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<?xml version='1.0' encoding='UTF-8'?>
<glyph name="hospital" format="2">
<advance width="1200"/>
<unicode hex="F0FF6"/>
<note>
hospital
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<outline>
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</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/hospital.glif/0
|
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| 541 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="incognito-circle" format="2">
<advance width="1200"/>
<unicode hex="F1421"/>
<note>
incognito-circle
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|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/incognito-circle.glif/0
|
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| 542 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="keyboard-backspace" format="2">
<advance width="1200"/>
<unicode hex="F030D"/>
<note>
keyboard-backspace
</note>
<outline>
<contour>
<point x="1180" y="647" type="line"/>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/keyboard-backspace.glif/0
|
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| 543 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="klingon" format="2">
<advance width="1200"/>
<unicode hex="F135B"/>
<note>
klingon
</note>
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<contour>
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/klingon.glif/0
|
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| 544 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="ladder" format="2">
<advance width="1200"/>
<unicode hex="F15A2"/>
<note>
ladder
</note>
<outline>
<contour>
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<contour>
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<point x="736" y="472" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/ladder.glif/0
|
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| 545 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="language-markdown-outline" format="2">
<advance width="1200"/>
<unicode hex="F0F5B"/>
<note>
language-markdown-outline
</note>
<outline>
<contour>
<point x="1131" y="361"/>
<point x="1180" y="410"/>
<point x="1180" y="443" type="qcurve" smooth="yes"/>
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/language-markdown-outline.glif/0
|
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| 546 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="layers" format="2">
<advance width="1200"/>
<unicode hex="F0328"/>
<note>
layers
</note>
<outline>
<contour>
<point x="126" y="792" type="line"/>
<point x="600" y="423" type="line"/>
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<contour>
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</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/layers.glif/0
|
{
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| 547 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="led-strip" format="2">
<advance width="1200"/>
<unicode hex="F07D6"/>
<note>
led-strip
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<outline>
<contour>
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/led-strip.glif/0
|
{
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| 548 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="menu-right-outline" format="2">
<advance width="1200"/>
<unicode hex="F0A03"/>
<note>
menu-right-outline
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<outline>
<contour>
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</outline>
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|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/menu-right-outline.glif/0
|
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| 549 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="message-minus" format="2">
<advance width="1200"/>
<unicode hex="F116E"/>
<note>
message-minus
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</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/message-minus.glif/0
|
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| 550 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="music-note-whole" format="2">
<advance width="1200"/>
<unicode hex="F038D"/>
<note>
music-note-whole
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<outline>
<contour>
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<point x="20" y="468"/>
<point x="361" y="127"/>
<point x="839" y="127"/>
<point x="1180" y="468"/>
<point x="1180" y="952"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/music-note-whole.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/music-note-whole.glif",
"repo_id": "cascadia-code",
"token_count": 497
}
| 551 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="not-equal" format="2">
<advance width="1200"/>
<unicode hex="F098D"/>
<note>
not-equal
</note>
<outline>
<contour>
<point x="1180" y="1017" type="line"/>
<point x="362" y="1017" type="line"/>
<point x="362" y="883" type="line"/>
<point x="1180" y="883" type="line"/>
</contour>
<contour>
<point x="1180" y="608" type="line"/>
<point x="362" y="608" type="line"/>
<point x="362" y="474" type="line"/>
<point x="1180" y="474" type="line"/>
</contour>
<contour>
<point x="20" y="474" type="line"/>
<point x="157" y="474" type="line"/>
<point x="157" y="1221" type="line"/>
<point x="20" y="1221" type="line"/>
</contour>
<contour>
<point x="20" y="336" type="line"/>
<point x="20" y="199" type="line"/>
<point x="157" y="199" type="line"/>
<point x="157" y="336" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/not-equal.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/not-equal.glif",
"repo_id": "cascadia-code",
"token_count": 473
}
| 552 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="notebook-outline" format="2">
<advance width="1200"/>
<unicode hex="F0EBF"/>
<note>
notebook-outline
</note>
<outline>
<contour>
<point x="1050" y="1227" type="line"/>
<point x="1050" y="193" type="line"/>
<point x="407" y="193" type="line"/>
<point x="407" y="1227" type="line"/>
<point x="663" y="1227" type="line"/>
<point x="663" y="840" type="line"/>
<point x="793" y="967" type="line"/>
<point x="923" y="840" type="line"/>
<point x="923" y="1227" type="line"/>
</contour>
<contour>
<point x="150" y="1033" type="line"/>
<point x="150" y="773" type="line"/>
<point x="20" y="773" type="line"/>
<point x="20" y="647" type="line"/>
<point x="150" y="647" type="line"/>
<point x="150" y="387" type="line"/>
<point x="20" y="387" type="line"/>
<point x="20" y="260" type="line"/>
<point x="150" y="260" type="line"/>
<point x="150" y="193" type="line" smooth="yes"/>
<point x="150" y="145"/>
<point x="228" y="67"/>
<point x="277" y="67" type="qcurve" smooth="yes"/>
<point x="1050" y="67" type="line" smooth="yes"/>
<point x="1101" y="67"/>
<point x="1180" y="145"/>
<point x="1180" y="193" type="qcurve" smooth="yes"/>
<point x="1180" y="1227" type="line" smooth="yes"/>
<point x="1180" y="1275"/>
<point x="1101" y="1353"/>
<point x="1050" y="1353" type="qcurve" smooth="yes"/>
<point x="277" y="1353" type="line" smooth="yes"/>
<point x="225" y="1353"/>
<point x="150" y="1278"/>
<point x="150" y="1227" type="qcurve" smooth="yes"/>
<point x="150" y="1160" type="line"/>
<point x="20" y="1160" type="line"/>
<point x="20" y="1033" type="line"/>
</contour>
<contour>
<point x="277" y="1160" type="line"/>
<point x="150" y="1160" type="line"/>
<point x="150" y="1033" type="line"/>
<point x="277" y="1033" type="line"/>
</contour>
<contour>
<point x="150" y="387" type="line"/>
<point x="150" y="260" type="line"/>
<point x="277" y="260" type="line"/>
<point x="277" y="387" type="line"/>
</contour>
<contour>
<point x="150" y="773" type="line"/>
<point x="150" y="647" type="line"/>
<point x="277" y="647" type="line"/>
<point x="277" y="773" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/notebook-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/notebook-outline.glif",
"repo_id": "cascadia-code",
"token_count": 1186
}
| 553 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="numeric-4" format="2">
<advance width="1200"/>
<unicode hex="F0B3D"/>
<note>
numeric-4
</note>
<outline>
<contour>
<point x="461" y="1420" type="line"/>
<point x="175" y="1420" type="line"/>
<point x="175" y="571" type="line"/>
<point x="739" y="571" type="line"/>
<point x="739" y="0" type="line"/>
<point x="1025" y="0" type="line"/>
<point x="1025" y="1420" type="line"/>
<point x="739" y="1420" type="line"/>
<point x="739" y="849" type="line"/>
<point x="461" y="849" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/numeric-4.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/numeric-4.glif",
"repo_id": "cascadia-code",
"token_count": 312
}
| 554 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="pan-right" format="2">
<advance width="1200"/>
<unicode hex="F0BBA"/>
<note>
pan-right
</note>
<outline>
<contour>
<point x="1180" y="710" type="line"/>
<point x="727" y="1111" type="line"/>
<point x="727" y="309" type="line"/>
</contour>
<contour>
<point x="308" y="913"/>
<point x="223" y="913" type="qcurve" smooth="yes"/>
<point x="138" y="913"/>
<point x="20" y="795"/>
<point x="20" y="625"/>
<point x="138" y="507"/>
<point x="308" y="507"/>
<point x="426" y="625"/>
<point x="426" y="795"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/pan-right.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/pan-right.glif",
"repo_id": "cascadia-code",
"token_count": 331
}
| 555 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="phone-bluetooth-outline" format="2">
<advance width="1200"/>
<unicode hex="F118F"/>
<note>
phone-bluetooth-outline
</note>
<outline>
<contour>
<point x="1141" y="418"/>
<point x="1117" y="418" type="qcurve" smooth="yes"/>
<point x="990" y="418"/>
<point x="884" y="458" type="qcurve"/>
<point x="863" y="458" type="line" smooth="yes"/>
<point x="839" y="458"/>
<point x="821" y="440" type="qcurve" smooth="yes"/>
<point x="679" y="298" type="line"/>
<point x="540" y="370"/>
<point x="319" y="591"/>
<point x="253" y="724" type="qcurve"/>
<point x="395" y="866" type="line"/>
