image, dim=0), attrs.split(preds_per_image, dim=0)
@torch.no_grad()
def inference(
self,
obj_logits,
attr_logits,
box_deltas,
pred_boxes,
features,
sizes,
scales=None,
):
# only the pred boxes is the
preds_per_image = [p.size(0) for p in pred_boxes]
boxes_all = self._predict_boxes(pred_boxes, box_deltas, preds_per_image)
obj_scores_all = self._predict_objs(obj_logits, preds_per_image) # list of length N
attr_probs_all, attrs_all = self._predict_attrs(attr_logits, preds_per_image)
features = features.split(preds_per_image, dim=0)
# fun for each image too, also I can experiment and do multiple images
final_results = []
zipped = zip(boxes_all, obj_scores_all, attr_probs_all, attrs_all, sizes)
for i, (boxes, obj_scores, attr_probs, attrs, size) in enumerate(zipped):
for nms_t in self.nms_thresh:
outputs = do_nms(
boxes,
obj_scores,
size,
self.score_thresh,
nms_t,
self.min_detections,
self.max_detections,
)
if outputs is not None:
max_boxes, max_scores, classes, ids = outputs
break
if scales is not None:
scale_yx = scales[i]
max_boxes[:, 0::2] *= scale_yx[1]
max_boxes[:, 1::2] *= scale_yx[0]
final_results.append(
(
max_boxes,
classes,
max_scores,
attrs[ids],
attr_probs[ids],
features[i][ids],
)
)
boxes, classes, class_probs, attrs, attr_probs, roi_features = map(list, zip(*final_results))
return boxes, classes, class_probs, attrs, attr_probs, roi_features
def training(self, obj_logits, attr_logits, box_deltas, pred_boxes, features, sizes):
pass
def __call__(
self,
obj_logits,
attr_logits,
box_deltas,
pred_boxes,
features,
sizes,
scales=None,
):
if self.training:
raise NotImplementedError()
return self.inference(
obj_logits,
attr_logits,
box_deltas,
pred_boxes,
features,
sizes,
scales=scales,
)
class Res5ROIHeads(nn.Module):
"""
ROIHeads perform all per-region computation in an R-CNN.
It contains logic of cropping the regions, extract per-region features
(by the res-5 block in this case), and make per-region predictions.
"""
def __init__(self, cfg, input_shape):
super().__init__()
self.batch_size_per_image = cfg.RPN.BATCH_SIZE_PER_IMAGE
self.positive_sample_fraction = cfg.ROI_HEADS.POSITIVE_FRACTION
self.in_features = cfg.ROI_HEADS.IN_FEATURES
self.num_classes = cfg.ROI_HEADS.NUM_CLASSES
self.proposal_append_gt = cfg.ROI_HEADS.PROPOSAL_APPEND_GT
self.feature_strides = {k: v.stride for k, v in input_shape.items()}
self.feature_channels = {k: v.channels for k, v in input_shape.items()}
self.cls_agnostic_bbox_reg = cfg.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG
self.stage_channel_factor = 2 ** 3 # res5 is 8x res2
self.out_channels = cfg.RESNETS.RES2_OUT_CHANNELS * self.stage_channel_factor
# self.proposal_matcher = Matcher(
# cfg.ROI_HEADS.IOU_THRESHOLDS,
# cfg.ROI_HEADS.IOU_LABELS,
# allow_low_quality_matches=False,
# )
pooler_resolution = cfg.ROI_BOX_HEAD.POOLER_RESOLUTION
pooler_scales = (1.0 / self.feature_strides[self.in_features[0]],)
sampling_ratio = cfg.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
res5_halve = cfg.ROI_BOX_HEAD.RES5HALVE
use_attr = cfg.ROI_BOX_HEAD.ATTR
num_attrs = cfg.ROI_BOX_HEAD.NUM_ATTRS
self.pooler = ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
)
self.res5 = self._build_res5_block(cfg)
if not res5_halve:
"""
Modifications for VG in RoI heads:
1. Change the stride of conv1 and shortcut in Res5.Block1 from 2 to 1
2. Modifying all conv2 with (padding: 1 --> 2) and (dilation: 1 --> 2)
"""
self.res5[0].conv1.stride = (1, 1)
self.res5[0].shortcut.stride = (1, 1)
for i in range(3):
self.res5[i].conv2.padding = (2, 2)
self.res5[i].conv2.dilation = (2, 2)
self.box_predictor = FastRCNNOutputLayers(
self.out_channels,
self.num_classes,
self.cls_agnostic_bbox_reg,
use_attr=use_attr,
num_attrs=num_attrs,
)
def _build_res5_block(self, cfg):
stage_channel_factor = self.stage_channel_factor # res5 is 8x res2
num_groups = cfg.RESNETS.NUM_GROUPS
width_per_group = cfg.RESNETS.WIDTH_PER_GROUP
bottleneck_channels = num_groups * width_per_group * stage_channel_factor
out_channels = self.out_channels
stride_in_1x1 = cfg.RESNETS.STRIDE_IN_1X1
norm = cfg.RESNETS.NORM
blocks = ResNet.make_stage(
BottleneckBlock,
3,
first_stride=2,
in_channels=out_channels // 2,
bottleneck_channels=bottleneck_channels,
out_channels=out_channels,
num_groups=num_groups,
norm=norm,
stride_in_1x1=stride_in_1x1,
)
return nn.Sequential(*blocks)
def _shared_roi_transform(self, features, boxes):
x = self.pooler(features, boxes)
return self.res5(x)
def forward(self, features, proposal_boxes, gt_boxes=None):
if self.training:
"""
see https://github.com/airsplay/py-bottom-up-attention/\
blob/master/detectron2/modeling/roi_heads/roi_heads.py
"""
raise NotImplementedError()
assert not proposal_boxes[0].requires_grad
box_features = self._shared_roi_transform(features, proposal_boxes)
feature_pooled = box_features.mean(dim=[2, 3]) # pooled to 1x1
obj_logits, attr_logits, pred_proposal_deltas = self.box_predictor(feature_pooled)
return obj_logits, attr_logits, pred_proposal_deltas, feature_pooled
class AnchorGenerator(nn.Module):
"""
For a set of image sizes and feature maps, computes a set of anchors.
"""
def __init__(self, cfg, input_shape: List[ShapeSpec]):
super().__init__()
sizes = cfg.ANCHOR_GENERATOR.SIZES
aspect_ratios = cfg.ANCHOR_GENERATOR.ASPECT_RATIOS
self.strides = [x.stride for x in input_shape]
self.offset = cfg.ANCHOR_GENERATOR.OFFSET
assert 0.0 <= self.offset < 1.0, self.offset
"""
sizes (list[list[int]]): sizes[i] is the list of anchor sizes for feat map i
1. given in absolute lengths in units of the input image;
2. they do not dynamically scale if the input image size changes.
aspect_ratios (list[list[float]])
strides (list[int]): stride of each input feature.
"""
self.num_features = len(self.strides)
self.cell_anchors = nn.ParameterList(self._calculate_anchors(sizes, aspect_ratios))
self._spacial_feat_dim = 4
def _calculate_anchors(self, sizes, aspect_ratios):
# If one size (or aspect ratio) is specified and there are multiple feature
# maps, then we "broadcast" anchors of that single size (or aspect ratio)
if len(sizes) == 1:
sizes *= self.num_features
if len(aspect_ratios) == 1:
aspect_ratios *= self.num_features
assert self.num_features == len(sizes)
assert self.num_features == len(aspect_ratios)
cell_anchors = [self.generate_cell_anchors(s, a).float() for s, a in zip(sizes, aspect_ratios)]
return cell_anchors
@property
def box_dim(self):
return self._spacial_feat_dim
@property
def num_cell_anchors(self):
"""
Returns:
list[int]: Each int is the number of anchors at every pixel location, on that feature map.
"""
return [len(cell_anchors) for cell_anchors in self.cell_anchors]
def grid_anchors(self, grid_sizes):
anchors = []
for (size, stride, base_anchors) in zip(grid_sizes, self.strides, self.cell_anchors):
shift_x, shift_y = _create_grid_offsets(size, stride, self.offset, base_anchors.device)
shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
return anchors
def generate_cell_anchors(self, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)):
"""
anchors are continuous geometric rectangles
centered on one feature map point sample.
We can later build the set of anchors
for the entire feature map by tiling these tensors
"""
anchors = []
for size in sizes:
area = size ** 2.0
for aspect_ratio in aspect_ratios:
w = math.sqrt(area / aspect_ratio)
h = aspect_ratio * w
x0, y0, x1, y1 = -w / 2.0, -h / 2.0, w / 2.0, h / 2.0
anchors.append([x0, y0, x1, y1])
return nn.Parameter(torch.Tensor(anchors))
def forward(self, features):
"""
Args:
features List[torch.Tensor]: list of feature maps on which to generate anchors.
Returns:
torch.Tensor: a list of #image elements.
"""
num_images = features[0].size(0)
grid_sizes = [feature_map.shape[-2:] for feature_map in features]
anchors_over_all_feature_maps = self.grid_anchors(grid_sizes)
anchors_over_all_feature_maps = torch.stack(anchors_over_all_feature_maps)
return anchors_over_all_feature_maps.unsqueeze(0).repeat_interleave(num_images, dim=0)
class RPNHead(nn.Module):
"""
RPN classification and regression heads. Uses a 3x3 conv to produce a shared
hidden state from which one 1x1 conv predicts objectness logits for each anchor
and a second 1x1 conv predicts bounding-box deltas specifying how to deform
each anchor into an object proposal.
"""
def __init__(self, cfg, input_shape: List[ShapeSpec]):
super().__init__()
# Standard RPN is shared across levels:
in_channels = [s.channels for s in input_shape]
assert len(set(in_channels)) == 1, "Each level must have the same channel!"
in_channels = in_channels[0]
anchor_generator = AnchorGenerator(cfg, input_shape)
num_cell_anchors = anchor_generator.num_cell_anchors
box_dim = anchor_generator.box_dim
assert len(set(num_cell_anchors)) == 1, "Each level must have the same number of cell anchors"
num_cell_anchors = num_cell_anchors[0]
if cfg.PROPOSAL_GENERATOR.HIDDEN_CHANNELS == -1:
hid_channels = in_channels
else:
hid_channels = cfg.PROPOSAL_GENERATOR.HIDDEN_CHANNELS
# Modifications for VG in RPN (modeling/proposal_generator/rpn.py)
# Use hidden dim instead fo the same dim as Res4 (in_channels)
# 3x3 conv for the hidden representation
self.conv = nn.Conv2d(in_channels, hid_channels, kernel_size=3, stride=1, padding=1)
# 1x1 conv for predicting objectness logits
self.objectness_logits = nn.Conv2d(hid_channels, num_cell_anchors, kernel_size=1, stride=1)
# 1x1 conv for predicting box2box transform deltas
self.anchor_deltas = nn.Conv2d(hid_channels, num_cell_anchors * box_dim, kernel_size=1, stride=1)
for layer in [self.conv, self.objectness_logits, self.anchor_deltas]:
nn.init.normal_(layer.weight, std=0.01)
nn.init.constant_(layer.bias, 0)
def forward(self, features):
"""
Args:
features (list[Tensor]): list of feature maps
"""
pred_objectness_logits = []
pred_anchor_deltas = []
for x in features:
t = F.relu(self.conv(x))
pred_objectness_logits.append(self.objectness_logits(t))
pred_anchor_deltas.append(self.anchor_deltas(t))
return pred_objectness_logits, pred_anchor_deltas
class RPN(nn.Module):
"""
Region Proposal Network, introduced by the Faster R-CNN paper.
"""
def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
self.min_box_side_len = cfg.PROPOSAL_GENERATOR.MIN_SIZE
self.in_features = cfg.RPN.IN_FEATURES
self.nms_thresh = cfg.RPN.NMS_THRESH
self.batch_size_per_image = cfg.RPN.BATCH_SIZE_PER_IMAGE
self.positive_fraction = cfg.RPN.POSITIVE_FRACTION
self.smooth_l1_beta = cfg.RPN.SMOOTH_L1_BETA
self.loss_weight = cfg.RPN.LOSS_WEIGHT
self.pre_nms_topk = {
True: cfg.RPN.PRE_NMS_TOPK_TRAIN,
False: cfg.RPN.PRE_NMS_TOPK_TEST,
}
self.post_nms_topk = {
True: cfg.RPN.POST_NMS_TOPK_TRAIN,
False: cfg.RPN.POST_NMS_TOPK_TEST,
}
self.boundary_threshold = cfg.RPN.BOUNDARY_THRESH
self.anchor_generator = AnchorGenerator(cfg, [input_shape[f] for f in self.in_features])
self.box2box_transform = Box2BoxTransform(weights=cfg.RPN.BBOX_REG_WEIGHTS)
self.anchor_matcher = Matcher(
cfg.RPN.IOU_THRESHOLDS,
cfg.RPN.IOU_LABELS,
allow_low_quality_matches=True,
)
self.rpn_head = RPNHead(cfg, [input_shape[f] for f in self.in_features])
def training(self, images, image_shapes, features, gt_boxes):
pass
def inference(self, outputs, images, image_shapes, features, gt_boxes=None):
outputs = find_top_rpn_proposals(
outputs.predict_proposals(),
outputs.predict_objectness_logits(),
images,
image_shapes,
self.nms_thresh,
self.pre_nms_topk[self.training],
self.post_nms_topk[self.training],
self.min_box_side_len,
self.training,
)
results = []
for img in outputs:
im_boxes, img_box_logits = img
img_box_logits, inds = img_box_logits.sort(descending=True)
im_boxes = im_boxes[inds]
results.append((im_boxes, img_box_logits))
(proposal_boxes, logits) = tuple(map(list, zip(*results)))
return proposal_boxes, logits
def forward(self, images, image_shapes, features, gt_boxes=None):
"""
Args:
images (torch.Tensor): input images of length `N`
features (dict[str: Tensor])
gt_instances
"""
# features is dict, key = block level, v = feature_map
features = [features[f] for f in self.in_features]
pred_objectness_logits, pred_anchor_deltas = self.rpn_head(features)
anchors = self.anchor_generator(features)
outputs = RPNOutputs(
self.box2box_transform,
self.anchor_matcher,
self.batch_size_per_image,
self.positive_fraction,
images,
pred_objectness_logits,
pred_anchor_deltas,
anchors,
self.boundary_threshold,
gt_boxes,
self.smooth_l1_beta,
)
# For RPN-only models, the proposals are the final output
if self.training:
raise NotImplementedError()
return self.training(outputs, images, image_shapes, features, gt_boxes)
else:
return self.inference(outputs, images, image_shapes, features, gt_boxes)
class FastRCNNOutputLayers(nn.Module):
"""
Two linear layers for predicting Fast R-CNN outputs:
(1) proposal-to-detection box regression deltas
(2) classification scores
"""
def __init__(
self,
input_size,
num_classes,
cls_agnostic_bbox_reg,
box_dim=4,
use_attr=False,
num_attrs=-1,
):
"""
Args:
input_size (int): channels, or (channels, height, width)
num_classes (int)
cls_agnostic_bbox_reg (bool)
box_dim (int)
"""
super().__init__()
if not isinstance(input_size, int):
input_size = np.prod(input_size)
# (do + 1 for background class)
self.cls_score = nn.Linear(input_size, num_classes + 1)
num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim)
self.use_attr = use_attr
if use_attr:
"""
Modifications for VG in RoI heads
Embedding: {num_classes + 1} --> {input_size // 8}
Linear: {input_size + input_size // 8} --> {input_size // 4}
Linear: {input_size // 4} --> {num_attrs + 1}
"""
self.cls_embedding = nn.Embedding(num_classes + 1, input_size // 8)
self.fc_attr = nn.Linear(input_size + input_size // 8, input_size // 4)
self.attr_score = nn.Linear(input_size // 4, num_attrs + 1)
nn.init.normal_(self.cls_score.weight, std=0.01)
nn.init.normal_(self.bbox_pred.weight, std=0.001)
for item in [self.cls_score, self.bbox_pred]:
nn.init.constant_(item.bias, 0)
def forward(self, roi_features):
if roi_features.dim() > 2:
roi_features = torch.flatten(roi_features, start_dim=1)
scores = self.cls_score(roi_features)
proposal_deltas = self.bbox_pred(roi_features)
if self.use_attr:
_, max_class = scores.max(-1) # [b, c] --> [b]
cls_emb = self.cls_embedding(max_class) # [b] --> [b, 256]
roi_features = torch.cat([roi_features, cls_emb], -1) # [b, 2048] + [b, 256] --> [b, 2304]
roi_features = self.fc_attr(roi_features)
roi_features = F.relu(roi_features)
attr_scores = self.attr_score(roi_features)
return scores, attr_scores, proposal_deltas
else:
return scores, proposal_deltas
class GeneralizedRCNN(nn.Module):
def __init__(self, cfg):
super().__init__()
self.device = torch.device(cfg.MODEL.DEVICE)
self.backbone = build_backbone(cfg)
self.proposal_generator = RPN(cfg, self.backbone.output_shape())
self.roi_heads = Res5ROIHeads(cfg, self.backbone.output_shape())
self.roi_outputs = ROIOutputs(cfg)
self.to(self.device)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
config = kwargs.pop("config", None)
state_dict = kwargs.pop("state_dict", None)
cache_dir = kwargs.pop("cache_dir", None)
from_tf = kwargs.pop("from_tf", False)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
use_cdn = kwargs.pop("use_cdn", True)
# Load config if we don't provide a configuration
if not isinstance(config, Config):
config_path = config if config is not None else pretrained_model_name_or_path
# try:
config = Config.from_pretrained(
config_path,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
)
# Load model
if pretrained_model_name_or_path is not None:
if os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
# Load from a PyTorch checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
else:
raise EnvironmentError(
"Error no file named {} found in directory {} ".format(
WEIGHTS_NAME,
pretrained_model_name_or_path,
)
)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
archive_file = pretrained_model_name_or_path
elif os.path.isfile(pretrained_model_name_or_path + ".index"):
assert (
from_tf
), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
pretrained_model_name_or_path + ".index"
)
archive_file = pretrained_model_name_or_path + ".index"
else:
archive_file = hf_bucket_url(
pretrained_model_name_or_path,
filename=WEIGHTS_NAME,
use_cdn=use_cdn,
)
try:
# Load from URL or cache if already cached
resolved_archive_file = cached_path(
archive_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
if resolved_archive_file is None:
raise EnvironmentError
except EnvironmentError:
msg = f"Can't load weights for '{pretrained_model_name_or_path}'."