<point x="410" y="878"/>
<point x="416" y="914"/>
<point x="407" y="929" type="qcurve"/>
<point x="373" y="1047"/>
<point x="373" y="1162" type="qcurve" smooth="yes"/>
<point x="373" y="1186"/>
<point x="334" y="1225"/>
<point x="310" y="1225" type="qcurve" smooth="yes"/>
<point x="83" y="1225" type="line" smooth="yes"/>
<point x="59" y="1225"/>
<point x="20" y="1186"/>
<point x="20" y="1162" type="qcurve" smooth="yes"/>
<point x="20" y="863"/>
<point x="168" y="609" type="qcurve" smooth="yes"/>
<point x="313" y="358"/>
<point x="564" y="213" type="qcurve" smooth="yes"/>
<point x="817" y="65"/>
<point x="1117" y="65" type="qcurve" smooth="yes"/>
<point x="1141" y="65"/>
<point x="1180" y="104"/>
<point x="1180" y="128" type="qcurve" smooth="yes"/>
<point x="1180" y="355" type="line" smooth="yes"/>
<point x="1180" y="379"/>
</contour>
<contour>
<point x="159" y="980"/>
<point x="150" y="1095" type="qcurve"/>
<point x="247" y="1095" type="line"/>
<point x="253" y="1026"/>
<point x="277" y="929" type="qcurve"/>
<point x="201" y="850" type="line"/>
</contour>
<contour>
<point x="1050" y="298" type="qcurve"/>
<point x="1050" y="195" type="line"/>
<point x="935" y="204"/>
<point x="805" y="246" type="qcurve"/>
<point x="884" y="322" type="line"/>
<point x="987" y="298"/>
</contour>
<contour>
<point x="987" y="832" type="line"/>
<point x="987" y="953" type="line"/>
<point x="1047" y="893" type="line"/>
</contour>
<contour>
<point x="987" y="1110" type="line"/>
<point x="987" y="1231" type="line"/>
<point x="1047" y="1171" type="line"/>
</contour>
<contour>
<point x="730" y="850" type="line"/>
<point x="775" y="805" type="line"/>
<point x="923" y="953" type="line"/>
<point x="923" y="708" type="line"/>
<point x="953" y="708" type="line"/>
<point x="1138" y="893" type="line"/>
<point x="999" y="1032" type="line"/>
<point x="1138" y="1171" type="line"/>
<point x="953" y="1355" type="line"/>
<point x="923" y="1355" type="line"/>
<point x="923" y="1110" type="line"/>
<point x="775" y="1258" type="line"/>
<point x="730" y="1213" type="line"/>
<point x="908" y="1032" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/phone-bluetooth-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/phone-bluetooth-outline.glif",
"repo_id": "cascadia-code",
"token_count": 1573
}
| 556 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="pine-tree" format="2">
<advance width="1200"/>
<unicode hex="F0405"/>
<note>
pine-tree
</note>
<outline>
<contour>
<point x="730" y="130" type="line"/>
<point x="730" y="323" type="line"/>
<point x="1180" y="323" type="line"/>
<point x="857" y="647" type="line"/>
<point x="1050" y="647" type="line"/>
<point x="730" y="967" type="line"/>
<point x="923" y="967" type="line"/>
<point x="600" y="1290" type="line"/>
<point x="277" y="967" type="line"/>
<point x="470" y="967" type="line"/>
<point x="150" y="647" type="line"/>
<point x="343" y="647" type="line"/>
<point x="20" y="323" type="line"/>
<point x="470" y="323" type="line"/>
<point x="470" y="130" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/pine-tree.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/pine-tree.glif",
"repo_id": "cascadia-code",
"token_count": 405
}
| 557 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="propane-tank" format="2">
<advance width="1200"/>
<unicode hex="F1357"/>
<note>
propane-tank
</note>
<outline>
<contour>
<point x="1170" y="497" type="line"/>
<point x="30" y="497" type="line"/>
<point x="30" y="283" type="line" smooth="yes"/>
<point x="30" y="167"/>
<point x="197" y="0"/>
<point x="317" y="0" type="qcurve" smooth="yes"/>
<point x="883" y="0" type="line" smooth="yes"/>
<point x="1003" y="0"/>
<point x="1170" y="167"/>
<point x="1170" y="283" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="30" y="640" type="line"/>
<point x="1170" y="640" type="line"/>
<point x="1170" y="853" type="line" smooth="yes"/>
<point x="1170" y="950"/>
<point x="1050" y="1103"/>
<point x="957" y="1127" type="qcurve"/>
<point x="957" y="1280" type="line" smooth="yes"/>
<point x="957" y="1337"/>
<point x="873" y="1420"/>
<point x="813" y="1420" type="qcurve" smooth="yes"/>
<point x="387" y="1420" type="line" smooth="yes"/>
<point x="327" y="1420"/>
<point x="243" y="1337"/>
<point x="243" y="1280" type="qcurve" smooth="yes"/>
<point x="243" y="1127" type="line"/>
<point x="150" y="1103"/>
<point x="30" y="950"/>
<point x="30" y="853" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="387" y="1137" type="line"/>
<point x="387" y="1280" type="line"/>
<point x="813" y="1280" type="line"/>
<point x="813" y="1137" type="line"/>
<point x="670" y="1137" type="line"/>
<point x="670" y="1167"/>
<point x="630" y="1207"/>
<point x="570" y="1207"/>
<point x="530" y="1167"/>
<point x="530" y="1137" type="qcurve"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/propane-tank.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/propane-tank.glif",
"repo_id": "cascadia-code",
"token_count": 919
}
| 558 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="qrcode-remove" format="2">
<advance width="1200"/>
<unicode hex="F118D"/>
<note>
qrcode-remove
</note>
<outline>
<contour>
<point x="232" y="974" type="line"/>
<point x="336" y="974" type="line"/>
<point x="336" y="1078" type="line"/>
<point x="232" y="1078" type="line"/>
</contour>
<contour>
<point x="20" y="762" type="line"/>
<point x="548" y="762" type="line"/>
<point x="548" y="1290" type="line"/>
<point x="20" y="1290" type="line"/>
</contour>
<contour>
<point x="442" y="1184" type="line"/>
<point x="442" y="868" type="line"/>
<point x="126" y="868" type="line"/>
<point x="126" y="1184" type="line"/>
</contour>
<contour>
<point x="232" y="342" type="line"/>
<point x="336" y="342" type="line"/>
<point x="336" y="446" type="line"/>
<point x="232" y="446" type="line"/>
</contour>
<contour>
<point x="20" y="130" type="line"/>
<point x="548" y="130" type="line"/>
<point x="548" y="658" type="line"/>
<point x="20" y="658" type="line"/>
</contour>
<contour>
<point x="442" y="552" type="line"/>
<point x="442" y="236" type="line"/>
<point x="126" y="236" type="line"/>
<point x="126" y="552" type="line"/>
</contour>
<contour>
<point x="652" y="342" type="line"/>
<point x="758" y="342" type="line"/>
<point x="758" y="236" type="line"/>
<point x="652" y="236" type="line"/>
<point x="652" y="130" type="line"/>
<point x="758" y="130" type="line"/>
<point x="758" y="236" type="line"/>
<point x="968" y="236" type="line"/>
<point x="968" y="130" type="line"/>
<point x="1180" y="130" type="line"/>
<point x="1180" y="446" type="line"/>
<point x="968" y="446" type="line"/>
<point x="968" y="552" type="line"/>
<point x="1180" y="552" type="line"/>
<point x="1180" y="658" type="line"/>
<point x="968" y="658" type="line"/>
<point x="968" y="552" type="line"/>
<point x="864" y="552" type="line"/>
<point x="864" y="658" type="line"/>
<point x="652" y="658" type="line"/>
</contour>
<contour>
<point x="968" y="236" type="line"/>
<point x="1074" y="236" type="line"/>
<point x="1074" y="342" type="line"/>
<point x="968" y="342" type="line"/>
</contour>
<contour>
<point x="968" y="342" type="line"/>
<point x="968" y="446" type="line"/>
<point x="864" y="446" type="line"/>
<point x="864" y="552" type="line"/>
<point x="758" y="552" type="line"/>
<point x="758" y="342" type="line"/>
</contour>
<contour>
<point x="916" y="1100" type="line"/>
<point x="768" y="1248" type="line"/>
<point x="694" y="1174" type="line"/>
<point x="842" y="1026" type="line"/>
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<point x="768" y="804" type="line"/>
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<point x="1064" y="804" type="line"/>
<point x="1138" y="878" type="line"/>
<point x="990" y="1026" type="line"/>
<point x="1138" y="1174" type="line"/>
<point x="1064" y="1248" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/qrcode-remove.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 1593
}
| 559 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="receipt-outline" format="2">
<advance width="1200"/>
<unicode hex="F19DC"/>
<note>
receipt-outline
</note>
<outline>
<contour>
<point x="1180" y="1353" type="line"/>
<point x="1083" y="1257" type="line"/>
<point x="987" y="1353" type="line"/>
<point x="890" y="1257" type="line"/>
<point x="793" y="1353" type="line"/>
<point x="697" y="1257" type="line"/>
<point x="600" y="1353" type="line"/>
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<point x="407" y="1353" type="line"/>
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<point x="213" y="1353" type="line"/>
<point x="117" y="1257" type="line"/>
<point x="20" y="1353" type="line"/>
<point x="20" y="67" type="line"/>
<point x="117" y="163" type="line"/>
<point x="213" y="67" type="line"/>
<point x="310" y="163" type="line"/>
<point x="407" y="67" type="line"/>
<point x="503" y="163" type="line"/>
<point x="600" y="67" type="line"/>
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<point x="793" y="67" type="line"/>
<point x="890" y="163" type="line"/>
<point x="987" y="67" type="line"/>
<point x="1083" y="163" type="line"/>
<point x="1180" y="67" type="line"/>
</contour>
<contour>
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<point x="1050" y="254" type="line"/>
<point x="150" y="254" type="line"/>
<point x="150" y="1166" type="line"/>
</contour>
<contour>
<point x="213" y="387" type="line"/>
<point x="987" y="387" type="line"/>
<point x="987" y="517" type="line"/>
<point x="213" y="517" type="line"/>
</contour>
<contour>
<point x="213" y="647" type="line"/>
<point x="987" y="647" type="line"/>
<point x="987" y="773" type="line"/>
<point x="213" y="773" type="line"/>
</contour>
<contour>
<point x="213" y="903" type="line"/>
<point x="987" y="903" type="line"/>
<point x="987" y="1033" type="line"/>
<point x="213" y="1033" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/receipt-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/receipt-outline.glif",
"repo_id": "cascadia-code",
"token_count": 1033
}
| 560 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="rectangle" format="2">
<advance width="1200"/>
<unicode hex="F0E5E"/>
<note>
rectangle
</note>
<outline>
<contour>
<point x="1180" y="1180" type="line"/>
<point x="20" y="1180" type="line"/>
<point x="20" y="240" type="line"/>
<point x="1180" y="240" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/rectangle.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/rectangle.glif",
"repo_id": "cascadia-code",
"token_count": 180
}
| 561 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="relation-many-to-one-or-many" format="2">
<advance width="1200"/>
<unicode hex="F1498"/>
<note>
relation-many-to-one-or-many
</note>
<outline>
<contour>
<point x="1123" y="653" type="line"/>
<point x="1006" y="536" type="line"/>
<point x="1006" y="653" type="line"/>
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<point x="77" y="1116" type="line"/>
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<point x="1180" y="304" type="line"/>
<point x="1180" y="653" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/relation-many-to-one-or-many.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 568
}
| 562 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="repeat-once" format="2">
<advance width="1200"/>
<unicode hex="F0458"/>
<note>
repeat-once
</note>
<outline>
<contour>
<point x="567" y="517" type="line"/>
<point x="663" y="517" type="line"/>
<point x="663" y="903" type="line"/>
<point x="600" y="903" type="line"/>
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<point x="470" y="773" type="line"/>
<point x="567" y="773" type="line"/>
</contour>
<contour>
<point x="923" y="647" type="line"/>
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<point x="277" y="580" type="line"/>
<point x="20" y="323" type="line"/>
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<point x="277" y="260" type="line"/>
<point x="1050" y="260" type="line"/>
<point x="1050" y="647" type="line"/>
</contour>
<contour>
<point x="277" y="773" type="line"/>
<point x="277" y="1033" type="line"/>
<point x="923" y="1033" type="line"/>
<point x="923" y="840" type="line"/>
<point x="1180" y="1097" type="line"/>
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<point x="923" y="1160" type="line"/>
<point x="150" y="1160" type="line"/>
<point x="150" y="773" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/repeat-once.glif/0
|
{
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"repo_id": "cascadia-code",
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| 563 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="scatter-plot" format="2">
<advance width="1200"/>
<unicode hex="F0EC9"/>
<note>
scatter-plot
</note>
<outline>
<contour>
<point x="152" y="767"/>
<point x="20" y="639"/>
<point x="20" y="454"/>
<point x="152" y="322"/>
<point x="337" y="322"/>
<point x="466" y="454"/>
<point x="466" y="639"/>
<point x="337" y="767"/>
<point x="243" y="767" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="448" y="1363"/>
<point x="320" y="1234"/>
<point x="320" y="1046"/>
<point x="448" y="917"/>
<point x="633" y="917"/>
<point x="765" y="1046"/>
<point x="765" y="1234"/>
<point x="633" y="1363"/>
<point x="543" y="1363" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="866" y="503"/>
<point x="734" y="370"/>
<point x="734" y="186"/>
<point x="866" y="57"/>
<point x="1051" y="57"/>
<point x="1180" y="186"/>
<point x="1180" y="370"/>
<point x="1051" y="503"/>
<point x="957" y="503" type="qcurve" smooth="yes"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/scatter-plot.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 614
}
| 564 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="screw-round-top" format="2">
<advance width="1200"/>
<unicode hex="F0DF7"/>
<note>
screw-round-top
</note>
<outline>
<contour>
<point x="493" y="213" type="line"/>
<point x="600" y="0" type="line"/>
<point x="707" y="213" type="line"/>
<point x="707" y="353" type="line"/>
</contour>
<contour>
<point x="777" y="1037" type="line"/>
<point x="777" y="1117" type="line"/>
<point x="707" y="1067" type="line"/>
<point x="707" y="1137" type="line"/>
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<point x="493" y="923" type="line"/>
<point x="423" y="873" type="line"/>
<point x="423" y="803" type="line"/>
</contour>
<contour>
<point x="777" y="753" type="line"/>
<point x="777" y="830" type="line"/>
<point x="707" y="780" type="line"/>
<point x="707" y="923" type="line"/>
<point x="493" y="780" type="line"/>
<point x="493" y="640" type="line"/>
<point x="423" y="590" type="line"/>
<point x="423" y="517" type="line"/>
</contour>
<contour>
<point x="777" y="467" type="line"/>
<point x="777" y="547" type="line"/>
<point x="707" y="497" type="line"/>
<point x="707" y="640" type="line"/>
<point x="493" y="497" type="line"/>
<point x="493" y="353" type="line"/>
<point x="423" y="303" type="line"/>
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</contour>
<contour>
<point x="243" y="1207" type="qcurve"/>
<point x="243" y="1207" type="line"/>
<point x="957" y="1207" type="line"/>
<point x="940" y="1240" type="line" smooth="yes"/>
<point x="917" y="1280"/>
<point x="883" y="1313" type="qcurve" smooth="yes"/>
<point x="837" y="1360"/>
<point x="777" y="1387" type="qcurve" smooth="yes"/>
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<point x="600" y="1420" type="qcurve" smooth="yes"/>
<point x="423" y="1420"/>
<point x="317" y="1313" type="qcurve" smooth="yes"/>
<point x="263" y="1260"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/screw-round-top.glif/0
|
{
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"repo_id": "cascadia-code",
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}
| 565 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="shape-circle-plus" format="2">
<advance width="1200"/>
<unicode hex="F065D"/>
<note>
shape-circle-plus
</note>
<outline>
<contour>
<point x="410" y="253"/>
<point x="241" y="351"/>
<point x="143" y="520"/>
<point x="143" y="717"/>
<point x="241" y="886"/>
<point x="410" y="986"/>
<point x="510" y="986" type="qcurve"/>
<point x="510" y="1107" type="line"/>
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<point x="20" y="752"/>
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<point x="865" y="262"/>
<point x="997" y="485"/>
<point x="997" y="620" type="qcurve"/>
<point x="876" y="620" type="line"/>
<point x="876" y="520"/>
<point x="776" y="351"/>
<point x="607" y="253"/>
<point x="510" y="253" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="997" y="1290" type="line"/>
<point x="876" y="1290" type="line"/>
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<point x="876" y="986" type="line"/>
<point x="876" y="803" type="line"/>
<point x="997" y="803" type="line"/>
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<point x="1180" y="1107" type="line"/>
<point x="997" y="1107" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/shape-circle-plus.glif/0
|
{
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"repo_id": "cascadia-code",
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}
| 566 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="shuffle-variant" format="2">
<advance width="1200"/>
<unicode hex="F049F"/>
<note>
shuffle-variant
</note>
<outline>
<contour>
<point x="879" y="1054" type="line"/>
<point x="721" y="1054" type="line"/>
<point x="205" y="538" type="line"/>
<point x="20" y="538" type="line"/>
<point x="20" y="366" type="line"/>
<point x="278" y="366" type="line"/>
<point x="793" y="882" type="line"/>
<point x="879" y="882" type="line"/>
<point x="879" y="538" type="line"/>
<point x="793" y="538" type="line"/>
<point x="681" y="648" type="line"/>
<point x="560" y="527" type="line"/>
<point x="721" y="366" type="line"/>
<point x="879" y="366" type="line"/>
<point x="879" y="194" type="line"/>
<point x="1180" y="452" type="line"/>
<point x="879" y="710" type="line"/>
<point x="1180" y="968" type="line"/>
<point x="879" y="1226" type="line"/>
</contour>
<contour>
<point x="20" y="882" type="line"/>
<point x="205" y="882" type="line"/>
<point x="318" y="772" type="line"/>
<point x="439" y="893" type="line"/>
<point x="278" y="1054" type="line"/>
<point x="20" y="1054" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/shuffle-variant.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/shuffle-variant.glif",
"repo_id": "cascadia-code",
"token_count": 634
}
| 567 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="smoke-detector" format="2">
<advance width="1200"/>
<unicode hex="F0392"/>
<note>
smoke-detector
</note>
<outline>
<contour>
<point x="494" y="323"/>
<point x="316" y="426"/>
<point x="213" y="604"/>
<point x="213" y="816"/>
<point x="316" y="994"/>
<point x="494" y="1097"/>
<point x="706" y="1097"/>
<point x="884" y="994"/>
<point x="987" y="816"/>
<point x="987" y="604"/>
<point x="884" y="426"/>
<point x="706" y="323"/>
<point x="600" y="323" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="150" y="1290" type="line" smooth="yes"/>
<point x="96" y="1290"/>
<point x="20" y="1214"/>
<point x="20" y="1160" type="qcurve" smooth="yes"/>
<point x="20" y="260" type="line" smooth="yes"/>
<point x="20" y="206"/>
<point x="96" y="130"/>
<point x="150" y="130" type="qcurve" smooth="yes"/>
<point x="1050" y="130" type="line" smooth="yes"/>
<point x="1104" y="130"/>
<point x="1180" y="206"/>
<point x="1180" y="260" type="qcurve" smooth="yes"/>
<point x="1180" y="1160" type="line" smooth="yes"/>
<point x="1180" y="1214"/>
<point x="1104" y="1290"/>
<point x="1050" y="1290" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="343" y="604"/>
<point x="494" y="453"/>
<point x="706" y="453"/>
<point x="857" y="604"/>
<point x="857" y="816"/>
<point x="706" y="967"/>
<point x="494" y="967"/>
<point x="343" y="816"/>
<point x="343" y="710" type="qcurve" smooth="yes"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/smoke-detector.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 858
}