raise EnvironmentError(msg)
if resolved_archive_file == archive_file:
print("loading weights file {}".format(archive_file))
else:
print("loading weights file {} from cache at {}".format(archive_file, resolved_archive_file))
else:
resolved_archive_file = None
# Instantiate model.
model = cls(config)
if state_dict is None:
try:
try:
state_dict = torch.load(resolved_archive_file, map_location="cpu")
except Exception:
state_dict = load_checkpoint(resolved_archive_file)
except Exception:
raise OSError(
"Unable to load weights from pytorch checkpoint file. "
"If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "
)
missing_keys = []
unexpected_keys = []
error_msgs = []
# Convert old format to new format if needed from a PyTorch state_dict
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if "gamma" in key:
new_key = key.replace("gamma", "weight")
if "beta" in key:
new_key = key.replace("beta", "bias")
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
model_to_load = model
model_to_load.load_state_dict(state_dict)
if model.__class__.__name__ != model_to_load.__class__.__name__:
base_model_state_dict = model_to_load.state_dict().keys()
head_model_state_dict_without_base_prefix = [
key.split(cls.base_model_prefix + ".")[-1] for key in model.state_dict().keys()
]
missing_keys.extend(head_model_state_dict_without_base_prefix - base_model_state_dict)
if len(unexpected_keys) > 0:
print(
f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when "
f"initializing {model.__class__.__name__}: {unexpected_keys}\n"
f"- This IS expected if you are initializing {model.__class__.__name__} from the checkpoint of a model trained on another task "
f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n"
f"- This IS NOT expected if you are initializing {model.__class__.__name__} from the checkpoint of a model that you expect "
f"to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
)
else:
print(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
if len(missing_keys) > 0:
print(
f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at {pretrained_model_name_or_path} "
f"and are newly initialized: {missing_keys}\n"
f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
)
else:
print(
f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at {pretrained_model_name_or_path}.\n"
f"If your task is similar to the task the model of the checkpoint was trained on, "
f"you can already use {model.__class__.__name__} for predictions without further training."
)
if len(error_msgs) > 0:
raise RuntimeError(
"Error(s) in loading state_dict for {}:\n\t{}".format(
model.__class__.__name__, "\n\t".join(error_msgs)
)
)
# Set model in evaluation mode to deactivate DropOut modules by default
model.eval()
return model
def forward(
self,
images,
image_shapes,
gt_boxes=None,
proposals=None,
scales_yx=None,
**kwargs,
):
"""
kwargs:
max_detections (int), return_tensors {"np", "pt", None}, padding {None,
"max_detections"}, pad_value (int), location = {"cuda", "cpu"}
"""
if self.training:
raise NotImplementedError()
return self.inference(
images=images,
image_shapes=image_shapes,
gt_boxes=gt_boxes,
proposals=proposals,
scales_yx=scales_yx,
**kwargs,
)
@torch.no_grad()
def inference(
self,
images,
image_shapes,
gt_boxes=None,
proposals=None,
scales_yx=None,
**kwargs,
):
# run images through backbone
original_sizes = image_shapes * scales_yx
features = self.backbone(images)
# generate proposals if none are available
if proposals is None:
proposal_boxes, _ = self.proposal_generator(images, image_shapes, features, gt_boxes)
else:
assert proposals is not None
# pool object features from either gt_boxes, or from proposals
obj_logits, attr_logits, box_deltas, feature_pooled = self.roi_heads(features, proposal_boxes, gt_boxes)
# prepare FRCNN Outputs and select top proposals
boxes, classes, class_probs, attrs, attr_probs, roi_features = self.roi_outputs(
obj_logits=obj_logits,
attr_logits=attr_logits,
box_deltas=box_deltas,
pred_boxes=proposal_boxes,
features=feature_pooled,
sizes=image_shapes,
scales=scales_yx,
)
# will we pad???
subset_kwargs = {
"max_detections": kwargs.get("max_detections", None),
"return_tensors": kwargs.get("return_tensors", None),
"pad_value": kwargs.get("pad_value", 0),
"padding": kwargs.get("padding", None),
}
preds_per_image = torch.tensor([p.size(0) for p in boxes])
boxes = pad_list_tensors(boxes, preds_per_image, **subset_kwargs)
classes = pad_list_tensors(classes, preds_per_image, **subset_kwargs)
class_probs = pad_list_tensors(class_probs, preds_per_image, **subset_kwargs)
attrs = pad_list_tensors(attrs, preds_per_image, **subset_kwargs)
attr_probs = pad_list_tensors(attr_probs, preds_per_image, **subset_kwargs)
roi_features = pad_list_tensors(roi_features, preds_per_image, **subset_kwargs)
subset_kwargs["padding"] = None
preds_per_image = pad_list_tensors(preds_per_image, None, **subset_kwargs)
sizes = pad_list_tensors(image_shapes, None, **subset_kwargs)
normalized_boxes = norm_box(boxes, original_sizes)
return OrderedDict(
{
"obj_ids": classes,
"obj_probs": class_probs,
"attr_ids": attrs,
"attr_probs": attr_probs,
"boxes": boxes,
"sizes": sizes,
"preds_per_image": preds_per_image,
"roi_features": roi_features,
"normalized_boxes": normalized_boxes,
}
)
|
AdaMix/examples/research_projects/lxmert/modeling_frcnn.py/0
|
{
"file_path": "AdaMix/examples/research_projects/lxmert/modeling_frcnn.py",
"repo_id": "AdaMix",
"token_count": 34761
}
| 44 |
# flake8: noqa
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
|
AdaMix/examples/research_projects/movement-pruning/emmental/__init__.py/0
|
{
"file_path": "AdaMix/examples/research_projects/movement-pruning/emmental/__init__.py",
"repo_id": "AdaMix",
"token_count": 107
}
| 45 |
#!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=dmar
export MAX_LEN=128
python finetune.py \
--learning_rate=3e-4 \
--do_train \
--do_predict \
--fp16 \
--val_check_interval 0.25 \
--data_dir $ENRO_DIR \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
--freeze_encoder --freeze_embeds \
--train_batch_size=$BS --eval_batch_size=$BS \
--tokenizer_name $m --model_name_or_path $m \
--warmup_steps 500 --sortish_sampler --logger_name wandb \
--gpus 1 --fp16_opt_level=O1 --task translation --num_sanity_val_steps=0 \
"$@"
|
AdaMix/examples/research_projects/seq2seq-distillation/distil_marian_no_teacher.sh/0
|
{
"file_path": "AdaMix/examples/research_projects/seq2seq-distillation/distil_marian_no_teacher.sh",
"repo_id": "AdaMix",
"token_count": 274
}
| 46 |
#!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--do_train \
--val_check_interval=0.25 \
--adam_eps 1e-06 \
--num_train_epochs 6 --src_lang en_XX --tgt_lang ro_RO \
--data_dir $ENRO_DIR \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
--train_batch_size=$BS --eval_batch_size=$BS \
--task translation \
--warmup_steps 500 \
--freeze_embeds \
--model_name_or_path=facebook/mbart-large-cc25 \
"$@"
|
AdaMix/examples/research_projects/seq2seq-distillation/train_mbart_cc25_enro.sh/0
|
{
"file_path": "AdaMix/examples/research_projects/seq2seq-distillation/train_mbart_cc25_enro.sh",
"repo_id": "AdaMix",
"token_count": 273
}
| 47 |
# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
import torch
from transformers.file_utils import is_apex_available
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow, torch_device
SRC_DIRS = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-generation",
"text-classification",
"token-classification",
"language-modeling",
"multiple-choice",
"question-answering",
"seq2seq",
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm
import run_generation
import run_glue
import run_mlm
import run_ner
import run_qa as run_squad
import run_summarization
import run_swag
import run_translation
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.ArgumentParser()
parser.add_argument("-f")
args = parser.parse_args()
return args.f
def get_results(output_dir):
results = {}
path = os.path.join(output_dir, "all_results.json")
if os.path.exists(path):
with open(path, "r") as f:
results = json.load(f)
else:
raise ValueError(f"can't find {path}")
return results
def is_cuda_and_apex_available():
is_using_cuda = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
class ExamplesTests(TestCasePlus):
def test_run_glue(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16")
with patch.object(sys, "argv", testargs):
run_glue.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
def test_run_clm(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_clm.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
if torch_device != "cuda":
testargs.append("--no_cuda")
with patch.object(sys, "argv", testargs):
run_clm.main()
result = get_results(tmp_dir)
self.assertLess(result["perplexity"], 100)
def test_run_mlm(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--prediction_loss_only
--num_train_epochs=1
""".split()
if torch_device != "cuda":
testargs.append("--no_cuda")
with patch.object(sys, "argv", testargs):
run_mlm.main()
result = get_results(tmp_dir)
self.assertLess(result["perplexity"], 42)
def test_run_ner(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
epochs = 7 if get_gpu_count() > 1 else 2
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
""".split()
if torch_device != "cuda":
testargs.append("--no_cuda")
with patch.object(sys, "argv", testargs):
run_ner.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
self.assertGreaterEqual(result["eval_precision"], 0.75)
self.assertLess(result["eval_loss"], 0.5)
def test_run_squad(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_squad.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=10
--warmup_steps=2
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(sys, "argv", testargs):
run_squad.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["f1"], 30)
self.assertGreaterEqual(result["exact"], 30)
def test_run_swag(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_swag.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=20
--warmup_steps=2
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(sys, "argv", testargs):
run_swag.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.8)
def test_generation(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"]
if is_cuda_and_apex_available():
testargs.append("--fp16")
model_type, model_name = (
"--model_type=gpt2",
"--model_name_or_path=sshleifer/tiny-gpt2",
)
with patch.object(sys, "argv", testargs + [model_type, model_name]):
result = run_generation.main()
self.assertGreaterEqual(len(result[0]), 10)
@slow
def test_run_summarization(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=50
--warmup_steps=8
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(sys, "argv", testargs):
run_summarization.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_rouge1"], 10)
self.assertGreaterEqual(result["eval_rouge2"], 2)
self.assertGreaterEqual(result["eval_rougeL"], 7)
self.assertGreaterEqual(result["eval_rougeLsum"], 7)
@slow
def test_run_translation(self):
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_translation.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--max_steps=50
--warmup_steps=8
--do_train
--do_eval
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
--source_lang en_XX
--target_lang ro_RO
""".split()
with patch.object(sys, "argv", testargs):
run_translation.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_bleu"], 30)
|
AdaMix/examples/test_examples.py/0
|
{
"file_path": "AdaMix/examples/test_examples.py",
"repo_id": "AdaMix",
"token_count": 5464
}
| 48 |
<jupyter_start><jupyter_text>Tokenization doesn't have to be slow ! IntroductionBefore going deep into any Machine Learning or Deep Learning Natural Language Processing models, every practitionershould find a way to map raw input strings to a representation understandable by a trainable model.One very simple approach would be to split inputs over every space and assign an identifier to each word. This approachwould look similar to the code below in python```pythons = "very long corpus..."words = s.split(" ") Split over spacevocabulary = dict(enumerate(set(words))) Map storing the word to it's corresponding id```This approach might work well if your vocabulary remains small as it would store every word (or **token**) present in your originalinput. Moreover, word variations like "cat" and "cats" would not share the same identifiers even if their meaning is quite close. Subtoken TokenizationTo overcome the issues described above, recent works have been done on tokenization, leveraging "subtoken" tokenization.**Subtokens** extends the previous splitting strategy to furthermore explode a word into grammatically logicial sub-components learnedfrom the data.Taking our previous example of the words __cat__ and __cats__, a sub-tokenization of the word __cats__ would be [cat, s]. Where the prefix _""_ indicates a subtoken of the initial input. Such training algorithms might extract sub-tokens such as _"ing"_, _"ed"_ over English corpus.As you might think of, this kind of sub-tokens construction leveraging compositions of _"pieces"_ overall reduces the sizeof the vocabulary you have to carry to train a Machine Learning model. On the other side, as one token might be explodedinto multiple subtokens, the input of your model might increase and become an issue on model with non-linear complexity over the input sequence's length. Among all the tokenization algorithms, we can highlight a few subtokens algorithms used in Transformers-based SoTA models : - [Byte Pair Encoding (BPE) - Neural Machine Translation of Rare Words with Subword Units (Sennrich et al., 2015)](https://arxiv.org/abs/1508.07909)- [Word Piece - Japanese and Korean voice search (Schuster, M., and Nakajima, K., 2015)](https://research.google/pubs/pub37842/)- [Unigram Language Model - Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (Kudo, T., 2018)](https://arxiv.org/abs/1804.10959)- [Sentence Piece - A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Taku Kudo and John Richardson, 2018)](https://arxiv.org/abs/1808.06226)Going through all of them is out of the scope of this notebook, so we will just highlight how you can use them. @huggingface/tokenizers library Along with the transformers library, we @huggingface provide a blazing fast tokenization libraryable to train, tokenize and decode dozens of Gb/s of text on a common multi-core machine.The library is written in Rust allowing us to take full advantage of multi-core parallel computations in a native and memory-aware way, on-top of which we provide bindings for Python and NodeJS (more bindings may be added in the future). We designed the library so that it provides all the required blocks to create end-to-end tokenizers in an interchangeable way. In that sense, we providethese various components: - **Normalizer**: Executes all the initial transformations over the initial input string. For example when you need tolowercase some text, maybe strip it, or even apply one of the common unicode normalization process, you will add a Normalizer. - **PreTokenizer**: In charge of splitting the initial input string. That's the component that decides where and how topre-segment the origin string. The simplest example would be like we saw before, to simply split on spaces.- **Model**: Handles all the sub-token discovery and generation, this part is trainable and really dependant of your input data.- **Post-Processor**: Provides advanced construction features to be compatible with some of the Transformers-based SoTAmodels. For instance, for BERT it would wrap the tokenized sentence around [CLS] and [SEP] tokens.- **Decoder**: In charge of mapping back a tokenized input to the original string. The decoder is usually chosen accordingto the `PreTokenizer` we used previously.- **Trainer**: Provides training capabilities to each model.For each of the components above we provide multiple implementations:- **Normalizer**: Lowercase, Unicode (NFD, NFKD, NFC, NFKC), Bert, Strip, ...- **PreTokenizer**: ByteLevel, WhitespaceSplit, CharDelimiterSplit, Metaspace, ...- **Model**: WordLevel, BPE, WordPiece- **Post-Processor**: BertProcessor, ...- **Decoder**: WordLevel, BPE, WordPiece, ...All of these building blocks can be combined to create working tokenization pipelines. In the next section we will go over our first pipeline. Alright, now we are ready to implement our first tokenization pipeline through `tokenizers`. For this, we will train a Byte-Pair Encoding (BPE) tokenizer on a quite small input for the purpose of this notebook.We will work with [the file from Peter Norving](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjYp9Ppru_nAhUBzIUKHfbUAG8QFjAAegQIBhAB&url=https%3A%2F%2Fnorvig.com%2Fbig.txt&usg=AOvVaw2ed9iwhcP1RKUiEROs15Dz).This file contains around 130.000 lines of raw text that will be processed by the library to generate a working tokenizer.<jupyter_code>!pip install tokenizers
BIG_FILE_URL = 'https://raw.githubusercontent.com/dscape/spell/master/test/resources/big.txt'
# Let's download the file and save it somewhere
from requests import get
with open('big.txt', 'wb') as big_f:
response = get(BIG_FILE_URL, )
if response.status_code == 200:
big_f.write(response.content)
else:
print("Unable to get the file: {}".format(response.reason))<jupyter_output><empty_output><jupyter_text>Now that we have our training data we need to create the overall pipeline for the tokenizer<jupyter_code># For the user's convenience `tokenizers` provides some very high-level classes encapsulating
# the overall pipeline for various well-known tokenization algorithm.
# Everything described below can be replaced by the ByteLevelBPETokenizer class.
from tokenizers import Tokenizer
from tokenizers.decoders import ByteLevel as ByteLevelDecoder
from tokenizers.models import BPE
from tokenizers.normalizers import Lowercase, NFKC, Sequence
from tokenizers.pre_tokenizers import ByteLevel
# First we create an empty Byte-Pair Encoding model (i.e. not trained model)
tokenizer = Tokenizer(BPE())
# Then we enable lower-casing and unicode-normalization
# The Sequence normalizer allows us to combine multiple Normalizer that will be
# executed in order.
tokenizer.normalizer = Sequence([
NFKC(),
Lowercase()
])
# Our tokenizer also needs a pre-tokenizer responsible for converting the input to a ByteLevel representation.
tokenizer.pre_tokenizer = ByteLevel()
# And finally, let's plug a decoder so we can recover from a tokenized input to the original one
tokenizer.decoder = ByteLevelDecoder()<jupyter_output><empty_output><jupyter_text>The overall pipeline is now ready to be trained on the corpus we downloaded earlier in this notebook.<jupyter_code>from tokenizers.trainers import BpeTrainer
# We initialize our trainer, giving him the details about the vocabulary we want to generate
trainer = BpeTrainer(vocab_size=25000, show_progress=True, initial_alphabet=ByteLevel.alphabet())
tokenizer.train(files=["big.txt"], trainer=trainer)
print("Trained vocab size: {}".format(tokenizer.get_vocab_size()))<jupyter_output>Trained vocab size: 25000<jupyter_text>Et voilà ! You trained your very first tokenizer from scratch using `tokenizers`. Of course, this covers only the basics, and you may want to have a look at the `add_special_tokens` or `special_tokens` parameterson the `Trainer` class, but the overall process should be very similar.We can save the content of the model to reuse it later.<jupyter_code># You will see the generated files in the output.