| 568 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="snowflake" format="2">
<advance width="1200"/>
<unicode hex="F0717"/>
<note>
snowflake
</note>
<outline>
<contour>
<point x="1177" y="467" type="line"/>
<point x="1148" y="587" type="line"/>
<point x="1002" y="549" type="line"/>
<point x="876" y="619" type="line"/>
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<point x="660" y="999" type="line"/>
<point x="660" y="1140" type="line"/>
<point x="768" y="1245" type="line"/>
<point x="680" y="1332" type="line"/>
<point x="599" y="1253" type="line"/>
<point x="517" y="1332" type="line"/>
<point x="429" y="1245" type="line"/>
<point x="537" y="1140" type="line"/>
<point x="537" y="999" type="line"/>
<point x="379" y="909" type="line"/>
<point x="257" y="979" type="line"/>
<point x="219" y="1125" type="line"/>
<point x="99" y="1093" type="line"/>
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<point x="20" y="953" type="line"/>
<point x="52" y="833" type="line"/>
<point x="195" y="871" type="line"/>
<point x="321" y="801" type="line"/>
<point x="321" y="619" type="line"/>
<point x="195" y="549" type="line"/>
<point x="52" y="587" type="line"/>
<point x="20" y="467" type="line"/>
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<point x="260" y="441" type="line"/>
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<point x="429" y="175" type="line"/>
<point x="517" y="88" type="line"/>
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<point x="680" y="88" type="line"/>
<point x="768" y="175" type="line"/>
<point x="660" y="280" type="line"/>
<point x="660" y="421" type="line"/>
<point x="818" y="511" type="line"/>
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<point x="981" y="298" type="line"/>
<point x="1098" y="327" type="line"/>
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</contour>
<contour>
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<point x="444" y="801" type="line"/>
<point x="599" y="891" type="line"/>
<point x="753" y="801" type="line"/>
<point x="753" y="619" type="line"/>
<point x="599" y="529" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/snowflake.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 1349
}
| 569 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="sort" format="2">
<advance width="1200"/>
<unicode hex="F04BA"/>
<note>
sort
</note>
<outline>
<contour>
<point x="1180" y="419" type="line"/>
<point x="1006" y="419" type="line"/>
<point x="1006" y="1001" type="line"/>
<point x="1180" y="1001" type="line"/>
<point x="949" y="1233" type="line"/>
<point x="717" y="1001" type="line"/>
<point x="891" y="1001" type="line"/>
<point x="891" y="419" type="line"/>
<point x="717" y="419" type="line"/>
<point x="949" y="187" type="line"/>
</contour>
<contour>
<point x="600" y="304" type="line"/>
<point x="600" y="419" type="line"/>
<point x="20" y="419" type="line"/>
<point x="20" y="304" type="line"/>
</contour>
<contour>
<point x="426" y="653" type="line"/>
<point x="426" y="767" type="line"/>
<point x="20" y="767" type="line"/>
<point x="20" y="653" type="line"/>
</contour>
<contour>
<point x="251" y="1001" type="line"/>
<point x="251" y="1116" type="line"/>
<point x="20" y="1116" type="line"/>
<point x="20" y="1001" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/sort.glif/0
|
{
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"repo_id": "cascadia-code",
"token_count": 591
}
| 570 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="source-commit" format="2">
<advance width="1200"/>
<unicode hex="F0718"/>
<note>
source-commit
</note>
<outline>
<contour>
<point x="996" y="851"/>
<point x="814" y="1069"/>
<point x="678" y="1098" type="qcurve"/>
<point x="678" y="1420" type="line"/>
<point x="522" y="1420" type="line"/>
<point x="522" y="1098" type="line"/>
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<point x="204" y="851"/>
<point x="204" y="569"/>
<point x="386" y="351"/>
<point x="522" y="322" type="qcurve"/>
<point x="522" y="0" type="line"/>
<point x="678" y="0" type="line"/>
<point x="678" y="322" type="line"/>
<point x="814" y="351"/>
<point x="996" y="569"/>
<point x="996" y="710" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="700" y="947"/>
<point x="837" y="810"/>
<point x="837" y="610"/>
<point x="700" y="473"/>
<point x="500" y="473"/>
<point x="363" y="610"/>
<point x="363" y="810"/>
<point x="500" y="947"/>
<point x="600" y="947" type="qcurve" smooth="yes"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/source-commit.glif/0
|
{
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"repo_id": "cascadia-code",
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}
| 571 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="star-settings-outline" format="2">
<advance width="1200"/>
<unicode hex="F166B"/>
<note>
star-settings-outline
</note>
<outline>
<contour>
<point x="543" y="71" type="line"/>
<point x="657" y="71" type="line"/>
<point x="657" y="189" type="line"/>
<point x="543" y="189" type="line"/>
</contour>
<contour>
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<point x="309" y="71" type="line"/>
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<contour>
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<point x="774" y="71" type="line"/>
<point x="891" y="71" type="line"/>
<point x="891" y="189" type="line"/>
</contour>
<contour>
<point x="1180" y="929" type="line"/>
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<point x="600" y="1349" type="line"/>
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<point x="959" y="246" type="line"/>
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</contour>
<contour>
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<point x="382" y="439" type="line"/>
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<point x="600" y="1112" type="line"/>
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<point x="954" y="856" type="line"/>
<point x="761" y="690" type="line"/>
<point x="818" y="439" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/star-settings-outline.glif/0
|
{
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"repo_id": "cascadia-code",
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}
| 572 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="table-row-plus-before" format="2">
<advance width="1200"/>
<unicode hex="F04F4"/>
<note>
table-row-plus-before
</note>
<outline>
<contour>
<point x="1180" y="159" type="line"/>
<point x="1180" y="564" type="line" smooth="yes"/>
<point x="1180" y="613"/>
<point x="1112" y="681"/>
<point x="1066" y="681" type="qcurve" smooth="yes"/>
<point x="134" y="681" type="line" smooth="yes"/>
<point x="88" y="681"/>
<point x="20" y="613"/>
<point x="20" y="564" type="qcurve" smooth="yes"/>
<point x="20" y="159" type="line"/>
<point x="134" y="159" type="line"/>
<point x="134" y="276" type="line"/>
<point x="369" y="276" type="line"/>
<point x="369" y="159" type="line"/>
<point x="483" y="159" type="line"/>
<point x="483" y="276" type="line"/>
<point x="717" y="276" type="line"/>
<point x="717" y="159" type="line"/>
<point x="831" y="159" type="line"/>
<point x="831" y="276" type="line"/>
<point x="1066" y="276" type="line"/>
<point x="1066" y="159" type="line"/>
</contour>
<contour>
<point x="134" y="390" type="line"/>
<point x="134" y="564" type="line"/>
<point x="369" y="564" type="line"/>
<point x="369" y="390" type="line"/>
</contour>
<contour>
<point x="483" y="390" type="line"/>
<point x="483" y="564" type="line"/>
<point x="717" y="564" type="line"/>
<point x="717" y="390" type="line"/>
</contour>
<contour>
<point x="831" y="564" type="line"/>
<point x="1066" y="564" type="line"/>
<point x="1066" y="390" type="line"/>
<point x="831" y="390" type="line"/>
</contour>
<contour>
<point x="543" y="973" type="line"/>
<point x="543" y="798" type="line"/>
<point x="657" y="798" type="line"/>
<point x="657" y="973" type="line"/>
<point x="831" y="973" type="line"/>
<point x="831" y="1087" type="line"/>
<point x="657" y="1087" type="line"/>
<point x="657" y="1261" type="line"/>
<point x="543" y="1261" type="line"/>
<point x="543" y="1087" type="line"/>
<point x="369" y="1087" type="line"/>
<point x="369" y="973" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/table-row-plus-before.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/table-row-plus-before.glif",
"repo_id": "cascadia-code",
"token_count": 1134
}
| 573 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="tag-off-outline" format="2">
<advance width="1200"/>
<unicode hex="F1221"/>
<note>
tag-off-outline
</note>
<outline>
<contour>
<point x="303" y="1124"/>
<point x="270" y="1124" type="qcurve" smooth="yes"/>
<point x="233" y="1124"/>
<point x="186" y="1077"/>
<point x="186" y="1007"/>
<point x="233" y="957"/>
<point x="303" y="957"/>
<point x="353" y="1007"/>
<point x="353" y="1077"/>
</contour>
<contour>
<point x="303" y="1124"/>
<point x="270" y="1124" type="qcurve" smooth="yes"/>
<point x="233" y="1124"/>
<point x="186" y="1077"/>
<point x="186" y="1007"/>
<point x="233" y="957"/>
<point x="303" y="957"/>
<point x="353" y="1007"/>
<point x="353" y="1077"/>
</contour>
<contour>
<point x="1097" y="759" type="qcurve" smooth="yes"/>
<point x="925" y="931" type="line"/>
<point x="1180" y="1183" type="line"/>
<point x="1102" y="1264" type="line"/>
<point x="46" y="208" type="line"/>
<point x="127" y="130" type="line"/>
<point x="379" y="385" type="line"/>
<point x="551" y="213" type="line" smooth="yes"/>
<point x="584" y="182"/>
<point x="675" y="182"/>
<point x="707" y="213" type="qcurve" smooth="yes"/>
<point x="1097" y="603" type="line" smooth="yes"/>
<point x="1128" y="635"/>
<point x="1128" y="726"/>
</contour>
<contour>
<point x="1019" y="681" type="line"/>
<point x="629" y="291" type="line"/>
<point x="460" y="463" type="line"/>
<point x="847" y="850" type="line"/>
</contour>
<contour>
<point x="51" y="713" type="qcurve" smooth="yes"/>
<point x="220" y="546" type="line"/>
<point x="296" y="624" type="line"/>
<point x="129" y="791" type="line"/>
<point x="129" y="1181" type="line"/>
<point x="519" y="1181" type="line"/>
<point x="688" y="1009" type="line"/>
<point x="766" y="1087" type="line"/>
<point x="597" y="1259" type="line" smooth="yes"/>
<point x="564" y="1290"/>
<point x="519" y="1290" type="qcurve" smooth="yes"/>
<point x="129" y="1290" type="line" smooth="yes"/>
<point x="85" y="1290"/>
<point x="20" y="1225"/>
<point x="20" y="1181" type="qcurve" smooth="yes"/>
<point x="20" y="791" type="line" smooth="yes"/>
<point x="20" y="746"/>
</contour>
<contour>
<point x="186" y="1077"/>
<point x="186" y="1040" type="qcurve" smooth="yes"/>
<point x="186" y="1007"/>
<point x="233" y="957"/>
<point x="303" y="957"/>
<point x="353" y="1007"/>
<point x="353" y="1077"/>
<point x="303" y="1124"/>
<point x="233" y="1124"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tag-off-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tag-off-outline.glif",
"repo_id": "cascadia-code",
"token_count": 1412
}
| 574 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="tally-mark-4" format="2">
<advance width="1200"/>
<unicode hex="F1ABF"/>
<note>
tally-mark-4
</note>
<outline>
<contour>
<point x="183" y="1290" type="line"/>
<point x="20" y="1290" type="line"/>
<point x="20" y="130" type="line"/>
<point x="183" y="130" type="line"/>
</contour>
<contour>
<point x="518" y="130" type="line"/>
<point x="518" y="1290" type="line"/>
<point x="351" y="1290" type="line"/>
<point x="351" y="130" type="line"/>
</contour>
<contour>
<point x="849" y="130" type="line"/>
<point x="849" y="1290" type="line"/>
<point x="682" y="1290" type="line"/>
<point x="682" y="130" type="line"/>
</contour>
<contour>
<point x="1180" y="130" type="line"/>
<point x="1180" y="1290" type="line"/>
<point x="1017" y="1290" type="line"/>
<point x="1017" y="130" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tally-mark-4.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tally-mark-4.glif",
"repo_id": "cascadia-code",
"token_count": 485
}
| 575 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="tent" format="2">
<advance width="1200"/>
<unicode hex="F0508"/>
<note>
tent
</note>
<outline>
<contour>
<point x="134" y="1110"/>
<point x="167" y="1200"/>
<point x="194" y="1233" type="qcurve"/>
<point x="121" y="1233"/>
<point x="20" y="1132"/>
<point x="20" y="985"/>
<point x="121" y="884"/>
<point x="194" y="884" type="qcurve"/>
<point x="167" y="917"/>
<point x="134" y="1007"/>
<point x="134" y="1059" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="1180" y="187" type="line"/>
<point x="1180" y="304" type="line"/>
<point x="1019" y="304" type="line"/>
<point x="600" y="1129" type="line"/>
<point x="181" y="304" type="line"/>
<point x="20" y="304" type="line"/>
<point x="20" y="187" type="line"/>
</contour>
<contour>
<point x="891" y="304" type="line"/>
<point x="309" y="304" type="line"/>
<point x="600" y="873" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tent.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tent.glif",
"repo_id": "cascadia-code",
"token_count": 536
}
| 576 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="toggle-switch-variant-off" format="2">
<advance width="1200"/>
<unicode hex="F1A26"/>
<note>
toggle-switch-variant-off
</note>
<outline>
<contour>
<point x="150" y="1341"/>
<point x="150" y="1290" type="qcurve" smooth="yes"/>
<point x="150" y="130" type="line" smooth="yes"/>
<point x="150" y="79"/>
<point x="225" y="0"/>
<point x="277" y="0" type="qcurve" smooth="yes"/>
<point x="923" y="0" type="line" smooth="yes"/>
<point x="975" y="0"/>
<point x="1050" y="79"/>
<point x="1050" y="130" type="qcurve" smooth="yes"/>
<point x="1050" y="1290" type="line" smooth="yes"/>
<point x="1050" y="1341"/>
<point x="975" y="1420"/>
<point x="923" y="1420" type="qcurve" smooth="yes"/>
<point x="277" y="1420" type="line" smooth="yes"/>
<point x="225" y="1420"/>
<point x="186" y="1381" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="857" y="1290" type="line" smooth="yes"/>
<point x="887" y="1290"/>
<point x="923" y="1254"/>
<point x="923" y="1227" type="qcurve" smooth="yes"/>
<point x="923" y="193" type="line" smooth="yes"/>
<point x="923" y="166"/>
<point x="887" y="130"/>
<point x="857" y="130" type="qcurve" smooth="yes"/>
<point x="343" y="130" type="line" smooth="yes"/>
<point x="313" y="130"/>
<point x="277" y="166"/>
<point x="277" y="193" type="qcurve" smooth="yes"/>
<point x="277" y="1227" type="line" smooth="yes"/>
<point x="277" y="1254"/>
<point x="313" y="1290"/>
<point x="343" y="1290" type="qcurve" smooth="yes"/>
</contour>
<contour>
<point x="343" y="260" type="line" smooth="yes"/>
<point x="343" y="230"/>
<point x="376" y="193"/>
<point x="407" y="193" type="qcurve" smooth="yes"/>
<point x="793" y="193" type="line" smooth="yes"/>
<point x="824" y="193"/>
<point x="857" y="230"/>
<point x="857" y="260" type="qcurve" smooth="yes"/>
<point x="857" y="387" type="line" smooth="yes"/>
<point x="857" y="417"/>
<point x="824" y="453"/>
<point x="793" y="453" type="qcurve" smooth="yes"/>
<point x="407" y="453" type="line" smooth="yes"/>
<point x="376" y="453"/>
<point x="343" y="417"/>
<point x="343" y="387" type="qcurve" smooth="yes"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/toggle-switch-variant-off.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/toggle-switch-variant-off.glif",
"repo_id": "cascadia-code",
"token_count": 1184
}
| 577 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="tooltip-minus" format="2">
<advance width="1200"/>
<unicode hex="F155E"/>
<note>
tooltip-minus
</note>
<outline>
<contour>
<point x="1112" y="1290"/>
<point x="1066" y="1290" type="qcurve" smooth="yes"/>
<point x="134" y="1290" type="line" smooth="yes"/>
<point x="88" y="1290"/>
<point x="20" y="1222"/>
<point x="20" y="1176" type="qcurve" smooth="yes"/>
<point x="20" y="479" type="line" smooth="yes"/>
<point x="20" y="430"/>
<point x="88" y="361"/>
<point x="134" y="361" type="qcurve" smooth="yes"/>
<point x="369" y="361" type="line"/>
<point x="600" y="130" type="line"/>
<point x="831" y="361" type="line"/>
<point x="1066" y="361" type="line" smooth="yes"/>
<point x="1112" y="361"/>
<point x="1180" y="430"/>
<point x="1180" y="479" type="qcurve" smooth="yes"/>
<point x="1180" y="1176" type="line" smooth="yes"/>
<point x="1180" y="1222"/>
</contour>
<contour>
<point x="831" y="884" type="line"/>
<point x="831" y="767" type="line"/>
<point x="369" y="767" type="line"/>
<point x="369" y="884" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tooltip-minus.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/tooltip-minus.glif",
"repo_id": "cascadia-code",
"token_count": 601
}
| 578 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="view-carousel-outline" format="2">
<advance width="1200"/>
<unicode hex="F1486"/>
<note>
view-carousel-outline
</note>
<outline>
<contour>
<point x="20" y="389" type="line"/>
<point x="251" y="389" type="line"/>
<point x="251" y="1029" type="line"/>
<point x="20" y="1029" type="line"/>
</contour>
<contour>
<point x="309" y="1146" type="line"/>
<point x="309" y="274" type="line"/>
<point x="891" y="274" type="line"/>
<point x="891" y="1146" type="line"/>
</contour>
<contour>
<point x="426" y="389" type="line"/>
<point x="426" y="1029" type="line"/>
<point x="774" y="1029" type="line"/>
<point x="774" y="389" type="line"/>
</contour>
<contour>
<point x="949" y="389" type="line"/>
<point x="1180" y="389" type="line"/>
<point x="1180" y="1029" type="line"/>
<point x="949" y="1029" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-carousel-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-carousel-outline.glif",
"repo_id": "cascadia-code",
"token_count": 484
}
| 579 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="view-sequential" format="2">
<advance width="1200"/>
<unicode hex="F0729"/>
<note>
view-sequential
</note>
<outline>
<contour>
<point x="20" y="903" type="line"/>
<point x="1180" y="903" type="line"/>
<point x="1180" y="1160" type="line"/>
<point x="20" y="1160" type="line"/>
</contour>
<contour>
<point x="20" y="580" type="line"/>
<point x="1180" y="580" type="line"/>
<point x="1180" y="840" type="line"/>
<point x="20" y="840" type="line"/>
</contour>
<contour>
<point x="20" y="260" type="line"/>
<point x="1180" y="260" type="line"/>
<point x="1180" y="517" type="line"/>
<point x="20" y="517" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-sequential.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/view-sequential.glif",
"repo_id": "cascadia-code",
"token_count": 380
}
| 580 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="volcano-outline" format="2">
<advance width="1200"/>
<unicode hex="F1A84"/>
<note>
volcano-outline
</note>
<outline>
<contour>
<point x="1180" y="100" type="line"/>
<point x="949" y="912" type="line"/>
<point x="543" y="912" type="line"/>
<point x="426" y="623" type="line"/>
<point x="251" y="623" type="line"/>
<point x="20" y="100" type="line"/>
</contour>
<contour>
<point x="197" y="214" type="line"/>
<point x="328" y="506" type="line"/>
<point x="505" y="506" type="line"/>
<point x="619" y="797" type="line"/>
<point x="861" y="797" type="line"/>
<point x="1028" y="214" type="line"/>
</contour>
<contour>
<point x="657" y="1086" type="line"/>
<point x="774" y="1086" type="line"/>
<point x="774" y="1320" type="line"/>
<point x="657" y="1320" type="line"/>
</contour>
<contour>
<point x="921" y="977" type="line"/>
<point x="1085" y="1143" type="line"/>
<point x="1003" y="1225" type="line"/>
<point x="840" y="1059" type="line"/>
</contour>
<contour>
<point x="510" y="977" type="line"/>
<point x="592" y="1061" type="line"/>
<point x="428" y="1225" type="line"/>
<point x="347" y="1143" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/volcano-outline.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/volcano-outline.glif",
"repo_id": "cascadia-code",
"token_count": 667
}
| 581 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="vuejs" format="2">
<advance width="1200"/>
<unicode hex="F0844"/>
<note>
vuejs
</note>