tokenizer.model.save('.')<jupyter_output><empty_output><jupyter_text>Now, let load the trained model and start using out newly trained tokenizer<jupyter_code># Let's tokenizer a simple input
tokenizer.model = BPE('vocab.json', 'merges.txt')
encoding = tokenizer.encode("This is a simple input to be tokenized")
print("Encoded string: {}".format(encoding.tokens))
decoded = tokenizer.decode(encoding.ids)
print("Decoded string: {}".format(decoded))<jupyter_output>Encoded string: ['Ġthis', 'Ġis', 'Ġa', 'Ġsimple', 'Ġin', 'put', 'Ġto', 'Ġbe', 'Ġtoken', 'ized']
Decoded string: this is a simple input to be tokenized
|
AdaMix/notebooks/01-training-tokenizers.ipynb/0
|
{
"file_path": "AdaMix/notebooks/01-training-tokenizers.ipynb",
"repo_id": "AdaMix",
"token_count": 2371
}
| 49 |
#!/usr/bin/env python
# coding: utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
mname = "facebook/wmt19-en-de"
tokenizer = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
config = FSMTConfig.from_pretrained(mname)
config.update(dict(
d_model=4,
encoder_layers=1, decoder_layers=1,
encoder_ffn_dim=4, decoder_ffn_dim=4,
encoder_attention_heads=1, decoder_attention_heads=1))
tiny_model = FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")
# Test
batch = tokenizer(["Making tiny model"], return_tensors="pt")
outputs = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
mname_tiny = "tiny-wmt19-en-de"
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"Generated {mname_tiny}")
# Upload
# transformers-cli upload tiny-wmt19-en-de
|
AdaMix/scripts/fsmt/fsmt-make-tiny-model.py/0
|
{
"file_path": "AdaMix/scripts/fsmt/fsmt-make-tiny-model.py",
"repo_id": "AdaMix",
"token_count": 698
}
| 50 |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Tuple
from ..file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
from ..utils import logging
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
logger = logging.get_logger(__name__)
@dataclass
class PyTorchBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""
This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
deleted
"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
positive_arg = deprecated_arg[3:]
setattr(self, positive_arg, not kwargs.pop(deprecated_arg))
logger.warning(
f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or {positive_arg}={kwargs[positive_arg]}"
)
self.torchscript = kwargs.pop("torchscript", self.torchscript)
self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
super().__init__(**kwargs)
torchscript: bool = field(default=False, metadata={"help": "Trace the models using torchscript"})
torch_xla_tpu_print_metrics: bool = field(default=False, metadata={"help": "Print Xla/PyTorch tpu metrics"})
fp16_opt_level: str = field(
default="O1",
metadata={
"help": (
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
)
},
)
@cached_property
@torch_required
def _setup_devices(self) -> Tuple["torch.device", int]:
logger.info("PyTorch: setting up devices")
if not self.cuda:
device = torch.device("cpu")
n_gpu = 0
elif is_torch_tpu_available():
device = xm.xla_device()
n_gpu = 0
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
return device, n_gpu
@property
def is_tpu(self):
return is_torch_tpu_available() and self.tpu
@property
@torch_required
def device_idx(self) -> int:
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
@torch_required
def device(self) -> "torch.device":
return self._setup_devices[0]
@property
@torch_required
def n_gpu(self):
return self._setup_devices[1]
@property
def is_gpu(self):
return self.n_gpu > 0
|
AdaMix/src/transformers/benchmark/benchmark_args.py/0
|
{
"file_path": "AdaMix/src/transformers/benchmark/benchmark_args.py",
"repo_id": "AdaMix",
"token_count": 1591
}
| 51 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Configuration base class and utilities."""
import copy
import json
import os
from typing import Any, Dict, Tuple, Union
from . import __version__
from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_offline_mode, is_remote_url
from .utils import logging
logger = logging.get_logger(__name__)
class PretrainedConfig(object):
r"""
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
Note: A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights. It only affects the model's configuration.
Class attributes (overridden by derived classes)
- **model_type** (:obj:`str`): An identifier for the model type, serialized into the JSON file, and used to
recreate the correct object in :class:`~transformers.AutoConfig`.
- **is_composition** (:obj:`bool`): Whether the config class is composed of multiple sub-configs. In this case
the config has to be initialized from two or more configs of type :class:`~transformers.PretrainedConfig`
like: :class:`~transformers.EncoderDecoderConfig` or :class:`~RagConfig`.
- **keys_to_ignore_at_inference** (:obj:`List[str]`): A list of keys to ignore by default when looking at
dictionary outputs of the model during inference.
Args:
name_or_path (:obj:`str`, `optional`, defaults to :obj:`""`):
Store the string that was passed to :func:`~transformers.PreTrainedModel.from_pretrained` or
:func:`~transformers.TFPreTrainedModel.from_pretrained` as ``pretrained_model_name_or_path`` if the
configuration was created with such a method.
output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should return all hidden-states.
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the model should returns all attentions.
return_dict (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a plain
tuple.
is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether the model is used as an encoder/decoder or not.
is_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether the model is used as decoder or not (in which case it's used as an encoder).
add_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
that can be used as decoder models within the `:class:~transformers.EncoderDecoderModel` class, which
consists of all models in ``AUTO_MODELS_FOR_CAUSAL_LM``.
tie_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`)
Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
and decoder model to have the exact same parameter names.
prune_heads (:obj:`Dict[int, List[int]]`, `optional`, defaults to :obj:`{}`):
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
heads to prune in said layer.
For instance ``{1: [0, 2], 2: [2, 3]}`` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`):
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of :obj:`0` means
that the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes
:obj:`n` < sequence_length embeddings at a time. For more information on feed forward chunking, see `How
does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
Parameters for sequence generation
- **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by default in the
:obj:`generate` method of the model.
- **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by default in the
:obj:`generate` method of the model.
- **do_sample** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default in the
:obj:`generate` method of the model. Whether or not to use sampling ; use greedy decoding otherwise.
- **early_stopping** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default
in the :obj:`generate` method of the model. Whether to stop the beam search when at least ``num_beams``
sentences are finished per batch or not.
- **num_beams** (:obj:`int`, `optional`, defaults to 1) -- Number of beams for beam search that will be used by
default in the :obj:`generate` method of the model. 1 means no beam search.
- **num_beam_groups** (:obj:`int`, `optional`, defaults to 1) -- Number of groups to divide :obj:`num_beams`
into in order to ensure diversity among different groups of beams that will be used by default in the
:obj:`generate` method of the model. 1 means no group beam search.
- **diversity_penalty** (:obj:`float`, `optional`, defaults to 0.0) -- Value to control diversity for group
beam search. that will be used by default in the :obj:`generate` method of the model. 0 means no diversity
penalty. The higher the penalty, the more diverse are the outputs.
- **temperature** (:obj:`float`, `optional`, defaults to 1) -- The value used to module the next token
probabilities that will be used by default in the :obj:`generate` method of the model. Must be strictly
positive.
- **top_k** (:obj:`int`, `optional`, defaults to 50) -- Number of highest probability vocabulary tokens to keep
for top-k-filtering that will be used by default in the :obj:`generate` method of the model.
- **top_p** (:obj:`float`, `optional`, defaults to 1) -- Value that will be used by default in the
:obj:`generate` method of the model for ``top_p``. If set to float < 1, only the most probable tokens with
probabilities that add up to ``top_p`` or higher are kept for generation.
- **repetition_penalty** (:obj:`float`, `optional`, defaults to 1) -- Parameter for repetition penalty that
will be used by default in the :obj:`generate` method of the model. 1.0 means no penalty.
- **length_penalty** (:obj:`float`, `optional`, defaults to 1) -- Exponential penalty to the length that will
be used by default in the :obj:`generate` method of the model.
- **no_repeat_ngram_size** (:obj:`int`, `optional`, defaults to 0) -- Value that will be used by default in the
:obj:`generate` method of the model for ``no_repeat_ngram_size``. If set to int > 0, all ngrams of that size
can only occur once.
- **encoder_no_repeat_ngram_size** (:obj:`int`, `optional`, defaults to 0) -- Value that will be used by
default in the :obj:`generate` method of the model for ``encoder_no_repeat_ngram_size``. If set to int > 0,
all ngrams of that size that occur in the ``encoder_input_ids`` cannot occur in the ``decoder_input_ids``.
- **bad_words_ids** (:obj:`List[int]`, `optional`) -- List of token ids that are not allowed to be generated
that will be used by default in the :obj:`generate` method of the model. In order to get the tokens of the
words that should not appear in the generated text, use :obj:`tokenizer.encode(bad_word,
add_prefix_space=True)`.
- **num_return_sequences** (:obj:`int`, `optional`, defaults to 1) -- Number of independently computed returned
sequences for each element in the batch that will be used by default in the :obj:`generate` method of the
model.
- **output_scores** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether the model should return the
logits when used for generation
- **return_dict_in_generate** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether the model should
return a :class:`~transformers.file_utils.ModelOutput` instead of a :obj:`torch.LongTensor`
- **forced_bos_token_id** (:obj:`int`, `optional`) -- The id of the token to force as the first generated token
after the :obj:`decoder_start_token_id`. Useful for multilingual models like :doc:`mBART
<../model_doc/mbart>` where the first generated token needs to be the target language token.
- **forced_eos_token_id** (:obj:`int`, `optional`) -- The id of the token to force as the last generated token
when :obj:`max_length` is reached.
Parameters for fine-tuning tasks
- **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the model
pretrained weights.
- **finetuning_task** (:obj:`str`, `optional`) -- Name of the task used to fine-tune the model. This can be
used when converting from an original (TensorFlow or PyTorch) checkpoint.
- **id2label** (:obj:`Dict[int, str]`, `optional`) -- A map from index (for instance prediction index, or
target index) to label.
- **label2id** (:obj:`Dict[str, int]`, `optional`) -- A map from label to index for the model.
- **num_labels** (:obj:`int`, `optional`) -- Number of labels to use in the last layer added to the model,
typically for a classification task.
- **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for the
current task.
Parameters linked to the tokenizer
- **tokenizer_class** (:obj:`str`, `optional`) -- The name of the associated tokenizer class to use (if none is
set, will use the tokenizer associated to the model by default).
- **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each text
before calling the model.
- **bos_token_id** (:obj:`int`, `optional`)) -- The id of the `beginning-of-stream` token.
- **pad_token_id** (:obj:`int`, `optional`)) -- The id of the `padding` token.
- **eos_token_id** (:obj:`int`, `optional`)) -- The id of the `end-of-stream` token.
- **decoder_start_token_id** (:obj:`int`, `optional`)) -- If an encoder-decoder model starts decoding with a
different token than `bos`, the id of that token.
- **sep_token_id** (:obj:`int`, `optional`)) -- The id of the `separation` token.
PyTorch specific parameters
- **torchscript** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should be
used with Torchscript.
- **tie_word_embeddings** (:obj:`bool`, `optional`, defaults to :obj:`True`) -- Whether the model's input and
output word embeddings should be tied. Note that this is only relevant if the model has a output word
embedding layer.
TensorFlow specific parameters
- **use_bfloat16** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should use
BFloat16 scalars (only used by some TensorFlow models).
"""
model_type: str = ""
is_composition: bool = False
def __init__(self, **kwargs):
# Attributes with defaults
self.return_dict = kwargs.pop("return_dict", True)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_attentions = kwargs.pop("output_attentions", False)
self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models
self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
self.pruned_heads = kwargs.pop("pruned_heads", {})
self.tie_word_embeddings = kwargs.pop(
"tie_word_embeddings", True
) # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models.
# Is decoder is used in encoder-decoder models to differentiate encoder from decoder
self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
self.is_decoder = kwargs.pop("is_decoder", False)
self.add_cross_attention = kwargs.pop("add_cross_attention", False)
self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False)
# Parameters for sequence generation
self.max_length = kwargs.pop("max_length", 20)
self.min_length = kwargs.pop("min_length", 0)
self.do_sample = kwargs.pop("do_sample", False)
self.early_stopping = kwargs.pop("early_stopping", False)
self.num_beams = kwargs.pop("num_beams", 1)
self.num_beam_groups = kwargs.pop("num_beam_groups", 1)
self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0)
self.temperature = kwargs.pop("temperature", 1.0)
self.top_k = kwargs.pop("top_k", 50)
self.top_p = kwargs.pop("top_p", 1.0)
self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0)
self.bad_words_ids = kwargs.pop("bad_words_ids", None)
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
self.output_scores = kwargs.pop("output_scores", False)
self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False)
self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None)
self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None)
# Fine-tuning task arguments
self.architectures = kwargs.pop("architectures", None)
self.finetuning_task = kwargs.pop("finetuning_task", None)
self.id2label = kwargs.pop("id2label", None)
self.label2id = kwargs.pop("label2id", None)
if self.id2label is not None:
kwargs.pop("num_labels", None)
self.id2label = dict((int(key), value) for key, value in self.id2label.items())
# Keys are always strings in JSON so convert ids to int here.
else:
self.num_labels = kwargs.pop("num_labels", 2)
# Tokenizer arguments TODO: eventually tokenizer and models should share the same config
self.tokenizer_class = kwargs.pop("tokenizer_class", None)
self.prefix = kwargs.pop("prefix", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
self.sep_token_id = kwargs.pop("sep_token_id", None)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# task specific arguments
self.task_specific_params = kwargs.pop("task_specific_params", None)
# TPU arguments
if kwargs.pop("xla_device", None) is not None:
logger.warn(
"The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can "
"safely remove it from your `config.json` file."
)
# Name or path to the pretrained checkpoint
self._name_or_path = str(kwargs.pop("name_or_path", ""))
# Drop the transformers version info
kwargs.pop("transformers_version", None)
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error("Can't set {} with value {} for {}".format(key, value, self))
raise err
@property
def name_or_path(self) -> str:
return self._name_or_path
@name_or_path.setter
def name_or_path(self, value):
self._name_or_path = str(value) # Make sure that name_or_path is a string (for JSON encoding)
@property
def use_return_dict(self) -> bool:
"""
:obj:`bool`: Whether or not return :class:`~transformers.file_utils.ModelOutput` instead of tuples.
"""
# If torchscript is set, force `return_dict=False` to avoid jit errors
return self.return_dict and not self.torchscript
@property
def num_labels(self) -> int:
"""
:obj:`int`: The number of labels for classification models.
"""
return len(self.id2label)
@num_labels.setter
def num_labels(self, num_labels: int):
if self.id2label is None or len(self.id2label) != num_labels:
self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
"""
Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
:func:`~transformers.PretrainedConfig.from_pretrained` class method.
Args:
save_directory (:obj:`str` or :obj:`os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
"""
if os.path.isfile(save_directory):
raise AssertionError("Provided path ({}) should be a directory, not a file".format(save_directory))
os.makedirs(save_directory, exist_ok=True)
# If we save using the predefined names, we can load using `from_pretrained`
output_config_file = os.path.join(save_directory, CONFIG_NAME)
self.to_json_file(output_config_file, use_diff=True)
logger.info(f"Configuration saved in {output_config_file}")
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
r"""
Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pretrained model
configuration.
Args:
pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
This can be either:
- a string, the `model id` of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a configuration file saved using the
:func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.,
``./my_model_directory/configuration.json``.
cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.
resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.
proxies (:obj:`Dict[str, str]`, `optional`):
A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (:obj:`str` or `bool`, `optional`):
The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
identifier allowed by git.
return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`False`, then this function returns just the final configuration object.
If :obj:`True`, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs`
is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e.,
the part of ``kwargs`` which has not been used to update ``config`` and is otherwise ignored.
kwargs (:obj:`Dict[str, Any]`, `optional`):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the ``return_unused_kwargs`` keyword parameter.
.. note::
Passing :obj:`use_auth_token=True` is required when you want to use a private model.
Returns:
:class:`PretrainedConfig`: The configuration object instantiated from this pretrained model.
Examples::
# We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained('bert-base-uncased') # Download configuration from huggingface.co and cache.
config = BertConfig.from_pretrained('./test/saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
config = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True,
foo=False, return_unused_kwargs=True)
assert config.output_attentions == True
assert unused_kwargs == {'foo': False}
"""
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
return cls.from_dict(config_dict, **kwargs)
@classmethod
def get_config_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
:class:`~transformers.PretrainedConfig` using ``from_dict``.
Parameters:
pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
Returns:
:obj:`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
else:
config_file = hf_bucket_url(
pretrained_model_name_or_path, filename=CONFIG_NAME, revision=revision, mirror=None
)
try:
# Load from URL or cache if already cached
resolved_config_file = cached_path(
config_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
# Load config dict
config_dict = cls._dict_from_json_file(resolved_config_file)
except EnvironmentError as err:
logger.error(err)
msg = (
f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
)
raise EnvironmentError(msg)
except json.JSONDecodeError:
msg = (
"Couldn't reach server at '{}' to download configuration file or "
"configuration file is not a valid JSON file. "
"Please check network or file content here: {}.".format(config_file, resolved_config_file)
)
raise EnvironmentError(msg)
if resolved_config_file == config_file:
logger.info("loading configuration file {}".format(config_file))
else:
logger.info("loading configuration file {} from cache at {}".format(config_file, resolved_config_file))
return config_dict, kwargs
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig":
"""
Instantiates a :class:`~transformers.PretrainedConfig` from a Python dictionary of parameters.
Args:
config_dict (:obj:`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the
:func:`~transformers.PretrainedConfig.get_config_dict` method.
kwargs (:obj:`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
:class:`PretrainedConfig`: The configuration object instantiated from those parameters.
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
config = cls(**config_dict)
if hasattr(config, "pruned_heads"):
config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())
# Update config with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info("Model config %s", str(config))
if return_unused_kwargs:
return config, kwargs
else:
return config
@classmethod
def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
"""
Instantiates a :class:`~transformers.PretrainedConfig` from the path to a JSON file of parameters.
Args:
json_file (:obj:`str` or :obj:`os.PathLike`):
Path to the JSON file containing the parameters.
Returns:
:class:`PretrainedConfig`: The configuration object instantiated from that JSON file.
"""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
def __eq__(self, other):
return self.__dict__ == other.__dict__
def __repr__(self):
return "{} {}".format(self.__class__.__name__, self.to_json_string())
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
:obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = PretrainedConfig().to_dict()
# get class specific config dict
class_config_dict = self.__class__().to_dict() if not self.is_composition else {}
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if (
key not in default_config_dict
or key == "transformers_version"
or value != default_config_dict[key]
or (key in class_config_dict and value != class_config_dict[key])
):
serializable_config_dict[key] = value
return serializable_config_dict
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
:obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
if hasattr(self.__class__, "model_type"):
output["model_type"] = self.__class__.model_type
# Transformers version when serializing the model
output["transformers_version"] = __version__
return output
def to_json_string(self, use_diff: bool = True) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, only the difference between the config instance and the default
``PretrainedConfig()`` is serialized to JSON string.
Returns:
:obj:`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
"""
Save this instance to a JSON file.
Args:
json_file_path (:obj:`str` or :obj:`os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, only the difference between the config instance and the default
``PretrainedConfig()`` is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string(use_diff=use_diff))
def update(self, config_dict: Dict[str, Any]):
"""
Updates attributes of this class with attributes from ``config_dict``.
Args:
config_dict (:obj:`Dict[str, Any]`): Dictionary of attributes that shall be updated for this class.
"""
for key, value in config_dict.items():
setattr(self, key, value)
|
AdaMix/src/transformers/configuration_utils.py/0
|
{
"file_path": "AdaMix/src/transformers/configuration_utils.py",
"repo_id": "AdaMix",
"token_count": 13394
}
| 52 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Very heavily inspired by the official evaluation script for SQuAD version 2.0 which was modified by XLNet authors to
update `find_best_threshold` scripts for SQuAD V2.0
In addition to basic functionality, we also compute additional statistics and plot precision-recall curves if an
additional na_prob.json file is provided. This file is expected to map question ID's to the model's predicted
probability that a question is unanswerable.
"""
import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.sub(regex, " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s:
return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for example in examples:
qas_id = example.qas_id
gold_answers = [answer["text"] for answer in example.answers if normalize_answer(answer["text"])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = [""]
if qas_id not in preds:
print("Missing prediction for %s" % qas_id)
continue
prediction = preds[qas_id]
exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores.values()) / total),
("f1", 100.0 * sum(f1_scores.values()) / total),
("total", total),
]
)
else:
total = len(qid_list)
return collections.OrderedDict(
[
("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total),
("f1", 100.0 * sum(f1_scores[k] for k in qid_list) / total),
("total", total),
]
)
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval["%s_%s" % (prefix, k)] = new_eval[k]
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]:
continue
has_ans_cnt += 1
if qid not in scores:
continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
main_eval["has_ans_exact"] = has_ans_exact
main_eval["has_ans_f1"] = has_ans_f1
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for _, qid in enumerate(qid_list):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval["best_exact"] = best_exact
main_eval["best_exact_thresh"] = exact_thresh
main_eval["best_f1"] = best_f1
main_eval["best_f1_thresh"] = f1_thresh
def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
if no_answer_probs is None:
no_answer_probs = {k: 0.0 for k in preds}
exact, f1 = get_raw_scores(examples, preds)
exact_threshold = apply_no_ans_threshold(
exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold
)
f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
evaluation = make_eval_dict(exact_threshold, f1_threshold)
if has_answer_qids:
has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids)
merge_eval(evaluation, has_ans_eval, "HasAns")
if no_answer_qids:
no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids)
merge_eval(evaluation, no_ans_eval, "NoAns")
if no_answer_probs:
find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer)
return evaluation
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info("Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info("Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position : (orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def compute_predictions_logits(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer,
):
"""Write final predictions to the json file and log-odds of null if needed."""
if output_prediction_file:
logger.info(f"Writing predictions to: {output_prediction_file}")
if output_nbest_file:
logger.info(f"Writing nbest to: {output_nbest_file}")
if output_null_log_odds_file and version_2_with_negative:
logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
)
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min null score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index],
)
)
if version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit,
)
)
prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"]
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# tok_text = " ".join(tok_tokens)
#
# # De-tokenize WordPieces that have been split off.
# tok_text = tok_text.replace(" ##", "")
# tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
# if we didn't include the empty option in the n-best, include it
if version_2_with_negative:
if "" not in seen_predictions:
nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
# In very rare edge cases we could only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if len(nbest) == 1:
nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1, "No valid predictions"
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1, "No valid predictions"
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
if output_prediction_file:
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
if output_nbest_file:
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if output_null_log_odds_file and version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def compute_predictions_log_probs(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
version_2_with_negative,
tokenizer,
verbose_logging,
):
"""
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of
null if needed.
Requires utils_squad_evaluate.py
"""
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"]
)
logger.info("Writing predictions to: %s", output_prediction_file)
# logger.info("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
cur_null_score = result.cls_logits
# if we could have irrelevant answers, get the min score of irrelevant
score_null = min(score_null, cur_null_score)
for i in range(start_n_top):
for j in range(end_n_top):
start_log_prob = result.start_logits[i]
start_index = result.start_top_index[i]
j_index = i * end_n_top + j
end_log_prob = result.end_logits[j_index]
end_index = result.end_top_index[j_index]
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= feature.paragraph_len - 1:
continue
if end_index >= feature.paragraph_len - 1:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_log_prob=start_log_prob,
end_log_prob=end_log_prob,
)
)
prelim_predictions = sorted(
prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
# XLNet un-tokenizer
# Let's keep it simple for now and see if we need all this later.
#
# tok_start_to_orig_index = feature.tok_start_to_orig_index
# tok_end_to_orig_index = feature.tok_end_to_orig_index
# start_orig_pos = tok_start_to_orig_index[pred.start_index]
# end_orig_pos = tok_end_to_orig_index[pred.end_index]
# paragraph_text = example.paragraph_text
# final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
# Previously used Bert untokenizer
tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
if hasattr(tokenizer, "do_lower_case"):
do_lower_case = tokenizer.do_lower_case
else:
do_lower_case = tokenizer.do_lowercase_and_remove_accent
final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)
)
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1, "No valid predictions"
assert best_non_null_entry is not None, "No valid predictions"
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
# note(zhiliny): always predict best_non_null_entry
# and the evaluation script will search for the best threshold
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
|
AdaMix/src/transformers/data/metrics/squad_metrics.py/0
|
{
"file_path": "AdaMix/src/transformers/data/metrics/squad_metrics.py",
"repo_id": "AdaMix",
"token_count": 13805
}
| 53 |
# Copyright 2020 The HuggingFace Team, the AllenNLP library authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities for working with the local dataset cache. Parts of this file is adapted from the AllenNLP library at
https://github.com/allenai/allennlp.
"""
import copy
import fnmatch
import importlib.util
import io
import json
import os
import re
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict, UserDict
from contextlib import contextmanager
from dataclasses import fields
from enum import Enum
from functools import partial, wraps
from hashlib import sha256
from pathlib import Path
from types import ModuleType
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import numpy as np
from packaging import version
from tqdm.auto import tqdm
import requests
from filelock import FileLock
from . import __version__
from .hf_api import HfFolder
from .utils import logging
# The package importlib_metadata is in a different place, depending on the python version.
if sys.version_info < (3, 8):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
_torch_available = importlib.util.find_spec("torch") is not None
if _torch_available:
try:
_torch_version = importlib_metadata.version("torch")
logger.info(f"PyTorch version {_torch_version} available.")
except importlib_metadata.PackageNotFoundError:
_torch_available = False
else:
logger.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
_tf_available = importlib.util.find_spec("tensorflow") is not None
if _tf_available:
# For the metadata, we have to look for both tensorflow and tensorflow-cpu
try:
_tf_version = importlib_metadata.version("tensorflow")
except importlib_metadata.PackageNotFoundError:
try:
_tf_version = importlib_metadata.version("tensorflow-cpu")
except importlib_metadata.PackageNotFoundError:
try:
_tf_version = importlib_metadata.version("tensorflow-gpu")
except importlib_metadata.PackageNotFoundError:
try:
_tf_version = importlib_metadata.version("tf-nightly")
except importlib_metadata.PackageNotFoundError:
try:
_tf_version = importlib_metadata.version("tf-nightly-cpu")
except importlib_metadata.PackageNotFoundError:
try:
_tf_version = importlib_metadata.version("tf-nightly-gpu")
except importlib_metadata.PackageNotFoundError:
# Support for intel-tensorflow version
try:
_tf_version = importlib_metadata.version("intel-tensorflow")
except importlib_metadata.PackageNotFoundError:
_tf_version = None
_tf_available = False
if _tf_available:
if version.parse(_tf_version) < version.parse("2"):
logger.info(f"TensorFlow found but with version {_tf_version}. Transformers requires version 2 minimum.")
_tf_available = False
else:
logger.info(f"TensorFlow version {_tf_version} available.")
else:
logger.info("Disabling Tensorflow because USE_TORCH is set")
_tf_available = False
if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
_flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None
if _flax_available:
try:
_jax_version = importlib_metadata.version("jax")
_flax_version = importlib_metadata.version("flax")
logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
except importlib_metadata.PackageNotFoundError:
_flax_available = False
else:
_flax_available = False
_datasets_available = importlib.util.find_spec("datasets") is not None
try:
# Check we're not importing a "datasets" directory somewhere but the actual library by trying to grab the version
# AND checking it has an author field in the metadata that is HuggingFace.
_ = importlib_metadata.version("datasets")
_datasets_metadata = importlib_metadata.metadata("datasets")
if _datasets_metadata.get("author", "") != "HuggingFace Inc.":
_datasets_available = False
except importlib_metadata.PackageNotFoundError:
_datasets_available = False
_faiss_available = importlib.util.find_spec("faiss") is not None
try:
_faiss_version = importlib_metadata.version("faiss")
logger.debug(f"Successfully imported faiss version {_faiss_version}")
except importlib_metadata.PackageNotFoundError:
try:
_faiss_version = importlib_metadata.version("faiss-cpu")
logger.debug(f"Successfully imported faiss version {_faiss_version}")
except importlib_metadata.PackageNotFoundError:
_faiss_available = False
_onnx_available = (
importlib.util.find_spec("keras2onnx") is not None and importlib.util.find_spec("onnxruntime") is not None
)
try:
_onxx_version = importlib_metadata.version("onnx")
logger.debug(f"Successfully imported onnx version {_onxx_version}")
except importlib_metadata.PackageNotFoundError:
_onnx_available = False
_scatter_available = importlib.util.find_spec("torch_scatter") is not None
try:
_scatter_version = importlib_metadata.version("torch_scatter")
logger.debug(f"Successfully imported torch-scatter version {_scatter_version}")
except importlib_metadata.PackageNotFoundError:
_scatter_available = False
_soundfile_available = importlib.util.find_spec("soundfile") is not None
try:
_soundfile_version = importlib_metadata.version("soundfile")
logger.debug(f"Successfully imported soundfile version {_soundfile_version}")
except importlib_metadata.PackageNotFoundError:
_soundfile_available = False
_torchaudio_available = importlib.util.find_spec("torchaudio")
try:
_torchaudio_version = importlib_metadata.version("torchaudio")
logger.debug(f"Successfully imported soundfile version {_torchaudio_version}")
except importlib_metadata.PackageNotFoundError:
_torchaudio_available = False
torch_cache_home = os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
old_default_cache_path = os.path.join(torch_cache_home, "transformers")
# New default cache, shared with the Datasets library
hf_cache_home = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
default_cache_path = os.path.join(hf_cache_home, "transformers")
# Onetime move from the old location to the new one if no ENV variable has been set.
if (
os.path.isdir(old_default_cache_path)
and not os.path.isdir(default_cache_path)
and "PYTORCH_PRETRAINED_BERT_CACHE" not in os.environ
and "PYTORCH_TRANSFORMERS_CACHE" not in os.environ
and "TRANSFORMERS_CACHE" not in os.environ
):
logger.warn(
"In Transformers v4.0.0, the default path to cache downloaded models changed from "
"'~/.cache/torch/transformers' to '~/.cache/huggingface/transformers'. Since you don't seem to have overridden "
"and '~/.cache/torch/transformers' is a directory that exists, we're moving it to "
"'~/.cache/huggingface/transformers' to avoid redownloading models you have already in the cache. You should "
"only see this message once."
)
shutil.move(old_default_cache_path, default_cache_path)
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", False)
WEIGHTS_NAME = "pytorch_model.bin"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF_WEIGHTS_NAME = "model.ckpt"
FLAX_WEIGHTS_NAME = "flax_model.msgpack"
CONFIG_NAME = "config.json"
FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
MODEL_CARD_NAME = "modelcard.json"
SENTENCEPIECE_UNDERLINE = "▁"
SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
MULTIPLE_CHOICE_DUMMY_INPUTS = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
HUGGINGFACE_CO_PREFIX = "https://huggingface.co/{model_id}/resolve/{revision}/{filename}"
PRESET_MIRROR_DICT = {
"tuna": "https://mirrors.tuna.tsinghua.edu.cn/hugging-face-models",
"bfsu": "https://mirrors.bfsu.edu.cn/hugging-face-models",
}
_is_offline_mode = True if os.environ.get("TRANSFORMERS_OFFLINE", "0").upper() in ENV_VARS_TRUE_VALUES else False
def is_offline_mode():
return _is_offline_mode
def is_torch_available():
return _torch_available
def is_torch_cuda_available():
if is_torch_available():
import torch
return torch.cuda.is_available()
else:
return False
def is_tf_available():
return _tf_available
def is_onnx_available():
return _onnx_available
def is_flax_available():
return _flax_available
def is_torch_tpu_available():
if not _torch_available:
return False
# This test is probably enough, but just in case, we unpack a bit.
if importlib.util.find_spec("torch_xla") is None:
return False
if importlib.util.find_spec("torch_xla.core") is None:
return False
return importlib.util.find_spec("torch_xla.core.xla_model") is not None
def is_datasets_available():
return _datasets_available
def is_psutil_available():
return importlib.util.find_spec("psutil") is not None
def is_py3nvml_available():
return importlib.util.find_spec("py3nvml") is not None
def is_apex_available():
return importlib.util.find_spec("apex") is not None
def is_faiss_available():
return _faiss_available
def is_sklearn_available():
if importlib.util.find_spec("sklearn") is None:
return False
if importlib.util.find_spec("scipy") is None:
return False
return importlib.util.find_spec("sklearn.metrics") and importlib.util.find_spec("scipy.stats")
def is_sentencepiece_available():
return importlib.util.find_spec("sentencepiece") is not None
def is_protobuf_available():
if importlib.util.find_spec("google") is None:
return False
return importlib.util.find_spec("google.protobuf") is not None
def is_tokenizers_available():
return importlib.util.find_spec("tokenizers") is not None
def is_in_notebook():
try:
# Test adapted from tqdm.autonotebook: https://github.com/tqdm/tqdm/blob/master/tqdm/autonotebook.py
get_ipython = sys.modules["IPython"].get_ipython
if "IPKernelApp" not in get_ipython().config:
raise ImportError("console")
if "VSCODE_PID" in os.environ:
raise ImportError("vscode")
return importlib.util.find_spec("IPython") is not None
except (AttributeError, ImportError, KeyError):
return False
def is_scatter_available():
return _scatter_available
def is_pandas_available():
return importlib.util.find_spec("pandas") is not None
def is_sagemaker_distributed_available():
# Get the sagemaker specific env variable.
sagemaker_params = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
sagemaker_params = json.loads(sagemaker_params)
if not sagemaker_params.get("sagemaker_distributed_dataparallel_enabled", False):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("smdistributed") is not None
def is_training_run_on_sagemaker():
return "SAGEMAKER_JOB_NAME" in os.environ and not DISABLE_TELEMETRY
def is_soundfile_availble():
return _soundfile_available
def is_torchaudio_available():
return _torchaudio_available
def torch_only_method(fn):
def wrapper(*args, **kwargs):
if not _torch_available:
raise ImportError(
"You need to install pytorch to use this method or class, "
"or activate it with environment variables USE_TORCH=1 and USE_TF=0."