<outline>
<contour>
<point x="600" y="187" type="line"/>
<point x="1180" y="1233" type="line"/>
<point x="978" y="1233" type="line"/>
<point x="600" y="536" type="line"/>
<point x="222" y="1233" type="line"/>
<point x="20" y="1233" type="line"/>
</contour>
<contour>
<point x="600" y="647" type="line"/>
<point x="919" y="1233" type="line"/>
<point x="744" y="1233" type="line"/>
<point x="600" y="966" type="line"/>
<point x="456" y="1233" type="line"/>
<point x="281" y="1233" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/vuejs.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/vuejs.glif",
"repo_id": "cascadia-code",
"token_count": 364
}
| 582 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="youtube-gaming" format="2">
<advance width="1200"/>
<unicode hex="F0848"/>
<note>
youtube-gaming
</note>
<outline>
<contour>
<point x="600" y="304" type="line"/>
<point x="1180" y="653" type="line"/>
<point x="1180" y="941" type="line"/>
<point x="891" y="1116" type="line"/>
<point x="600" y="941" type="line"/>
<point x="309" y="1116" type="line"/>
<point x="20" y="941" type="line"/>
<point x="20" y="653" type="line"/>
</contour>
<contour>
<point x="426" y="827" type="line"/>
<point x="426" y="767" type="line"/>
<point x="309" y="767" type="line"/>
<point x="309" y="653" type="line"/>
<point x="251" y="653" type="line"/>
<point x="251" y="767" type="line"/>
<point x="134" y="767" type="line"/>
<point x="134" y="827" type="line"/>
<point x="251" y="827" type="line"/>
<point x="251" y="941" type="line"/>
<point x="309" y="941" type="line"/>
<point x="309" y="827" type="line"/>
</contour>
<contour>
<point x="799" y="653"/>
<point x="774" y="653" type="qcurve" smooth="yes"/>
<point x="750" y="653"/>
<point x="717" y="685"/>
<point x="717" y="735"/>
<point x="750" y="767"/>
<point x="799" y="767"/>
<point x="831" y="735"/>
<point x="831" y="685"/>
</contour>
<contour>
<point x="973" y="767"/>
<point x="949" y="767" type="qcurve" smooth="yes"/>
<point x="924" y="767"/>
<point x="891" y="803"/>
<point x="891" y="849"/>
<point x="924" y="884"/>
<point x="973" y="884"/>
<point x="1006" y="849"/>
<point x="1006" y="803"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/youtube-gaming.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/MaterialDesignIconsDesktop.ufo/glyphs/youtube-gaming.glif",
"repo_id": "cascadia-code",
"token_count": 882
}
| 583 |
<?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>com.schriftgestaltung.disablesAutomaticAlignment</key>
<false/>
<key>com.schriftgestaltung.fontMasterID</key>
<string>m01</string>
<key>com.schriftgestaltung.formatVersion</key>
<integer>3</integer>
<key>com.schriftgestaltung.useGlyphOrder</key>
<true/>
<key>com.schriftgestaltung.useNiceNames</key>
<false/>
<key>public.glyphOrder</key>
<array>
<string>.notdef</string>
<string>POWER SYMBOL</string>
<string>POWER ON-OFF SYMBOL</string>
<string>HEAVY CIRCLE</string>
<string>POWER ON SYMBOL</string>
<string>POWER SLEEP SYMBOL</string>
</array>
<key>public.postscriptNames</key>
<dict>
<key>HEAVY CIRCLE</key>
<string>uni2B58</string>
<key>POWER ON SYMBOL</key>
<string>uni23FD</string>
<key>POWER ON-OFF SYMBOL</key>
<string>uni23FC</string>
<key>POWER SLEEP SYMBOL</key>
<string>uni23FE</string>
<key>POWER SYMBOL</key>
<string>uni23FB</string>
</dict>
</dict>
</plist>
|
cascadia-code/sources/nerdfonts/full/processed/Unicode_IEC_symbol_font.ufo/lib.plist/0
|
{
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"repo_id": "cascadia-code",
"token_count": 575
}
| 584 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="arrow-swap" format="2">
<advance width="1200"/>
<unicode hex="EBCB"/>
<note>
arrow-swap
</note>
<outline>
<contour>
<point x="343" y="183" type="line"/>
<point x="148" y="378" type="line"/>
<point x="1180" y="378" type="line"/>
<point x="1180" y="462" type="line"/>
<point x="148" y="462" type="line"/>
<point x="343" y="657" type="line"/>
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<point x="20" y="449" type="line"/>
<point x="20" y="391" type="line"/>
<point x="286" y="126" type="line"/>
</contour>
<contour>
<point x="857" y="763" type="line"/>
<point x="914" y="706" type="line"/>
<point x="1180" y="971" type="line"/>
<point x="1180" y="1029" type="line"/>
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<point x="857" y="1237" type="line"/>
<point x="1052" y="1042" type="line"/>
<point x="20" y="1042" type="line"/>
<point x="20" y="958" type="line"/>
<point x="1052" y="958" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/arrow-swap.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/arrow-swap.glif",
"repo_id": "cascadia-code",
"token_count": 532
}
| 585 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="circle-filled" format="2">
<advance width="1200"/>
<unicode hex="EA71"/>
<note>
circle-filled
</note>
<outline>
<contour>
<point x="523" y="1290"/>
<point x="376" y="1251"/>
<point x="306" y="1213" type="qcurve" smooth="yes"/>
<point x="175" y="1135"/>
<point x="97" y="1004" type="qcurve" smooth="yes"/>
<point x="59" y="934"/>
<point x="20" y="787"/>
<point x="20" y="633"/>
<point x="43" y="555" type="qcurve" smooth="yes"/>
<point x="82" y="408"/>
<point x="298" y="192"/>
<point x="445" y="153" type="qcurve" smooth="yes"/>
<point x="523" y="130"/>
<point x="677" y="130"/>
<point x="755" y="153" type="qcurve" smooth="yes"/>
<point x="902" y="192"/>
<point x="1118" y="408"/>
<point x="1157" y="555" type="qcurve" smooth="yes"/>
<point x="1180" y="633"/>
<point x="1180" y="787"/>
<point x="1157" y="865" type="qcurve" smooth="yes"/>
<point x="1118" y="1012"/>
<point x="902" y="1228"/>
<point x="755" y="1267" type="qcurve" smooth="yes"/>
<point x="677" y="1290"/>
<point x="600" y="1290" type="qcurve" smooth="yes"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/circle-filled.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/circle-filled.glif",
"repo_id": "cascadia-code",
"token_count": 623
}
| 586 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="combine" format="2">
<advance width="1200"/>
<unicode hex="EBB6"/>
<note>
combine
</note>
<outline>
<contour>
<point x="288" y="1251" type="line"/>
<point x="57" y="1251" type="line"/>
<point x="20" y="1214" type="line"/>
<point x="20" y="982" type="line"/>
<point x="57" y="941" type="line"/>
<point x="288" y="941" type="line"/>
<point x="330" y="982" type="line"/>
<point x="330" y="1214" type="line"/>
</contour>
<contour>
<point x="251" y="1020" type="line"/>
<point x="98" y="1020" type="line"/>
<point x="98" y="1176" type="line"/>
<point x="251" y="1176" type="line"/>
</contour>
<contour>
<point x="288" y="867" type="line"/>
<point x="57" y="867" type="line"/>
<point x="20" y="826" type="line"/>
<point x="20" y="594" type="line"/>
<point x="57" y="557" type="line"/>
<point x="288" y="557" type="line"/>
<point x="330" y="594" type="line"/>
<point x="330" y="826" type="line"/>
</contour>
<contour>
<point x="251" y="632" type="line"/>
<point x="98" y="632" type="line"/>
<point x="98" y="788" type="line"/>
<point x="251" y="788" type="line"/>
</contour>
<contour>
<point x="20" y="206" type="line"/>
<point x="57" y="169" type="line"/>
<point x="288" y="169" type="line"/>
<point x="330" y="206" type="line"/>
<point x="330" y="438" type="line"/>
<point x="288" y="479" type="line"/>
<point x="57" y="479" type="line"/>
<point x="20" y="438" type="line"/>
</contour>
<contour>
<point x="251" y="400" type="line"/>
<point x="251" y="248" type="line"/>
<point x="98" y="248" type="line"/>
<point x="98" y="400" type="line"/>
</contour>
<contour>
<point x="1143" y="941" type="line"/>
<point x="908" y="941" type="line"/>
<point x="870" y="904" type="line"/>
<point x="870" y="438" type="line"/>
<point x="908" y="400" type="line"/>
<point x="1143" y="400" type="line"/>
<point x="1180" y="438" type="line"/>
<point x="1180" y="904" type="line"/>
</contour>
<contour>
<point x="1102" y="867" type="line"/>
<point x="1102" y="710" type="line"/>
<point x="949" y="710" type="line"/>
<point x="949" y="867" type="line"/>
</contour>
<contour>
<point x="1102" y="632" type="line"/>
<point x="1102" y="479" type="line"/>
<point x="949" y="479" type="line"/>
<point x="949" y="632" type="line"/>
</contour>
<contour>
<point x="569" y="788" type="line"/>
<point x="647" y="710" type="line"/>
<point x="408" y="710" type="line"/>
<point x="408" y="632" type="line"/>
<point x="647" y="632" type="line"/>
<point x="569" y="557" type="line"/>
<point x="623" y="508" type="line"/>
<point x="763" y="648" type="line"/>
<point x="763" y="702" type="line"/>
<point x="623" y="842" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/combine.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/combine.glif",
"repo_id": "cascadia-code",
"token_count": 1522
}
| 587 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="device-mobile" format="2">
<advance width="1200"/>
<unicode hex="EADB"/>
<note>
device-mobile
</note>
<outline>
<contour>
<point x="142" y="1371" type="line"/>
<point x="142" y="49" type="line"/>
<point x="191" y="0" type="line"/>
<point x="1004" y="0" type="line"/>
<point x="1058" y="49" type="line"/>
<point x="1058" y="1371" type="line"/>
<point x="1004" y="1420" type="line"/>
<point x="191" y="1420" type="line"/>
</contour>
<contour>
<point x="245" y="1322" type="line"/>
<point x="955" y="1322" type="line"/>
<point x="955" y="103" type="line"/>
<point x="245" y="103" type="line"/>
</contour>
<contour>
<point x="500" y="201" type="line"/>
<point x="700" y="201" type="line"/>
<point x="700" y="304" type="line"/>
<point x="500" y="304" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/device-mobile.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/device-mobile.glif",
"repo_id": "cascadia-code",
"token_count": 460
}
| 588 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="extensions" format="2">
<advance width="1200"/>
<unicode hex="EAE6"/>
<note>
extensions
</note>
<outline>
<contour>
<point x="673" y="857" type="line"/>
<point x="747" y="783" type="line"/>
<point x="1107" y="783" type="line"/>