)
else:
return fn(*args, **kwargs)
return wrapper
# docstyle-ignore
DATASETS_IMPORT_ERROR = """
{0} requires the 🤗 Datasets library but it was not found in your environment. You can install it with:
```
pip install datasets
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install datasets
```
then restarting your kernel.
Note that if you have a local folder named `datasets` or a local python file named `datasets.py` in your current
working directory, python may try to import this instead of the 🤗 Datasets library. You should rename this folder or
that python file if that's the case.
"""
# docstyle-ignore
TOKENIZERS_IMPORT_ERROR = """
{0} requires the 🤗 Tokenizers library but it was not found in your environment. You can install it with:
```
pip install tokenizers
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install tokenizers
```
"""
# docstyle-ignore
SENTENCEPIECE_IMPORT_ERROR = """
{0} requires the SentencePiece library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
that match your environment.
"""
# docstyle-ignore
PROTOBUF_IMPORT_ERROR = """
{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
that match your environment.
"""
# docstyle-ignore
FAISS_IMPORT_ERROR = """
{0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
that match your environment.
"""
# docstyle-ignore
PYTORCH_IMPORT_ERROR = """
{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
"""
# docstyle-ignore
SKLEARN_IMPORT_ERROR = """
{0} requires the scikit-learn library but it was not found in your environment. You can install it with:
```
pip install -U scikit-learn
```
In a notebook or a colab, you can install it by executing a cell with
```
!pip install -U scikit-learn
```
"""
# docstyle-ignore
TENSORFLOW_IMPORT_ERROR = """
{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
"""
# docstyle-ignore
FLAX_IMPORT_ERROR = """
{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
installation page: https://github.com/google/flax and follow the ones that match your environment.
"""
# docstyle-ignore
SCATTER_IMPORT_ERROR = """
{0} requires the torch-scatter library but it was not found in your environment. You can install it with pip as
explained here: https://github.com/rusty1s/pytorch_scatter.
"""
# docstyle-ignore
PANDAS_IMPORT_ERROR = """
{0} requires the pandas library but it was not found in your environment. You can install it with pip as
explained here: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html.
"""
def requires_datasets(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_datasets_available():
raise ImportError(DATASETS_IMPORT_ERROR.format(name))
def requires_faiss(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_faiss_available():
raise ImportError(FAISS_IMPORT_ERROR.format(name))
def requires_pytorch(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_torch_available():
raise ImportError(PYTORCH_IMPORT_ERROR.format(name))
def requires_sklearn(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_sklearn_available():
raise ImportError(SKLEARN_IMPORT_ERROR.format(name))
def requires_tf(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_tf_available():
raise ImportError(TENSORFLOW_IMPORT_ERROR.format(name))
def requires_flax(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_flax_available():
raise ImportError(FLAX_IMPORT_ERROR.format(name))
def requires_tokenizers(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_tokenizers_available():
raise ImportError(TOKENIZERS_IMPORT_ERROR.format(name))
def requires_sentencepiece(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_sentencepiece_available():
raise ImportError(SENTENCEPIECE_IMPORT_ERROR.format(name))
def requires_protobuf(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_protobuf_available():
raise ImportError(PROTOBUF_IMPORT_ERROR.format(name))
def requires_pandas(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_pandas_available():
raise ImportError(PANDAS_IMPORT_ERROR.format(name))
def requires_scatter(obj):
name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
if not is_scatter_available():
raise ImportError(SCATTER_IMPORT_ERROR.format(name))
def add_start_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
return fn
return docstring_decorator
def add_start_docstrings_to_model_forward(*docstr):
def docstring_decorator(fn):
class_name = ":class:`~transformers.{}`".format(fn.__qualname__.split(".")[0])
intro = " The {} forward method, overrides the :func:`__call__` special method.".format(class_name)
note = r"""
.. note::
Although the recipe for forward pass needs to be defined within this function, one should call the
:class:`Module` instance afterwards instead of this since the former takes care of running the pre and post
processing steps while the latter silently ignores them.
"""
fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
return fn
return docstring_decorator
def add_end_docstrings(*docstr):
def docstring_decorator(fn):
fn.__doc__ = fn.__doc__ + "".join(docstr)
return fn
return docstring_decorator
PT_RETURN_INTRODUCTION = r"""
Returns:
:class:`~{full_output_type}` or :obj:`tuple(torch.FloatTensor)`: A :class:`~{full_output_type}` (if
``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`torch.FloatTensor`
comprising various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.
"""
TF_RETURN_INTRODUCTION = r"""
Returns:
:class:`~{full_output_type}` or :obj:`tuple(tf.Tensor)`: A :class:`~{full_output_type}` (if
``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`tf.Tensor` comprising
various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.
"""
def _get_indent(t):
"""Returns the indentation in the first line of t"""
search = re.search(r"^(\s*)\S", t)
return "" if search is None else search.groups()[0]
def _convert_output_args_doc(output_args_doc):
"""Convert output_args_doc to display properly."""
# Split output_arg_doc in blocks argument/description
indent = _get_indent(output_args_doc)
blocks = []
current_block = ""
for line in output_args_doc.split("\n"):
# If the indent is the same as the beginning, the line is the name of new arg.
if _get_indent(line) == indent:
if len(current_block) > 0:
blocks.append(current_block[:-1])
current_block = f"{line}\n"
else:
# Otherwise it's part of the description of the current arg.
# We need to remove 2 spaces to the indentation.
current_block += f"{line[2:]}\n"
blocks.append(current_block[:-1])
# Format each block for proper rendering
for i in range(len(blocks)):
blocks[i] = re.sub(r"^(\s+)(\S+)(\s+)", r"\1- **\2**\3", blocks[i])
blocks[i] = re.sub(r":\s*\n\s*(\S)", r" -- \1", blocks[i])
return "\n".join(blocks)
def _prepare_output_docstrings(output_type, config_class):
"""
Prepares the return part of the docstring using `output_type`.
"""
docstrings = output_type.__doc__
# Remove the head of the docstring to keep the list of args only
lines = docstrings.split("\n")
i = 0
while i < len(lines) and re.search(r"^\s*(Args|Parameters):\s*$", lines[i]) is None:
i += 1
if i < len(lines):
docstrings = "\n".join(lines[(i + 1) :])
docstrings = _convert_output_args_doc(docstrings)
# Add the return introduction
full_output_type = f"{output_type.__module__}.{output_type.__name__}"
intro = TF_RETURN_INTRODUCTION if output_type.__name__.startswith("TF") else PT_RETURN_INTRODUCTION
intro = intro.format(full_output_type=full_output_type, config_class=config_class)
return intro + docstrings
PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0) # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
PT_QUESTION_ANSWERING_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors='pt')
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
"""
PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
PT_MASKED_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
PT_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
"""
PT_MULTIPLE_CHOICE_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import torch
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1
>>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
>>> outputs = model(**{{k: v.unsqueeze(0) for k,v in encoding.items()}}, labels=labels) # batch size is 1
>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
PT_CAUSAL_LM_SAMPLE = r"""
Example::
>>> import torch
>>> from transformers import {tokenizer_class}, {model_class}
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs, labels=inputs["input_ids"])
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> input_ids = inputs["input_ids"]
>>> inputs["labels"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))) # Batch size 1
>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
TF_QUESTION_ANSWERING_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> input_dict = tokenizer(question, text, return_tensors='tf')
>>> outputs = model(input_dict)
>>> start_logits = outputs.start_logits
>>> end_logits = outputs.end_logits
>>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
>>> answer = ' '.join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0]+1])
"""
TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1)) # Batch size 1
>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
TF_MASKED_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
"""
TF_BASE_MODEL_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden_state
"""
TF_MULTIPLE_CHOICE_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='tf', padding=True)
>>> inputs = {{k: tf.expand_dims(v, 0) for k, v in encoding.items()}}
>>> outputs = model(inputs) # batch size is 1
>>> # the linear classifier still needs to be trained
>>> logits = outputs.logits
"""
TF_CAUSAL_LM_SAMPLE = r"""
Example::
>>> from transformers import {tokenizer_class}, {model_class}
>>> import tensorflow as tf
>>> tokenizer = {tokenizer_class}.from_pretrained('{checkpoint}')
>>> model = {model_class}.from_pretrained('{checkpoint}')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logits
"""
def add_code_sample_docstrings(
*docstr, tokenizer_class=None, checkpoint=None, output_type=None, config_class=None, mask=None
):
def docstring_decorator(fn):
model_class = fn.__qualname__.split(".")[0]
is_tf_class = model_class[:2] == "TF"
doc_kwargs = dict(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint)
if "SequenceClassification" in model_class:
code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE
elif "QuestionAnswering" in model_class:
code_sample = TF_QUESTION_ANSWERING_SAMPLE if is_tf_class else PT_QUESTION_ANSWERING_SAMPLE
elif "TokenClassification" in model_class:
code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE
elif "MultipleChoice" in model_class:
code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE
elif "MaskedLM" in model_class or model_class in ["FlaubertWithLMHeadModel", "XLMWithLMHeadModel"]:
doc_kwargs["mask"] = "[MASK]" if mask is None else mask
code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE
elif "LMHead" in model_class or "CausalLM" in model_class:
code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE
elif "Model" in model_class or "Encoder" in model_class:
code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE
else:
raise ValueError(f"Docstring can't be built for model {model_class}")
output_doc = _prepare_output_docstrings(output_type, config_class) if output_type is not None else ""
built_doc = code_sample.format(**doc_kwargs)
fn.__doc__ = (fn.__doc__ or "") + "".join(docstr) + output_doc + built_doc
return fn
return docstring_decorator
def replace_return_docstrings(output_type=None, config_class=None):
def docstring_decorator(fn):
docstrings = fn.__doc__
lines = docstrings.split("\n")
i = 0
while i < len(lines) and re.search(r"^\s*Returns?:\s*$", lines[i]) is None:
i += 1
if i < len(lines):
lines[i] = _prepare_output_docstrings(output_type, config_class)
docstrings = "\n".join(lines)
else:
raise ValueError(
f"The function {fn} should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, current docstring is:\n{docstrings}"
)
fn.__doc__ = docstrings
return fn
return docstring_decorator
def is_remote_url(url_or_filename):
parsed = urlparse(url_or_filename)
return parsed.scheme in ("http", "https")
def hf_bucket_url(
model_id: str, filename: str, subfolder: Optional[str] = None, revision: Optional[str] = None, mirror=None
) -> str:
"""
Resolve a model identifier, a file name, and an optional revision id, to a huggingface.co-hosted url, redirecting
to Cloudfront (a Content Delivery Network, or CDN) for large files.
Cloudfront is replicated over the globe so downloads are way faster for the end user (and it also lowers our
bandwidth costs).
Cloudfront aggressively caches files by default (default TTL is 24 hours), however this is not an issue here
because we migrated to a git-based versioning system on huggingface.co, so we now store the files on S3/Cloudfront
in a content-addressable way (i.e., the file name is its hash). Using content-addressable filenames means cache
can't ever be stale.
In terms of client-side caching from this library, we base our caching on the objects' ETag. An object' ETag is:
its sha1 if stored in git, or its sha256 if stored in git-lfs. Files cached locally from transformers before v3.5.0
are not shared with those new files, because the cached file's name contains a hash of the url (which changed).
"""
if subfolder is not None:
filename = f"{subfolder}/{filename}"
if mirror:
endpoint = PRESET_MIRROR_DICT.get(mirror, mirror)
legacy_format = "/" not in model_id
if legacy_format:
return f"{endpoint}/{model_id}-{filename}"
else:
return f"{endpoint}/{model_id}/{filename}"
if revision is None:
revision = "main"
return HUGGINGFACE_CO_PREFIX.format(model_id=model_id, revision=revision, filename=filename)
def url_to_filename(url: str, etag: Optional[str] = None) -> str:
"""
Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's,
delimited by a period. If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can
identify it as a HDF5 file (see
https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
"""
url_bytes = url.encode("utf-8")
filename = sha256(url_bytes).hexdigest()
if etag:
etag_bytes = etag.encode("utf-8")
filename += "." + sha256(etag_bytes).hexdigest()
if url.endswith(".h5"):
filename += ".h5"
return filename
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or
its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + ".json"
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata["url"]
etag = metadata["etag"]
return url, etag
def get_cached_models(cache_dir: Union[str, Path] = None) -> List[Tuple]:
"""
Returns a list of tuples representing model binaries that are cached locally. Each tuple has shape
:obj:`(model_url, etag, size_MB)`. Filenames in :obj:`cache_dir` are use to get the metadata for each model, only
urls ending with `.bin` are added.
Args:
cache_dir (:obj:`Union[str, Path]`, `optional`):
The cache directory to search for models within. Will default to the transformers cache if unset.
Returns:
List[Tuple]: List of tuples each with shape :obj:`(model_url, etag, size_MB)`
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
elif isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cached_models = []
for file in os.listdir(cache_dir):
if file.endswith(".json"):
meta_path = os.path.join(cache_dir, file)
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata["url"]
etag = metadata["etag"]
if url.endswith(".bin"):
size_MB = os.path.getsize(meta_path.strip(".json")) / 1e6
cached_models.append((url, etag, size_MB))
return cached_models
def cached_path(
url_or_filename,
cache_dir=None,
force_download=False,
proxies=None,
resume_download=False,
user_agent: Union[Dict, str, None] = None,
extract_compressed_file=False,
force_extract=False,
use_auth_token: Union[bool, str, None] = None,
local_files_only=False,
) -> Optional[str]:
"""
Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file
and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and
then return the path
Args:
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
force_download: if True, re-download the file even if it's already cached in the cache dir.
resume_download: if True, resume the download if incompletely received file is found.
user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
use_auth_token: Optional string or boolean to use as Bearer token for remote files. If True,
will get token from ~/.huggingface.
extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
file in a folder along the archive.
force_extract: if True when extract_compressed_file is True and the archive was already extracted,
re-extract the archive and override the folder where it was extracted.
Return:
Local path (string) of file or if networking is off, last version of file cached on disk.
Raises:
In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
if is_remote_url(url_or_filename):
# URL, so get it from the cache (downloading if necessary)
output_path = get_from_cache(
url_or_filename,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
user_agent=user_agent,
use_auth_token=use_auth_token,
local_files_only=local_files_only,
)
elif os.path.exists(url_or_filename):
# File, and it exists.
output_path = url_or_filename
elif urlparse(url_or_filename).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
if extract_compressed_file:
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
output_dir, output_file = os.path.split(output_path)
output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lock_path = output_path + ".lock"
with FileLock(lock_path):
shutil.rmtree(output_path_extracted, ignore_errors=True)
os.makedirs(output_path_extracted)
if is_zipfile(output_path):
with ZipFile(output_path, "r") as zip_file:
zip_file.extractall(output_path_extracted)
zip_file.close()
elif tarfile.is_tarfile(output_path):
tar_file = tarfile.open(output_path)
tar_file.extractall(output_path_extracted)
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(output_path))
return output_path_extracted
return output_path
def define_sagemaker_information():
try:
instance_data = requests.get(os.environ["ECS_CONTAINER_METADATA_URI"]).json()
dlc_container_used = instance_data["Image"]
dlc_tag = instance_data["Image"].split(":")[1]
except Exception:
dlc_container_used = None
dlc_tag = None
sagemaker_params = json.loads(os.getenv("SM_FRAMEWORK_PARAMS", "{}"))
runs_distributed_training = True if "sagemaker_distributed_dataparallel_enabled" in sagemaker_params else False
account_id = os.getenv("TRAINING_JOB_ARN").split(":")[4] if "TRAINING_JOB_ARN" in os.environ else None
sagemaker_object = {
"sm_framework": os.getenv("SM_FRAMEWORK_MODULE", None),
"sm_region": os.getenv("AWS_REGION", None),
"sm_number_gpu": os.getenv("SM_NUM_GPUS", 0),
"sm_number_cpu": os.getenv("SM_NUM_CPUS", 0),
"sm_distributed_training": runs_distributed_training,
"sm_deep_learning_container": dlc_container_used,
"sm_deep_learning_container_tag": dlc_tag,
"sm_account_id": account_id,
}
return sagemaker_object
def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
"""
Formats a user-agent string with basic info about a request.
"""
ua = "transformers/{}; python/{}".format(__version__, sys.version.split()[0])
if is_torch_available():
ua += f"; torch/{_torch_version}"
if is_tf_available():
ua += f"; tensorflow/{_tf_version}"
if is_training_run_on_sagemaker():
ua += "; " + "; ".join(f"{k}/{v}" for k, v in define_sagemaker_information().items())
if isinstance(user_agent, dict):
ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
return ua
def http_get(url: str, temp_file: BinaryIO, proxies=None, resume_size=0, headers: Optional[Dict[str, str]] = None):
"""
Donwload remote file. Do not gobble up errors.
"""
headers = copy.deepcopy(headers)
if resume_size > 0:
headers["Range"] = "bytes=%d-" % (resume_size,)
r = requests.get(url, stream=True, proxies=proxies, headers=headers)
r.raise_for_status()
content_length = r.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
progress = tqdm(
unit="B",
unit_scale=True,
total=total,
initial=resume_size,
desc="Downloading",
disable=bool(logging.get_verbosity() == logging.NOTSET),
)
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(
url: str,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10,
resume_download=False,
user_agent: Union[Dict, str, None] = None,
use_auth_token: Union[bool, str, None] = None,
local_files_only=False,
) -> Optional[str]:
"""
Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the
path to the cached file.
Return:
Local path (string) of file or if networking is off, last version of file cached on disk.
Raises:
In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
"""
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
headers = {"user-agent": http_user_agent(user_agent)}
if isinstance(use_auth_token, str):
headers["authorization"] = "Bearer {}".format(use_auth_token)
elif use_auth_token:
token = HfFolder.get_token()
if token is None:
raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
headers["authorization"] = "Bearer {}".format(token)
url_to_download = url
etag = None
if not local_files_only:
try:
r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=etag_timeout)
r.raise_for_status()
etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")
# We favor a custom header indicating the etag of the linked resource, and
# we fallback to the regular etag header.
# If we don't have any of those, raise an error.
if etag is None:
raise OSError(
"Distant resource does not have an ETag, we won't be able to reliably ensure reproducibility."
)
# In case of a redirect,
# save an extra redirect on the request.get call,
# and ensure we download the exact atomic version even if it changed
# between the HEAD and the GET (unlikely, but hey).
if 300 <= r.status_code <= 399:
url_to_download = r.headers["Location"]
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
# etag is already None
pass
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# etag is None == we don't have a connection or we passed local_files_only.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file
for file in fnmatch.filter(os.listdir(cache_dir), filename.split(".")[0] + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise FileNotFoundError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False."
)
else:
raise ValueError(
"Connection error, and we cannot find the requested files in the cached path."
" Please try again or make sure your Internet connection is on."
)
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lock_path = cache_path + ".lock"
with FileLock(lock_path):
# If the download just completed while the lock was activated.
if os.path.exists(cache_path) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
incomplete_path = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager() -> "io.BufferedWriter":
with open(incomplete_path, "ab") as f:
yield f
temp_file_manager = _resumable_file_manager
if os.path.exists(incomplete_path):
resume_size = os.stat(incomplete_path).st_size
else:
resume_size = 0
else:
temp_file_manager = partial(tempfile.NamedTemporaryFile, mode="wb", dir=cache_dir, delete=False)
resume_size = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, headers=headers)
logger.info("storing %s in cache at %s", url, cache_path)
os.replace(temp_file.name, cache_path)
logger.info("creating metadata file for %s", cache_path)
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
json.dump(meta, meta_file)
return cache_path
class cached_property(property):
"""
Descriptor that mimics @property but caches output in member variable.
From tensorflow_datasets
Built-in in functools from Python 3.8.
"""
def __get__(self, obj, objtype=None):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
attr = "__cached_" + self.fget.__name__
cached = getattr(obj, attr, None)
if cached is None:
cached = self.fget(obj)
setattr(obj, attr, cached)
return cached
def torch_required(func):
# Chose a different decorator name than in tests so it's clear they are not the same.
@wraps(func)
def wrapper(*args, **kwargs):
if is_torch_available():
return func(*args, **kwargs)
else:
raise ImportError(f"Method `{func.__name__}` requires PyTorch.")
return wrapper
def tf_required(func):
# Chose a different decorator name than in tests so it's clear they are not the same.
@wraps(func)
def wrapper(*args, **kwargs):
if is_tf_available():
return func(*args, **kwargs)
else:
raise ImportError(f"Method `{func.__name__}` requires TF.")
return wrapper
def is_tensor(x):
""" Tests if ``x`` is a :obj:`torch.Tensor`, :obj:`tf.Tensor` or :obj:`np.ndarray`. """
if is_torch_available():
import torch
if isinstance(x, torch.Tensor):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(x, tf.Tensor):
return True
return isinstance(x, np.ndarray)
def _is_numpy(x):
return isinstance(x, np.ndarray)
def _is_torch(x):
import torch
return isinstance(x, torch.Tensor)
def _is_torch_device(x):
import torch
return isinstance(x, torch.device)
def _is_tensorflow(x):
import tensorflow as tf
return isinstance(x, tf.Tensor)
def _is_jax(x):
import jax.numpy as jnp # noqa: F811
return isinstance(x, jnp.ndarray)
def to_py_obj(obj):
"""
Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
"""
if isinstance(obj, (dict, UserDict)):
return {k: to_py_obj(v) for k, v in obj.items()}
elif isinstance(obj, (list, tuple)):
return [to_py_obj(o) for o in obj]
elif is_tf_available() and _is_tensorflow(obj):
return obj.numpy().tolist()
elif is_torch_available() and _is_torch(obj):
return obj.detach().cpu().tolist()
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return obj
class ModelOutput(OrderedDict):
"""
Base class for all model outputs as dataclass. Has a ``__getitem__`` that allows indexing by integer or slice (like
a tuple) or strings (like a dictionary) that will ignore the ``None`` attributes. Otherwise behaves like a regular
python dictionary.
.. warning::
You can't unpack a :obj:`ModelOutput` directly. Use the :meth:`~transformers.file_utils.ModelOutput.to_tuple`
method to convert it to a tuple before.
"""
def __post_init__(self):
class_fields = fields(self)
# Safety and consistency checks
assert len(class_fields), f"{self.__class__.__name__} has no fields."
assert all(
field.default is None for field in class_fields[1:]
), f"{self.__class__.__name__} should not have more than one required field."
first_field = getattr(self, class_fields[0].name)
other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(first_field):
try:
iterator = iter(first_field)
first_field_iterator = True
except TypeError:
first_field_iterator = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for element in iterator:
if (
not isinstance(element, (list, tuple))
or not len(element) == 2
or not isinstance(element[0], str)
):
break
setattr(self, element[0], element[1])
if element[1] is not None:
self[element[0]] = element[1]
elif first_field is not None:
self[class_fields[0].name] = first_field
else:
for field in class_fields:
v = getattr(self, field.name)
if v is not None:
self[field.name] = v
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__(self, k):
if isinstance(k, str):
inner_dict = {k: v for (k, v) in self.items()}
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self, name, value):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(name, value)
super().__setattr__(name, value)
def __setitem__(self, key, value):
# Will raise a KeyException if needed
super().__setitem__(key, value)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(key, value)
def to_tuple(self) -> Tuple[Any]:
"""
Convert self to a tuple containing all the attributes/keys that are not ``None``.
"""
return tuple(self[k] for k in self.keys())
class ExplicitEnum(Enum):
"""
Enum with more explicit error message for missing values.
"""
@classmethod
def _missing_(cls, value):
raise ValueError(
"%r is not a valid %s, please select one of %s"
% (value, cls.__name__, str(list(cls._value2member_map_.keys())))
)
class PaddingStrategy(ExplicitEnum):
"""
Possible values for the ``padding`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for tab-completion
in an IDE.
"""
LONGEST = "longest"
MAX_LENGTH = "max_length"
DO_NOT_PAD = "do_not_pad"
class TensorType(ExplicitEnum):
"""
Possible values for the ``return_tensors`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
tab-completion in an IDE.
"""
PYTORCH = "pt"
TENSORFLOW = "tf"
NUMPY = "np"
JAX = "jax"
class _BaseLazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
# Very heavily inspired by optuna.integration._IntegrationModule
# https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
def __init__(self, name, import_structure):
super().__init__(name)
self._modules = set(import_structure.keys())
self._class_to_module = {}
for key, values in import_structure.items():
for value in values:
self._class_to_module[value] = key
# Needed for autocompletion in an IDE
self.__all__ = list(import_structure.keys()) + sum(import_structure.values(), [])
# Needed for autocompletion in an IDE
def __dir__(self):
return super().__dir__() + self.__all__
def __getattr__(self, name: str) -> Any:
if name in self._modules:
value = self._get_module(name)
elif name in self._class_to_module.keys():
module = self._get_module(self._class_to_module[name])
value = getattr(module, name)
else:
raise AttributeError(f"module {self.__name__} has no attribute {name}")
setattr(self, name, value)
return value
def _get_module(self, module_name: str) -> ModuleType:
raise NotImplementedError
|
AdaMix/src/transformers/file_utils.py/0
|
{
"file_path": "AdaMix/src/transformers/file_utils.py",
"repo_id": "AdaMix",
"token_count": 24853
}
| 54 |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for ALBERT model."""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...file_utils import is_sentencepiece_available
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
AlbertTokenizer = None
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
},
"tokenizer_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
SPIECE_UNDERLINE = "▁"
class AlbertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" ALBERT tokenizer (backed by HuggingFace's `tokenizers` library). Based on `Unigram
<https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models>`__. This tokenizer
inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods
Args:
vocab_file (:obj:`str`):
`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to lowercase the input when tokenizing.
remove_space (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to keep accents when tokenizing.
bos_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the :obj:`sep_token`.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
slow_tokenizer_class = AlbertTokenizer
def __init__(
self,
vocab_file,
tokenizer_file=None,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An ALBERT sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of ids.
token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)
|
AdaMix/src/transformers/models/albert/tokenization_albert_fast.py/0
|
{
"file_path": "AdaMix/src/transformers/models/albert/tokenization_albert_fast.py",
"repo_id": "AdaMix",
"token_count": 5145
}
| 55 |
# coding=utf-8
# Copyright 2020, The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Bort checkpoint."""
import argparse
import os
import numpy as np
import torch
from packaging import version
import gluonnlp as nlp
import mxnet as mx
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "The Nymphenburg Palace is a beautiful palace in Munich!"
def convert_bort_checkpoint_to_pytorch(bort_checkpoint_path: str, pytorch_dump_folder_path: str):
"""
Convert the original Bort checkpoint (based on MXNET and Gluonnlp) to our BERT structure-
"""
# Original Bort configuration
bort_4_8_768_1024_hparams = {
"attention_cell": "multi_head",
"num_layers": 4,
"units": 1024,
"hidden_size": 768,
"max_length": 512,
"num_heads": 8,
"scaled": True,
"dropout": 0.1,
"use_residual": True,
"embed_size": 1024,
"embed_dropout": 0.1,
"word_embed": None,
"layer_norm_eps": 1e-5,
"token_type_vocab_size": 2,
}
predefined_args = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
encoder = BERTEncoder(
attention_cell=predefined_args["attention_cell"],
num_layers=predefined_args["num_layers"],
units=predefined_args["units"],
hidden_size=predefined_args["hidden_size"],
max_length=predefined_args["max_length"],
num_heads=predefined_args["num_heads"],
scaled=predefined_args["scaled"],
dropout=predefined_args["dropout"],
output_attention=False,
output_all_encodings=False,
use_residual=predefined_args["use_residual"],
activation=predefined_args.get("activation", "gelu"),
layer_norm_eps=predefined_args.get("layer_norm_eps", None),
)
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
vocab_name = "openwebtext_ccnews_stories_books_cased"
# Specify download folder to Gluonnlp's vocab
gluon_cache_dir = os.path.join(get_home_dir(), "models")
bort_vocab = _load_vocab(vocab_name, None, gluon_cache_dir, cls=Vocab)
original_bort = nlp.model.BERTModel(
encoder,
len(bort_vocab),
units=predefined_args["units"],
embed_size=predefined_args["embed_size"],
embed_dropout=predefined_args["embed_dropout"],
word_embed=predefined_args["word_embed"],
use_pooler=False,
use_token_type_embed=False,
token_type_vocab_size=predefined_args["token_type_vocab_size"],
use_classifier=False,
use_decoder=False,
)
original_bort.load_parameters(bort_checkpoint_path, cast_dtype=True, ignore_extra=True)
params = original_bort._collect_params_with_prefix()
# Build our config 🤗
hf_bort_config_json = {
"architectures": ["BertForMaskedLM"],
"attention_probs_dropout_prob": predefined_args["dropout"],
"hidden_act": "gelu",
"hidden_dropout_prob": predefined_args["dropout"],
"hidden_size": predefined_args["embed_size"],
"initializer_range": 0.02,
"intermediate_size": predefined_args["hidden_size"],
"layer_norm_eps": predefined_args["layer_norm_eps"],
"max_position_embeddings": predefined_args["max_length"],
"model_type": "bort",
"num_attention_heads": predefined_args["num_heads"],
"num_hidden_layers": predefined_args["num_layers"],
"pad_token_id": 1, # 2 = BERT, 1 = RoBERTa
"type_vocab_size": 1, # 2 = BERT, 1 = RoBERTa
"vocab_size": len(bort_vocab),
}
hf_bort_config = BertConfig.from_dict(hf_bort_config_json)
hf_bort_model = BertForMaskedLM(hf_bort_config)
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(mx_array) -> torch.nn.Parameter:
return torch.nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()))
# Check param shapes and map new HF param back
def check_and_map_params(hf_param, gluon_param):
shape_hf = hf_param.shape
gluon_param = to_torch(params[gluon_param])
shape_gluon = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"
return gluon_param
hf_bort_model.bert.embeddings.word_embeddings.weight = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight, "word_embed.0.weight"
)
hf_bort_model.bert.embeddings.position_embeddings.weight = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight, "encoder.position_weight"
)
hf_bort_model.bert.embeddings.LayerNorm.bias = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias, "encoder.layer_norm.beta"
)
hf_bort_model.bert.embeddings.LayerNorm.weight = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight, "encoder.layer_norm.gamma"
)
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data
)
for i in range(hf_bort_config.num_hidden_layers):
layer: BertLayer = hf_bort_model.bert.encoder.layer[i]
# self attention
self_attn: BertSelfAttention = layer.attention.self
self_attn.key.bias.data = check_and_map_params(
self_attn.key.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"
)
self_attn.key.weight.data = check_and_map_params(
self_attn.key.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"
)
self_attn.query.bias.data = check_and_map_params(
self_attn.query.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"
)
self_attn.query.weight.data = check_and_map_params(
self_attn.query.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"
)
self_attn.value.bias.data = check_and_map_params(
self_attn.value.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"
)
self_attn.value.weight.data = check_and_map_params(
self_attn.value.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"
)
# self attention output
self_output: BertSelfOutput = layer.attention.output
self_output.dense.bias = check_and_map_params(
self_output.dense.bias, f"encoder.transformer_cells.{i}.proj.bias"
)
self_output.dense.weight = check_and_map_params(
self_output.dense.weight, f"encoder.transformer_cells.{i}.proj.weight"
)
self_output.LayerNorm.bias = check_and_map_params(
self_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.layer_norm.beta"
)
self_output.LayerNorm.weight = check_and_map_params(
self_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.layer_norm.gamma"
)
# intermediate
intermediate: BertIntermediate = layer.intermediate
intermediate.dense.bias = check_and_map_params(
intermediate.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"
)
intermediate.dense.weight = check_and_map_params(
intermediate.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"
)
# output
bert_output: BertOutput = layer.output
bert_output.dense.bias = check_and_map_params(
bert_output.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_2.bias"
)
bert_output.dense.weight = check_and_map_params(
bert_output.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_2.weight"
)
bert_output.LayerNorm.bias = check_and_map_params(
bert_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.ffn.layer_norm.beta"
)
bert_output.LayerNorm.weight = check_and_map_params(
bert_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.ffn.layer_norm.gamma"
)
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
input_ids = tokenizer.encode_plus(SAMPLE_TEXT)["input_ids"]
# Get gluon output
gluon_input_ids = mx.nd.array([input_ids])
output_gluon = original_bort(inputs=gluon_input_ids, token_types=[])
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(pytorch_dump_folder_path)
hf_bort_model = BertModel.from_pretrained(pytorch_dump_folder_path)
hf_bort_model.eval()
input_ids = tokenizer.encode_plus(SAMPLE_TEXT, return_tensors="pt")
output_hf = hf_bort_model(**input_ids)[0]
gluon_layer = output_gluon[0].asnumpy()
hf_layer = output_hf[0].detach().numpy()
max_absolute_diff = np.max(np.abs(hf_layer - gluon_layer)).item()
success = np.allclose(gluon_layer, hf_layer, atol=1e-3)
if success:
print("✔️ Both model do output the same tensors")
else:
print("❌ Both model do **NOT** output the same tensors")
print("Absolute difference is:", max_absolute_diff)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
|
AdaMix/src/transformers/models/bort/convert_bort_original_gluonnlp_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/bort/convert_bort_original_gluonnlp_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 6174
}
| 56 |
# coding=utf-8
# Copyright 2018 Salesforce and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for Salesforce CTRL."""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"ctrl": 256,
}
CONTROL_CODES = {
"Pregnancy": 168629,
"Christianity": 7675,
"Explain": 106423,
"Fitness": 63440,
"Saving": 63163,
"Ask": 27171,
"Ass": 95985,
"Joke": 163509,
"Questions": 45622,
"Thoughts": 49605,
"Retail": 52342,
"Feminism": 164338,
"Writing": 11992,
"Atheism": 192263,
"Netflix": 48616,
"Computing": 39639,
"Opinion": 43213,
"Alone": 44967,
"Funny": 58917,
"Gaming": 40358,
"Human": 4088,
"India": 1331,
"Joker": 77138,
"Diet": 36206,
"Legal": 11859,
"Norman": 4939,
"Tip": 72689,
"Weight": 52343,
"Movies": 46273,
"Running": 23425,
"Science": 2090,
"Horror": 37793,
"Confession": 60572,
"Finance": 12250,
"Politics": 16360,
"Scary": 191985,
"Support": 12654,
"Technologies": 32516,
"Teenage": 66160,
"Event": 32769,
"Learned": 67460,
"Notion": 182770,
"Wikipedia": 37583,
"Books": 6665,
"Extract": 76050,
"Confessions": 102701,
"Conspiracy": 75932,
"Links": 63674,
"Narcissus": 150425,
"Relationship": 54766,
"Relationships": 134796,
"Reviews": 41671,
"News": 4256,
"Translation": 26820,
"multilingual": 128406,
}
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
pairs = set(pairs)
return pairs
class CTRLTokenizer(PreTrainedTokenizer):
"""
Construct a CTRL tokenizer. Based on Byte-Pair-Encoding.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
control_codes = CONTROL_CODES
def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
super().__init__(unk_token=unk_token, **kwargs)
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[1:-1]
merges = [tuple(merge.split()) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = "@@ ".join(word)
word = word[:-4]
self.cache[token] = word
return word
def _tokenize(self, text):
"""Tokenize a string."""