<point x="1180" y="857" type="line"/>
<point x="1180" y="1217" type="line"/>
<point x="1107" y="1290" type="line"/>
<point x="747" y="1290" type="line"/>
<point x="673" y="1217" type="line"/>
</contour>
<contour>
<point x="1107" y="1217" type="line"/>
<point x="1107" y="857" type="line"/>
<point x="747" y="857" type="line"/>
<point x="747" y="1217" type="line"/>
</contour>
<contour>
<point x="20" y="203" type="line"/>
<point x="93" y="130" type="line"/>
<point x="890" y="130" type="line"/>
<point x="963" y="203" type="line"/>
<point x="963" y="567" type="line"/>
<point x="890" y="637" type="line"/>
<point x="527" y="637" type="line"/>
<point x="527" y="1000" type="line"/>
<point x="457" y="1073" type="line"/>
<point x="93" y="1073" type="line"/>
<point x="20" y="1000" type="line"/>
<point x="20" y="567" type="line"/>
</contour>
<contour>
<point x="93" y="637" type="line"/>
<point x="93" y="1000" type="line"/>
<point x="457" y="1000" type="line"/>
<point x="457" y="637" type="line"/>
</contour>
<contour>
<point x="457" y="203" type="line"/>
<point x="93" y="203" type="line"/>
<point x="93" y="567" type="line"/>
<point x="457" y="567" type="line"/>
</contour>
<contour>
<point x="527" y="567" type="line"/>
<point x="890" y="567" type="line"/>
<point x="890" y="203" type="line"/>
<point x="527" y="203" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/extensions.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/extensions.glif",
"repo_id": "cascadia-code",
"token_count": 932
}
| 589 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="loading" format="2">
<advance width="1200"/>
<unicode hex="EB19"/>
<note>
loading
</note>
<outline>
<contour>
<point x="1180" y="460" type="line"/>
<point x="1158" y="603"/>
<point x="993" y="830"/>
<point x="743" y="960"/>
<point x="457" y="960"/>
<point x="207" y="830"/>
<point x="42" y="603"/>
<point x="20" y="460" type="qcurve"/>
<point x="105" y="460" type="line"/>
<point x="127" y="576"/>
<point x="265" y="768"/>
<point x="480" y="875"/>
<point x="720" y="875"/>
<point x="935" y="768"/>
<point x="1073" y="576"/>
<point x="1095" y="460" type="qcurve"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/loading.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/loading.glif",
"repo_id": "cascadia-code",
"token_count": 373
}
| 590 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="multiple-windows" format="2">
<advance width="1200"/>
<unicode hex="EB23"/>
<note>
multiple-windows
</note>
<outline>
<contour>
<point x="436" y="958" type="line"/>
<point x="516" y="958" type="line"/>
<point x="516" y="1042" type="line"/>
<point x="1100" y="1042" type="line"/>
<point x="1100" y="710" type="line"/>
<point x="932" y="710" type="line"/>
<point x="932" y="626" type="line"/>
<point x="1140" y="626" type="line"/>
<point x="1180" y="670" type="line"/>
<point x="1180" y="1250" type="line"/>
<point x="1140" y="1290" type="line"/>
<point x="476" y="1290" type="line"/>
<point x="436" y="1250" type="line"/>
</contour>
<contour>
<point x="1100" y="1210" type="line"/>
<point x="1100" y="1126" type="line"/>
<point x="516" y="1126" type="line"/>
<point x="516" y="1210" type="line"/>
</contour>
<contour>
<point x="724" y="794" type="line"/>
<point x="60" y="794" type="line"/>
<point x="20" y="750" type="line"/>
<point x="20" y="170" type="line"/>
<point x="60" y="130" type="line"/>
<point x="724" y="130" type="line"/>
<point x="768" y="170" type="line"/>
<point x="768" y="750" type="line"/>
</contour>
<contour>
<point x="684" y="626" type="line"/>
<point x="104" y="626" type="line"/>
<point x="104" y="710" type="line"/>
<point x="684" y="710" type="line"/>
</contour>
<contour>
<point x="104" y="214" type="line"/>
<point x="104" y="546" type="line"/>
<point x="684" y="546" type="line"/>
<point x="684" y="214" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/multiple-windows.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/multiple-windows.glif",
"repo_id": "cascadia-code",
"token_count": 839
}
| 591 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="save-all" format="2">
<advance width="1200"/>
<unicode hex="EB49"/>
<note>
save-all
</note>
<outline>
<contour>
<point x="1180" y="1126" type="line"/>
<point x="1167" y="1153" type="line"/>
<point x="1043" y="1277" type="line"/>
<point x="1016" y="1290" type="line"/>
<point x="308" y="1290" type="line"/>
<point x="268" y="1250" type="line"/>
<point x="268" y="1042" type="line"/>
<point x="60" y="1042" type="line"/>
<point x="20" y="1002" type="line"/>
<point x="20" y="170" type="line"/>
<point x="60" y="130" type="line"/>
<point x="892" y="130" type="line"/>
<point x="932" y="170" type="line"/>
<point x="932" y="378" type="line"/>
<point x="1140" y="378" type="line"/>
<point x="1180" y="418" type="line"/>
</contour>
<contour>
<point x="848" y="861" type="line"/>
<point x="848" y="214" type="line"/>
<point x="104" y="214" type="line"/>
<point x="104" y="958" type="line"/>
<point x="184" y="958" type="line"/>
<point x="184" y="706" type="line"/>
<point x="684" y="706" type="line"/>
<point x="684" y="958" type="line"/>
<point x="751" y="958" type="line"/>
</contour>
<contour>
<point x="600" y="794" type="line"/>
<point x="436" y="794" type="line"/>
<point x="436" y="958" type="line"/>
<point x="600" y="958" type="line"/>
</contour>
<contour>
<point x="1100" y="1108" type="line"/>
<point x="1100" y="462" type="line"/>
<point x="932" y="462" type="line"/>
<point x="932" y="878" type="line"/>
<point x="919" y="905" type="line"/>
<point x="795" y="1029" type="line"/>
<point x="768" y="1042" type="line"/>
<point x="352" y="1042" type="line"/>
<point x="352" y="1210" type="line"/>
<point x="998" y="1210" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/save-all.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/save-all.glif",
"repo_id": "cascadia-code",
"token_count": 959
}
| 592 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="sort-precedence" format="2">
<advance width="1200"/>
<unicode hex="EB55"/>
<note>
sort-precedence
</note>
<outline>
<contour>
<point x="1100" y="1208" type="line"/>
<point x="516" y="1208" type="line"/>
<point x="436" y="1124" type="line"/>
<point x="436" y="876" type="line"/>
<point x="516" y="876" type="line"/>
<point x="516" y="1124" type="line"/>
<point x="1100" y="1124" type="line"/>
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<point x="1114" y="934" type="line"/>
<point x="1180" y="867" type="line"/>
<point x="1180" y="1124" type="line"/>
</contour>
<contour>
<point x="600" y="876" type="line"/>
<point x="768" y="876" type="line"/>
<point x="768" y="1040" type="line"/>
<point x="600" y="1040" type="line"/>
</contour>
<contour>
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<point x="352" y="624" type="line"/>
<point x="184" y="624" type="line"/>
<point x="184" y="460" type="line"/>
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<contour>
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<point x="104" y="792" type="line"/>
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<point x="104" y="212" type="line"/>
<point x="684" y="212" type="line"/>
<point x="768" y="292" type="line"/>
<point x="768" y="708" type="line"/>
</contour>
<contour>
<point x="684" y="292" type="line"/>
<point x="104" y="292" type="line"/>
<point x="104" y="708" type="line"/>
<point x="684" y="708" type="line"/>
</contour>
<contour>
<point x="600" y="376" type="line"/>
<point x="600" y="544" type="line"/>
<point x="436" y="544" type="line"/>
<point x="436" y="376" type="line"/>
</contour>
<contour>
<point x="1016" y="721" type="line"/>
<point x="1016" y="1040" type="line"/>
<point x="932" y="1040" type="line"/>
<point x="932" y="721" type="line"/>
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<point x="777" y="756" type="line"/>
<point x="945" y="588" type="line"/>
<point x="1003" y="588" type="line"/>
<point x="1171" y="756" type="line"/>
<point x="1114" y="818" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/sort-precedence.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/sort-precedence.glif",
"repo_id": "cascadia-code",
"token_count": 1127
}
| 593 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="symbol-event" format="2">
<advance width="1200"/>
<unicode hex="EA86"/>
<note>
symbol-event
</note>
<outline>
<contour>
<point x="156" y="656" type="line"/>
<point x="248" y="509" type="line"/>
<point x="372" y="509" type="line"/>
<point x="156" y="119" type="line"/>
<point x="318" y="0" type="line"/>
<point x="1044" y="743" type="line"/>
<point x="969" y="916" type="line"/>
<point x="790" y="916" type="line"/>
<point x="1012" y="1263" type="line"/>
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<point x="595" y="1420" type="line"/>
<point x="502" y="1366" type="line"/>
</contour>
<contour>
<point x="248" y="607" type="line"/>
<point x="595" y="1322" type="line"/>
<point x="931" y="1322" type="line"/>
<point x="595" y="813" type="line"/>
<point x="969" y="813" type="line"/>
<point x="248" y="70" type="line"/>
<point x="540" y="607" type="line"/>
</contour>
</outline>
</glyph>
|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/symbol-event.glif/0
|
{
"file_path": "cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/symbol-event.glif",
"repo_id": "cascadia-code",
"token_count": 506
}
| 594 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="symbol-structure" format="2">
<advance width="1200"/>
<unicode hex="EA91"/>
<note>
symbol-structure
</note>
<outline>
<contour>
<point x="1100" y="1208" type="line"/>
<point x="104" y="1208" type="line"/>
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cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/symbol-structure.glif/0
|
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| 595 |
<?xml version='1.0' encoding='UTF-8'?>
<glyph name="text-size" format="2">
<advance width="1200"/>
<unicode hex="EB69"/>
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text-size
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|