split_tokens = []
words = re.findall(r"\S+\n?", text)
for token in words:
split_tokens.extend([t for t in self.bpe(token).split(" ")])
return split_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
out_string = " ".join(tokens).replace("@@ ", "").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
"Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file)
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
|
AdaMix/src/transformers/models/ctrl/tokenization_ctrl.py/0
|
{
"file_path": "AdaMix/src/transformers/models/ctrl/tokenization_ctrl.py",
"repo_id": "AdaMix",
"token_count": 3919
}
| 57 |
# coding=utf-8
# Copyright 2020 Microsoft and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization class for model DeBERTa."""
import os
import unicodedata
from typing import Optional, Tuple
import sentencepiece as sp
import six
from ...tokenization_utils import PreTrainedTokenizer
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
"microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
"microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model",
"microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"microsoft/deberta-v2-xlarge": 512,
"microsoft/deberta-v2-xxlarge": 512,
"microsoft/deberta-v2-xlarge-mnli": 512,
"microsoft/deberta-v2-xxlarge-mnli": 512,
}
PRETRAINED_INIT_CONFIGURATION = {
"microsoft/deberta-v2-xlarge": {"do_lower_case": False},
"microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
"microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
"microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
VOCAB_FILES_NAMES = {"vocab_file": "spm.model"}
class DebertaV2Tokenizer(PreTrainedTokenizer):
r"""
Constructs a DeBERTa-v2 tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
Args:
vocab_file (:obj:`str`):
`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to lowercase the input when tokenizing.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=False,
split_by_punct=False,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
super().__init__(
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
split_by_punct=split_by_punct,
**kwargs,
)
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = DebertaV2Tokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.do_lower_case = do_lower_case
self.split_by_punct = split_by_punct
self._tokenizer = SPMTokenizer(vocab_file, split_by_punct=split_by_punct)
@property
def vocab_size(self):
return len(self.vocab)
@property
def vocab(self):
return self._tokenizer.vocab
def get_vocab(self):
vocab = self.vocab.copy()
vocab.update(self.get_added_vocab())
return vocab
def _tokenize(self, text):
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
if self.do_lower_case:
text = text.lower()
return self._tokenizer.tokenize(text)
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self._tokenizer.spm.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self._tokenizer.spm.IdToPiece(index) if index < self.vocab_size else self.unk_token
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
return self._tokenizer.decode(tokens)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A DeBERTa sequence has the following format:
- single sequence: [CLS] X [SEP]
- pair of sequences: [CLS] A [SEP] B [SEP]
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(
map(
lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0,
token_ids_0,
)
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", False)
if is_split_into_words or add_prefix_space:
text = " " + text
return (text, kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
return self._tokenizer.save_pretrained(save_directory, filename_prefix=filename_prefix)
class SPMTokenizer:
def __init__(self, vocab_file, split_by_punct=False):
self.split_by_punct = split_by_punct
self.vocab_file = vocab_file
spm = sp.SentencePieceProcessor()
assert os.path.exists(vocab_file)
spm.load(vocab_file)
bpe_vocab_size = spm.GetPieceSize()
# Token map
# <unk> 0+1
# <s> 1+1
# </s> 2+1
self.vocab = {spm.IdToPiece(i): i for i in range(bpe_vocab_size)}
self.id_to_tokens = [spm.IdToPiece(i) for i in range(bpe_vocab_size)]
# self.vocab['[PAD]'] = 0
# self.vocab['[CLS]'] = 1
# self.vocab['[SEP]'] = 2
# self.vocab['[UNK]'] = 3
self.spm = spm
def __getstate__(self):
state = self.__dict__.copy()
state["spm"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
self.spm = sp.SentencePieceProcessor()
self.spm.Load(self.vocab_file)
def tokenize(self, text):
pieces = self._encode_as_pieces(text)
def _norm(x):
if x not in self.vocab or x == "<unk>":
return "[UNK]"
else:
return x
pieces = [_norm(p) for p in pieces]
return pieces
def convert_ids_to_tokens(self, ids):
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
def decode(self, tokens, start=-1, end=-1, raw_text=None):
if raw_text is None:
return self.spm.decode_pieces([t for t in tokens])
else:
words = self.split_to_words(raw_text)
word_tokens = [self.tokenize(w) for w in words]
token2words = [0] * len(tokens)
tid = 0
for i, w in enumerate(word_tokens):
for k, t in enumerate(w):
token2words[tid] = i
tid += 1
word_start = token2words[start]
word_end = token2words[end] if end < len(tokens) else len(words)
text = "".join(words[word_start:word_end])
return text
def add_special_token(self, token):
if token not in self.special_tokens:
self.special_tokens.append(token)
if token not in self.vocab:
self.vocab[token] = len(self.vocab) - 1
self.id_to_tokens.append(token)
return self.id(token)
def part_of_whole_word(self, token, is_bos=False):
if is_bos:
return True
if (
len(token) == 1
and (_is_whitespace(list(token)[0]) or _is_control(list(token)[0]) or _is_punctuation(list(token)[0]))
) or token in self.special_tokens:
return False
word_start = b"\xe2\x96\x81".decode("utf-8")
return not token.startswith(word_start)
def pad(self):
return "[PAD]"
def bos(self):
return "[CLS]"
def eos(self):
return "[SEP]"
def unk(self):
return "[UNK]"
def mask(self):
return "[MASK]"
def sym(self, id):
return self.ids_to_tokens[id]
def id(self, sym):
return self.vocab[sym] if sym in self.vocab else 1
def _encode_as_pieces(self, text):
text = convert_to_unicode(text)
if self.split_by_punct:
words = self._run_split_on_punc(text)
pieces = [self.spm.encode_as_pieces(w) for w in words]
return [p for w in pieces for p in w]
else:
return self.spm.encode_as_pieces(text)
def split_to_words(self, text):
pieces = self._encode_as_pieces(text)
word_start = b"\xe2\x96\x81".decode("utf-8")
words = []
offset = 0
prev_end = 0
for i, p in enumerate(pieces):
if p.startswith(word_start):
if offset > prev_end:
words.append(text[prev_end:offset])
prev_end = offset
w = p.replace(word_start, "")
else:
w = p
try:
s = text.index(w, offset)
pn = ""
k = i + 1
while k < len(pieces):
pn = pieces[k].replace(word_start, "")
if len(pn) > 0:
break
k += 1
if len(pn) > 0 and pn in text[offset:s]:
offset = offset + 1
else:
offset = s + len(w)
except Exception:
offset = offset + 1
if prev_end < offset:
words.append(text[prev_end:offset])
return words
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def save_pretrained(self, path: str, filename_prefix: str = None):
filename = VOCAB_FILES_NAMES[list(VOCAB_FILES_NAMES.keys())[0]]
if filename_prefix is not None:
filename = filename_prefix + "-" + filename
full_path = os.path.join(path, filename)
with open(full_path, "wb") as fs:
fs.write(self.spm.serialized_model_proto())
return (full_path,)
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
|
AdaMix/src/transformers/models/deberta_v2/tokenization_deberta_v2.py/0
|
{
"file_path": "AdaMix/src/transformers/models/deberta_v2/tokenization_deberta_v2.py",
"repo_id": "AdaMix",
"token_count": 8468
}
| 58 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team, and the
# Lxmert Authors.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 LXMERT model. """
import warnings
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_utils import TFPreTrainedModel, get_initializer, input_processing, keras_serializable, shape_list
from ...utils import logging
from .configuration_lxmert import LxmertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased"
_CONFIG_FOR_DOC = "LxmertConfig"
_TOKENIZER_FOR_DOC = "LxmertTokenizer"
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"unc-nlp/lxmert-base-uncased",
]
@dataclass
class TFLxmertModelOutput(ModelOutput):
"""
Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
encoder")
Args:
language_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the language encoder.
vision_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the visual encoder.
pooled_output (:obj:`tf.Tensor` of shape :obj:`(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear
language_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for input features + one for the output of each cross-modality layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for input features + one for the output of each cross-modality layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
language_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
language_output: Optional[tf.Tensor] = None
vision_output: Optional[tf.Tensor] = None
pooled_output: Optional[tf.Tensor] = None
language_hidden_states: Optional[Tuple[tf.Tensor]] = None
vision_hidden_states: Optional[Tuple[tf.Tensor]] = None
language_attentions: Optional[Tuple[tf.Tensor]] = None
vision_attentions: Optional[Tuple[tf.Tensor]] = None
cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None
@dataclass
class TFLxmertForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.LxmertForPreTraining`.
Args:
loss (`optional`, returned when ``labels`` is provided, ``tf.Tensor`` of shape :obj:`(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cross_relationship_score: (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
Prediction scores of the textual matching objective (classification) head (scores of True/False
continuation before SoftMax).
question_answering_score: (:obj:`tf.Tensor` of shape :obj:`(batch_size, n_qa_answers)`):
Prediction scores of question answering objective (classification).
language_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for input features + one for the output of each cross-modality layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for input features + one for the output of each cross-modality layer) of
shape :obj:`(batch_size, sequence_length, hidden_size)`.
language_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: Optional[tf.Tensor] = None
prediction_logits: Optional[tf.Tensor] = None
cross_relationship_score: Optional[tf.Tensor] = None
question_answering_score: Optional[tf.Tensor] = None
language_hidden_states: Optional[Tuple[tf.Tensor]] = None
vision_hidden_states: Optional[Tuple[tf.Tensor]] = None
language_attentions: Optional[Tuple[tf.Tensor]] = None
vision_attentions: Optional[Tuple[tf.Tensor]] = None
cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None
class TFLxmertVisualFeatureEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
# Object feature encoding
self.visn_fc = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="visn_fc",
)
self.visn_layer_norm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="visn_layer_norm"
)
# Box position encoding
self.box_fc = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="box_fc",
)
self.box_layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="box_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, visn_input, training=False):
feats, boxes = visn_input
x = self.visn_fc(feats)
x = self.visn_layer_norm(x)
y = self.box_fc(boxes)
y = self.box_layer_norm(y)
output = (x + y) / 2
output = self.dropout(output, training=training)
return output
class TFLxmertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.type_vocab_size = config.type_vocab_size
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.initializer_range = config.initializer_range
self.embeddings_sum = tf.keras.layers.Add()
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
def build(self, input_shape):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
)
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
)
with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
)
super().build(input_shape)
def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
"""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
final_embeddings = self.LayerNorm(inputs=final_embeddings)
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
return final_embeddings
class TFLxmertAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
assert config.hidden_size % config.num_attention_heads == 0
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = tf.keras.layers.Dense(
self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name="query",
)
self.key = tf.keras.layers.Dense(
self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name="key",
)
self.value = tf.keras.layers.Dense(
self.all_head_size,
kernel_initializer=get_initializer(config.initializer_range),
name="value",
)
self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x, batch_size):
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, hidden_states, context, attention_mask, output_attentions, training=False):
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(context)
mixed_value_layer = self.value(context)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = tf.matmul(
query_layer, key_layer, transpose_b=True
) # (batch size, num_heads, seq_len_q, seq_len_k)
dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype) # scale attention_scores
attention_scores = attention_scores / tf.math.sqrt(dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFLxmertModel call() function)
attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
context_layer = tf.reshape(
context_layer, (batch_size, -1, self.all_head_size)
) # (batch_size, seq_len_q, all_head_size)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class TFLxmertIntermediate(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TFLxmertOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, input_tensor, training=False):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFLxmertAttentionOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, input_tensor, training=False):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFLxmertSelfAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self = TFLxmertAttention(config, name="self")
self.attention_output = TFLxmertAttentionOutput(config, name="output")
def call(self, input_tensor, attention_mask, output_attentions, training=False):
# Self attention attends to itself, thus keys and queries are the same (input_tensor).
self_output = self.self(input_tensor, input_tensor, attention_mask, output_attentions)
if output_attentions:
attention_probs = self_output[1]
attention_output = self.attention_output(self_output[0], input_tensor)
return (attention_output, attention_probs) if output_attentions else (attention_output,)
class TFLxmertCrossAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.att = TFLxmertAttention(config, name="att")
self.attention_output = TFLxmertAttentionOutput(config, name="output")
def call(
self,
input_tensor,
ctx_tensor,
ctx_att_mask,
output_attentions=False,
training=False,
):
output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions, training=training)
if output_attentions:
attention_probs = output[1]
attention_output = self.attention_output(output[0], input_tensor, training=training)
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
class TFLxmertLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attention = TFLxmertSelfAttentionLayer(config, name="attention")
self.intermediate = TFLxmertIntermediate(config, name="intermediate")
self.transformer_output = TFLxmertOutput(config, name="output")
def call(self, hidden_states, attention_mask, output_attentions, training=False):
attention_outputs = self.attention(hidden_states, attention_mask, output_attentions, training=training)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(attention_output)
layer_output = self.transformer_output(intermediate_output, attention_output, training=training)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
return outputs
class TFLxmertXLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention")
# Self-attention Layers
self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_self_att")
self.visn_self_att = TFLxmertSelfAttentionLayer(config, name="visn_self_att")
# Intermediate and Output Layers (FFNs)
self.lang_inter = TFLxmertIntermediate(config, name="lang_inter")
self.lang_output = TFLxmertOutput(config, name="lang_output")
self.visn_inter = TFLxmertIntermediate(config, name="visn_inter")
self.visn_output = TFLxmertOutput(config, name="visn_output")
def cross_att(
self,
lang_input,
lang_attention_mask,
visn_input,
visn_attention_mask,
output_attentions,
training=False,
):
# Cross Attention
# Keras saving and loading model *does not work* with the same inputs for two layers.
lang_attention_lang_input = tf.identity(lang_input)
visn_attention_lang_input = tf.identity(lang_input)
lang_attention_visn_input = tf.identity(visn_input)
visn_attention_visn_input = tf.identity(visn_input)
lang_att_output = self.visual_attention(
lang_attention_lang_input,
lang_attention_visn_input,
visn_attention_mask,
output_attentions=output_attentions,
training=training,
)
visn_att_output = self.visual_attention(
visn_attention_visn_input,
visn_attention_lang_input,
lang_attention_mask,
output_attentions=output_attentions,
training=training,
)
return lang_att_output, visn_att_output
def self_att(
self,
lang_input,
lang_attention_mask,
visn_input,
visn_attention_mask,
training=False,
):
# Self Attention
output_attentions = False
lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions, training=training)
visn_att_output = self.visn_self_att(visn_input, visn_attention_mask, output_attentions, training=training)
return lang_att_output[0], visn_att_output[0]
def output_fc(self, lang_input, visn_input, training=False):
# FC layers
lang_inter_output = self.lang_inter(lang_input)
visn_inter_output = self.visn_inter(visn_input)
# Layer output
lang_output = self.lang_output(lang_inter_output, lang_input, training)
visn_output = self.visn_output(visn_inter_output, visn_input, training)
return lang_output, visn_output
def call(
self,
lang_feats,
lang_attention_mask,
visn_feats,
visn_attention_mask,
output_attentions,
training=False,
):
lang_att_output = lang_feats
visn_att_output = visn_feats
lang_att_output, visn_att_output = self.cross_att(
lang_att_output,
lang_attention_mask,
visn_att_output,
visn_attention_mask,
output_attentions,
training=training,
)
attention_probs = lang_att_output[1:]
lang_att_output, visn_att_output = self.self_att(
lang_att_output[0],
lang_attention_mask,
visn_att_output[0],
visn_attention_mask,
training=training,
)
lang_output, visn_output = self.output_fc(lang_att_output, visn_att_output, training=training)
return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output)
class TFLxmertEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc")
# Number of layers
self.num_l_layers = config.l_layers
self.num_x_layers = config.x_layers
self.num_r_layers = config.r_layers
# Layers
# Using self.layer instead of self.l_layer to support loading BERT weights.
self.layer = [TFLxmertLayer(config, name="layer_._{}".format(i)) for i in range(self.num_l_layers)]
self.x_layers = [TFLxmertXLayer(config, name="x_layers_._{}".format(i)) for i in range(self.num_x_layers)]
self.r_layers = [TFLxmertLayer(config, name="r_layers_._{}".format(i)) for i in range(self.num_r_layers)]
self.config = config
def call(
self,
lang_feats=None,
lang_attention_mask=None,
visual_feats=None,
visual_pos=None,
visual_attention_mask=None,
output_attentions=None,
training=False,
):
vision_hidden_states = ()
language_hidden_states = ()
vision_attentions = () if output_attentions or self.config.output_attentions else None
language_attentions = () if output_attentions or self.config.output_attentions else None
cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None
visual_feats = self.visn_fc([visual_feats, visual_pos], training=training)
# Run language layers
for layer_module in self.layer:
l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions, training=training)
lang_feats = l_outputs[0]
language_hidden_states = language_hidden_states + (lang_feats,)
if language_attentions is not None:
language_attentions = language_attentions + (l_outputs[1],)
# Run relational layers
for layer_module in self.r_layers:
v_outputs = layer_module(
visual_feats,
visual_attention_mask,
output_attentions,
training=training,
)
visual_feats = v_outputs[0]
vision_hidden_states = vision_hidden_states + (visual_feats,)
if vision_attentions is not None:
vision_attentions = vision_attentions + (v_outputs[1],)
# Run cross-modality layers
for layer_module in self.x_layers:
x_outputs = layer_module(
lang_feats,
lang_attention_mask,
visual_feats,
visual_attention_mask,
output_attentions,
training=training,
)
lang_feats, visual_feats = x_outputs[:2]
vision_hidden_states = vision_hidden_states + (visual_feats,)
language_hidden_states = language_hidden_states + (lang_feats,)
if cross_encoder_attentions is not None:
cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
visual_encoder_outputs = (
vision_hidden_states,
vision_attentions if output_attentions else None,
)
lang_encoder_outputs = (
language_hidden_states,
language_attentions if output_attentions else None,
)
return (
visual_encoder_outputs,
lang_encoder_outputs,
cross_encoder_attentions if output_attentions else None,
)
@keras_serializable
class TFLxmertMainLayer(tf.keras.layers.Layer):
config_class = LxmertConfig
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
batch_size = 2
num_visual_features = 10
input_ids = tf.constant([[3, 5, 6], [2, 3, 4]])
visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
return {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": visual_pos,
}
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.num_l_layers = config.l_layers
self.num_x_layers = config.x_layers
self.num_r_layers = config.r_layers
self.initializer_range = config.initializer_range
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.return_dict = config.use_return_dict
self.embeddings = TFLxmertEmbeddings(config, name="embeddings")
self.encoder = TFLxmertEncoder(config, name="encoder")
self.pooler = TFLxmertPooler(config, name="pooler")
self.config = config
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings.weight = value
self.embeddings.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def call(
self,
input_ids=None,
visual_feats=None,
visual_pos=None,
attention_mask=None,
visual_attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
visual_feats=visual_feats,
visual_pos=visual_pos,
attention_mask=attention_mask,
visual_attention_mask=visual_attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["visual_pos"] is None or inputs["visual_feats"] is None:
raise ValueError("visual_feats and visual_pos cannot be `None` in LXMERT's `call` method.")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(input_shape, 1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(input_shape, 0)
# Positional Word Embeddings
embedding_output = self.embeddings(
inputs["input_ids"], inputs["token_type_ids"], inputs["inputs_embeds"], training=inputs["training"]
)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
if inputs["visual_attention_mask"] is not None:
extended_visual_attention_mask = tf.reshape(
inputs["visual_attention_mask"], (input_shape[0], 1, 1, input_shape[1])
)
extended_visual_attention_mask = tf.expand_dims(
tf.expand_dims(inputs["visual_attention_mask"], axis=1), axis=1
)
extended_visual_attention_mask = tf.cast(extended_visual_attention_mask, dtype=embedding_output.dtype)
extended_visual_attention_mask = tf.multiply(
tf.subtract(one_cst, extended_visual_attention_mask), ten_thousand_cst
)
else:
extended_visual_attention_mask = None
# Run Lxmert encoder
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
inputs["visual_feats"],
inputs["visual_pos"],
extended_visual_attention_mask,
output_attentions=inputs["output_attentions"],
training=inputs["training"],
)
visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
vision_hidden_states = visual_encoder_outputs[0]
language_hidden_states = lang_encoder_outputs[0]
all_attentions = ()
if inputs["output_attentions"]:
language_attentions = lang_encoder_outputs[1]
vision_attentions = visual_encoder_outputs[1]
cross_encoder_attentions = encoder_outputs[2]
all_attentions = (
language_attentions,
vision_attentions,
cross_encoder_attentions,
)
hidden_states = (language_hidden_states, vision_hidden_states) if inputs["output_hidden_states"] else ()
visual_output = vision_hidden_states[-1]
lang_output = language_hidden_states[-1]
pooled_output = self.pooler(lang_output)
if not inputs["return_dict"]:
return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions
return TFLxmertModelOutput(
pooled_output=pooled_output,
language_output=lang_output,
vision_output=visual_output,
language_hidden_states=language_hidden_states if inputs["output_hidden_states"] else None,
vision_hidden_states=vision_hidden_states if inputs["output_hidden_states"] else None,
language_attentions=language_attentions if inputs["output_attentions"] else None,
vision_attentions=vision_attentions if inputs["output_attentions"] else None,
cross_encoder_attentions=cross_encoder_attentions if inputs["output_attentions"] else None,
)
class TFLxmertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LxmertConfig
base_model_prefix = "lxmert"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
return getattr(self, self.base_model_prefix).dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"visual_feats": tf.TensorSpec((None, None, None), tf.float32, name="visual_feats"),
"visual_pos": tf.TensorSpec((None, None, None), tf.float32, name="visual_pos"),
"visual_attention_mask": tf.TensorSpec((None, None), tf.int32, name="visual_attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
LXMERT_START_DOCSTRING = r"""
The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers
<https://arxiv.org/abs/1908.07490>`__ by Hao Tan and Mohit Bansal. It's a vision and language transformer model,
pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual genome,
using a combination of masked language modeling, region of interest feature regression, cross entropy loss for
question answering attribute prediction, and object tag prediction.
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Parameters:
config (:class:`~transformers.LxmertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
LXMERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`np.ndarray` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.LxmertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
visual_feats: (:obj:`tf.Tensor` of shape :obj:՝(batch_size, num_visual_features, visual_feat_dim)՝):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos: (:obj:`tf.Tensor` of shape :obj:՝(batch_size, num_visual_features, visual_feat_dim)՝):
This input represents spacial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
visual_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
MMask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`__
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.",
LXMERT_START_DOCSTRING,
)
class TFLxmertModel(TFLxmertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.lxmert = TFLxmertMainLayer(config, name="lxmert")
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFLxmertModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids=None,
visual_feats=None,
visual_pos=None,
attention_mask=None,
visual_attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
visual_feats=visual_feats,
visual_pos=visual_pos,
attention_mask=attention_mask,
visual_attention_mask=visual_attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.lxmert(
input_ids=inputs["input_ids"],
visual_feats=inputs["visual_feats"],
visual_pos=inputs["visual_pos"],
attention_mask=inputs["attention_mask"],
visual_attention_mask=inputs["visual_attention_mask"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
return outputs
def serving_output(self, output):
l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None
v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None
l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None
v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None
c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None
return TFLxmertModelOutput(
pooled_output=output.pooled_output,
language_output=output.language_output,
vision_output=output.vision_output,
language_hidden_states=l_hs,
vision_hidden_states=v_hs,
language_attentions=l_attns,
vision_attentions=v_attns,
cross_encoder_attentions=c_enc_attns,
)
class TFLxmertPooler(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
def call(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
return pooled_output
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert
class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config: LxmertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(inputs=hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert
class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings = input_embeddings
def build(self, input_shape: tf.TensorShape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
super().build(input_shape)
def get_output_embeddings(self) -> tf.keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.vocab_size = shape_list(value["bias"])[0]
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.transform(hidden_states=hidden_states)
seq_length = shape_list(hidden_states)[1]
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
return hidden_states
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert
class TFLxmertMLMHead(tf.keras.layers.Layer):
def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
prediction_scores = self.predictions(hidden_states=sequence_output)
return prediction_scores
class TFLxmertPreTrainingHeads(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
self.seq_relationship = tf.keras.layers.Dense(
2,
kernel_initializer=get_initializer(config.initializer_range),
name="seq_relationship",
)
def call(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class TFLxmertVisualAnswerHead(tf.keras.layers.Layer):
def __init__(self, config, num_labels, **kwargs):
super().__init__(**kwargs)
hid_dim = config.hidden_size
self.dense = tf.keras.layers.Dense(
hid_dim * 2,
kernel_initializer=get_initializer(config.initializer_range),
name="logit_fc_._0",
)
self.activation = get_tf_activation("gelu")
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="logit_fc_._2")
self.dense_1 = tf.keras.layers.Dense(
num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name="logit_fc_._3",
)
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.dense_1(hidden_states)
return hidden_states
class TFLxmertVisualObjHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
# Decide the use of visual losses
visual_losses = {}
if config.visual_obj_loss:
visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels}
if config.visual_attr_loss:
visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels}
if config.visual_obj_loss:
visual_losses["feat"] = {"shape": (-1, 2048), "num": config.visual_feat_dim}
self.visual_losses = visual_losses
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder_dict = {
key: tf.keras.layers.Dense(
self.visual_losses[key]["num"],
kernel_initializer=get_initializer(config.initializer_range),
name=f"decoder_dict.{key}",
)
for key in self.visual_losses
}
def call(self, hidden_states):
hidden_states = self.transform(hidden_states)
output = {}
for key in self.visual_losses:
output[key] = self.decoder_dict[key](hidden_states)
return output
@add_start_docstrings("""Lxmert Model with a `language modeling` head on top. """, LXMERT_START_DOCSTRING)
class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = config.visual_loss_normalizer
# Use of pretraining tasks
self.task_mask_lm = config.task_mask_lm
self.task_obj_predict = config.task_obj_predict
self.task_matched = config.task_matched
self.task_qa = config.task_qa
# Lxmert backbone
self.lxmert = TFLxmertMainLayer(config, name="lxmert")
# Pre-training heads
self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls")
if self.task_obj_predict:
self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head")
if self.task_qa:
self.answer_head = TFLxmertVisualAnswerHead(config, self.num_qa_labels, name="answer_head")
# Loss functions
self.loss_fcts = {
"l2": tf.keras.losses.Huber(delta=1.0, name="huber_loss"),
"visn_ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
"ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
}
visual_losses = {}
if config.visual_obj_loss:
visual_losses["obj"] = {
"shape": (-1,),
"num": config.num_object_labels,
"loss": "visn_ce",
}
if config.visual_attr_loss:
visual_losses["attr"] = {
"shape": (-1,),
"num": config.num_attr_labels,
"loss": "visn_ce",
}
if config.visual_obj_loss:
visual_losses["feat"] = {
"shape": (-1, config.visual_feat_dim),
"num": config.visual_feat_dim,
"loss": "l2",
}
self.visual_losses = visual_losses
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
batch_size = 2
num_visual_features = 10
input_ids = tf.constant([[3, 5, 6], [2, 3, 4]])
visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
if self.config.task_obj_predict:
obj_labels = {}
if self.config.visual_attr_loss and self.config.task_obj_predict:
obj_labels["attr"] = (
tf.ones([batch_size, num_visual_features]),
tf.ones([batch_size, num_visual_features]),
)
if self.config.visual_feat_loss and self.config.task_obj_predict:
obj_labels["feat"] = (
tf.ones([batch_size, num_visual_features, self.config.visual_feat_dim]),
tf.ones([batch_size, num_visual_features]),
)
if self.config.visual_obj_loss and self.config.task_obj_predict:
obj_labels["obj"] = (
tf.ones([batch_size, num_visual_features]),
tf.ones([batch_size, num_visual_features]),
)
return {
**{
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": visual_pos,
},
**({"obj_labels": obj_labels} if self.config.task_obj_predict else {}),
}
def get_lm_head(self):
return self.cls.predictions
def get_prefix_bias_name(self):
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name
@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
visual_feats=None,
visual_pos=None,
attention_mask=None,
visual_attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
masked_lm_labels=None,
obj_labels=None,
matched_label=None,
ans=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
masked_lm_labels (``tf.Tensor`` of shape ``(batch_size, sequence_length)``, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
obj_labels: (``Dict[Str: Tuple[tf.Tensor, tf.Tensor]]``, `optional`, defaults to :obj: `None`):
each key is named after each one of the visual losses and each element of the tuple is of the shape
``(batch_size, num_features)`` and ``(batch_size, num_features, visual_feature_dim)`` for each the label id
and the label score respectively
matched_label (``tf.Tensor`` of shape ``(batch_size,)``, `optional`):
Labels for computing the whether or not the text input matches the image (classification) loss. Input
should be a sequence pair (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
- 0 indicates that the sentence does not match the image,
- 1 indicates that the sentence does match the image.
ans: (``Torch.Tensor`` of shape ``(batch_size)``, `optional`, defaults to :obj: `None`):
a one hot representation hof the correct answer `optional`
Returns:
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
visual_feats=visual_feats,
visual_pos=visual_pos,
attention_mask=attention_mask,
visual_attention_mask=visual_attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
masked_lm_labels=masked_lm_labels,
obj_labels=obj_labels,
matched_label=matched_label,
ans=ans,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
lxmert_output = self.lxmert(
input_ids=inputs["input_ids"],
visual_feats=inputs["visual_feats"],
visual_pos=inputs["visual_pos"],
attention_mask=inputs["attention_mask"],
visual_attention_mask=inputs["visual_attention_mask"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
lang_output, visual_output, pooled_output = (
lxmert_output[0],
lxmert_output[1],
lxmert_output[2],
)
lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
if self.task_qa:
answer_score = self.answer_head(pooled_output)
else:
answer_score = pooled_output[0][0]
total_loss = (
None
if (
inputs["masked_lm_labels"] is None
and inputs["matched_label"] is None
and inputs["obj_labels"] is None
and inputs["ans"] is None
)
else tf.constant(0.0)
)
losses = ()
if inputs["masked_lm_labels"] is not None and self.task_mask_lm:
masked_lm_loss = self.loss_fcts["ce"](
tf.reshape(inputs["masked_lm_labels"], [-1]),
tf.reshape(lang_prediction_scores, [-1, self.config.vocab_size]),
)
total_loss += masked_lm_loss
losses += (masked_lm_loss,)
if inputs["matched_label"] is not None and self.task_matched:
matched_loss = self.loss_fcts["ce"](
tf.reshape(inputs["matched_label"], [-1]),
tf.reshape(cross_relationship_score, [-1, 2]),
)
total_loss += matched_loss
losses += (matched_loss,)
if inputs["obj_labels"] is not None and self.task_obj_predict:
total_visn_loss = 0.0
visn_prediction_scores_dict = self.obj_predict_head(visual_output)
for key, key_info in self.visual_losses.items():
label, mask_conf = inputs["obj_labels"][key]
output_dim = key_info["num"]
loss_fct_name = key_info["loss"]
label_shape = key_info["shape"]
weight = self.visual_loss_normalizer
visn_loss_fct = self.loss_fcts[loss_fct_name]
visn_prediction_scores = visn_prediction_scores_dict[key]
visn_loss = visn_loss_fct(
tf.reshape(label, label_shape),
tf.reshape(visn_prediction_scores, [-1, output_dim]),
)
if visn_loss.ndim > 1: # Regression Losses
visn_loss = tf.reduce_mean(visn_loss)
visn_loss = tf.reduce_mean(visn_loss * tf.cast(tf.reshape(mask_conf, [-1]), visn_loss.dtype)) * weight
total_visn_loss += visn_loss
losses += (visn_loss,)
total_loss += total_visn_loss
if inputs["ans"] is not None and self.task_qa:
answer_loss = self.loss_fcts["ce"](
tf.reshape(ans, [-1]), tf.reshape(answer_score, [-1, self.num_qa_labels])
)
# exclude "*2" here to match the effect of QA losses.
# Previous: (loss *0) for 6 epochs, (loss *2) for 6 epochs. (Used 10 instead of 6 in EMNLP paper)
# Now : (loss *1) for 12 epochs
#
# * 2 # Multiply by 2 because > half of the data will not have label
total_loss += answer_loss
losses += (answer_loss,)
# return total_loss, tf.stack(losses)[tf.new_axis, ...], answer_score.detach()
if not inputs["return_dict"]:
output = (
lang_prediction_scores,
cross_relationship_score,
answer_score,
) + lxmert_output[3:]
return ((total_loss,) + output) if total_loss is not None else output
return TFLxmertForPreTrainingOutput(
loss=total_loss,
prediction_logits=lang_prediction_scores,
cross_relationship_score=cross_relationship_score,
question_answering_score=answer_score,
language_hidden_states=lxmert_output.language_hidden_states,
vision_hidden_states=lxmert_output.vision_hidden_states,
language_attentions=lxmert_output.language_attentions,
vision_attentions=lxmert_output.vision_attentions,
cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
)
def serving_output(self, output):
l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None
v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None
l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None
v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None
c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None
return TFLxmertForPreTrainingOutput(
prediction_logits=output.prediction_logits,
cross_relationship_score=output.cross_relationship_score,
question_answering_score=output.question_answering_score,
language_hidden_states=l_hs,
vision_hidden_states=v_hs,
language_attentions=l_attns,
vision_attentions=v_attns,
cross_encoder_attentions=c_enc_attns,
)
|
AdaMix/src/transformers/models/lxmert/modeling_tf_lxmert.py/0
|
{
"file_path": "AdaMix/src/transformers/models/lxmert/modeling_tf_lxmert.py",
"repo_id": "AdaMix",
"token_count": 29062
}
| 59 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
for k in ignore_keys:
state_dict.pop(k, None)
def make_linear_from_emb(emb):
vocab_size, emb_size = emb.weight.shape
lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
lin_layer.weight.data = emb.weight.data
return lin_layer
def convert_fairseq_mbart_checkpoint_from_disk(
checkpoint_path, hf_config_path="facebook/mbart-large-en-ro", finetuned=False, mbart_50=False
):
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
remove_ignore_keys_(state_dict)
vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
mbart_config = MBartConfig.from_pretrained(hf_config_path, vocab_size=vocab_size)
if mbart_50 and finetuned:
mbart_config.activation_function = "relu"
state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
model = MBartForConditionalGeneration(mbart_config)
model.model.load_state_dict(state_dict)
if finetuned:
model.lm_head = make_linear_from_emb(model.model.shared)
return model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
)
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--hf_config",
default="facebook/mbart-large-cc25",
type=str,
help="Which huggingface architecture to use: mbart-large",
)
parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
args = parser.parse_args()
model = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_50=args.mbart_50
)
model.save_pretrained(args.pytorch_dump_folder_path)
|
AdaMix/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py/0
|
{
"file_path": "AdaMix/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py",
"repo_id": "AdaMix",
"token_count": 1117
}
| 60 |
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