cascadia-code/sources/nerdfonts/full/processed/codicon.ufo/glyphs/text-size.glif/0
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| 596 |
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|
cascadia-code/sources/nerdfonts/full/processed/font-logos.ufo/glyphs/A_rduino.glif/0
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| 597 |
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|
cascadia-code/sources/nerdfonts/full/processed/font-logos.ufo/glyphs/L_inux M_int _inverse_.glif/0
|
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| 598 |
<?xml version='1.0' encoding='UTF-8'?>
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Snappy
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cascadia-code/sources/nerdfonts/full/processed/font-logos.ufo/glyphs/S_nappy.glif/0
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| 599 |
<jupyter_start><jupyter_text>An example notebook showing how to train a DECI model on [CSuite](https://github.com/microsoft/csuite) Data.This demonstrates how to assemble the various components of the library and how to perform training.<jupyter_code>import os
from dataclasses import dataclass
import networkx as nx
import pytorch_lightning as pl
import torch
from tensordict import TensorDict
from torch.utils.data import DataLoader
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data
from causica.datasets.tensordict_utils import tensordict_shapes
from causica.distributions import (
AdjacencyDistribution,
ContinuousNoiseDist,
DistributionModule,
ENCOAdjacencyDistributionModule,
GibbsDAGPrior,
JointNoiseModule,
create_noise_modules,
)
from causica.functional_relationships import DECIEmbedFunctionalRelationships
from causica.graph.dag_constraint import calculate_dagness
from causica.sem.sem_distribution import SEMDistributionModule
from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig<jupyter_output><empty_output><jupyter_text>Define various parameters of the training process.<jupyter_code>@dataclass(frozen=True)
class TrainingConfig:
noise_dist: ContinuousNoiseDist = ContinuousNoiseDist.SPLINE
batch_size: int = 128
max_epoch: int = int(os.environ.get("TEST_RUN", 2000)) # used by testing to run the notebook as a script
gumbel_temp: float = 0.25
averaging_period: int = 10
prior_sparsity_lambda: float = 5.0
init_rho: float = 1.0
init_alpha: float = 0.0
training_config = TrainingConfig()
auglag_config = AugLagLRConfig()
seed = 1
dataset = "csuite_nonlin_simpson"
pl.seed_everything(seed) # set the random seed
device = "cuda" if torch.cuda.is_available() else "cpu"<jupyter_output>Global seed set to 1<jupyter_text>We now download the dataset from Azure Blob storage.The Causica repo makes heavy use of [TensorDict](https://pytorch.org/rl/tensordict/reference/generated/tensordict.TensorDict.html). A dataset is assumed to be a TensorDict, with each key containing the data associated with a given node.The `variables_metadata` object stored with the CSuite data also describes the type of each node, which we will need when we create the Noise Distributions for each node.<jupyter_code>root_path = os.path.join(CAUSICA_DATASETS_PATH, dataset)
variables_metadata = load_data(root_path, DataEnum.VARIABLES_JSON)
dataset_train: TensorDict = load_data(root_path, DataEnum.TRAIN, variables_metadata)
# move the entire dataset to the device, since it is small
dataset_train = dataset_train.apply(lambda t: t.to(dtype=torch.float32, device=device))
dataloader_train = DataLoader(
dataset=dataset_train,
collate_fn=lambda x: x,
batch_size=training_config.batch_size,
shuffle=True,
drop_last=False,
)<jupyter_output><empty_output><jupyter_text>Create a prior distribution over DAGs.<jupyter_code>num_nodes = len(dataset_train.keys())
prior = GibbsDAGPrior(num_nodes=num_nodes, sparsity_lambda=training_config.prior_sparsity_lambda)<jupyter_output><empty_output><jupyter_text>Create the Variational Posterior Distribution over Adjacency Matrices, which we will optimize.<jupyter_code>adjacency_dist: DistributionModule[AdjacencyDistribution] = ENCOAdjacencyDistributionModule(num_nodes)<jupyter_output><empty_output><jupyter_text>Create the Graph Neural network that will estimate the functional relationships. More info can be found [here](https://openreview.net/forum?id=S2pNPZM-w-f).<jupyter_code>functional_relationships = DECIEmbedFunctionalRelationships(
shapes=tensordict_shapes(dataset_train),
embedding_size=32,
out_dim_g=32,
num_layers_g=2,
num_layers_zeta=2,
)<jupyter_output><empty_output><jupyter_text>Create the Noise Distributions for each node using the definitions in `variables_metadata`.<jupyter_code>variable_shapes = tensordict_shapes(dataset_train)
types_dict = {var.group_name: var.type for var in variables_metadata.variables}
noise_submodules = create_noise_modules(variable_shapes, types_dict, training_config.noise_dist)
noise_module = JointNoiseModule(noise_submodules)<jupyter_output><empty_output><jupyter_text>Create the SEM Module which combines the variational adjacency distribution, the functional relationships and the noise distributions for each node.<jupyter_code>sem_module: SEMDistributionModule = SEMDistributionModule(adjacency_dist, functional_relationships, noise_module)
sem_module.to(device);<jupyter_output><empty_output><jupyter_text>Create the optimizer, with separate learning rates for each module.<jupyter_code>modules = {
"functional_relationships": sem_module.functional_relationships,
"vardist": sem_module.adjacency_module,
"noise_dist": sem_module.noise_module,
}
parameter_list = [
{"params": module.parameters(), "lr": auglag_config.lr_init_dict[name], "name": name}
for name, module in modules.items()
]
optimizer = torch.optim.Adam(parameter_list)<jupyter_output><empty_output><jupyter_text>Define the [Augmented Lagrangian Scheduler](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method).This allows DECI to optimize towards a DAG, by slowly increasing the alpha and rho parameters as the optimization takes place<jupyter_code>scheduler = AugLagLR(config=auglag_config)
auglag_loss = AugLagLossCalculator(init_alpha=training_config.init_alpha, init_rho=training_config.init_rho)<jupyter_output><empty_output><jupyter_text>The main training loop.For each batch, we:* Sample a graph from the SEM.* Calculate the log probability of that batch, given the graph.* Create the ELBO to be optimized.* Calculate the DAG constraint* Combine the DAG constraint with the ELBO to get the loss.<jupyter_code>assert len(dataset_train.batch_size) == 1, "Only 1D batch size is supported"
num_samples = len(dataset_train)
for epoch in range(training_config.max_epoch):
for i, batch in enumerate(dataloader_train):
optimizer.zero_grad()
sem_distribution = sem_module()
sem, *_ = sem_distribution.relaxed_sample(
torch.Size([]), temperature=training_config.gumbel_temp
) # soft sample
batch_log_prob = sem.log_prob(batch).mean()
sem_distribution_entropy = sem_distribution.entropy()
prior_term = prior.log_prob(sem.graph)
objective = (-sem_distribution_entropy - prior_term) / num_samples - batch_log_prob
constraint = calculate_dagness(sem.graph)
loss = auglag_loss(objective, constraint / num_samples)
loss.backward()
optimizer.step()
# update the Auglag parameters
scheduler.step(
optimizer=optimizer,
loss=auglag_loss,
loss_value=loss,
lagrangian_penalty=constraint,
)
# log metrics
if epoch % 10 == 0 and i == 0:
print(
f"epoch:{epoch} loss:{loss.item():.5g} nll:{-batch_log_prob.detach().cpu().numpy():.5g} "
f"dagness:{constraint.item():.5f} num_edges:{(sem.graph > 0.0).sum()} "
f"alpha:{auglag_loss.alpha:.5g} rho:{auglag_loss.rho:.5g} "
f"step:{scheduler.outer_opt_counter}|{scheduler.step_counter} "
f"num_lr_updates:{scheduler.num_lr_updates}"
)<jupyter_output>epoch:0 loss:7.0666 nll:7.0575 dagness:1.08616 num_edges:5 alpha:0 rho:1 step:0|1 num_lr_updates:0
epoch:10 loss:1.8691 nll:1.8574 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|161 num_lr_updates:0
epoch:20 loss:1.1163 nll:1.102 dagness:1.63362 num_edges:6 alpha:0 rho:1 step:0|321 num_lr_updates:0
epoch:30 loss:1.1561 nll:1.149 dagness:0.00000 num_edges:3 alpha:0 rho:1 step:0|481 num_lr_updates:0
epoch:40 loss:1.2811 nll:1.2715 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|641 num_lr_updates:0
epoch:50 loss:1.4415 nll:1.4294 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|801 num_lr_updates:0
epoch:60 loss:1.08 nll:1.0678 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|961 num_lr_updates:0
epoch:70 loss:1.0654 nll:1.0557 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1121 num_lr_updates:0
epoch:80 loss:1.1433 nll:1.1336 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1281 num_lr_updates:0
epoch:90 loss:1.2957 nll:1.2859 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1441 n[...]<jupyter_text>Download and display the true graph from the CSuite Dataset<jupyter_code>true_adj_matrix = load_data(root_path, DataEnum.TRUE_ADJACENCY)
true_graph = nx.from_numpy_array(true_adj_matrix.cpu().numpy(), create_using=nx.DiGraph)
nx.draw_networkx(true_graph, pos=nx.planar_layout(true_graph), with_labels=True, arrows=True)<jupyter_output><empty_output><jupyter_text>Display the graph learned by DECI<jupyter_code>vardist = adjacency_dist()
graph = nx.from_numpy_array(vardist.mode.cpu().numpy(), create_using=nx.DiGraph)
nx.draw_networkx(graph, pos=nx.planar_layout(true_graph), with_labels=True, arrows=True)<jupyter_output><empty_output>
|
causica/examples/csuite_example.ipynb/0
|
{
"file_path": "causica/examples/csuite_example.ipynb",
"repo_id": "causica",
"token_count": 3431
}
| 600 |
from typing import Optional
import torch
import torch.distributions as td
import torch.nn.functional as F
from torch import nn
from torch.distributions.utils import logits_to_probs
from causica.distributions.adjacency.adjacency_distributions import AdjacencyDistribution
from causica.distributions.distribution_module import DistributionModule
from causica.distributions.gumbel_binary import gumbel_softmax_binary
class BernoulliDigraphDistribution(AdjacencyDistribution):
"""Directed graph distribution, with independent Bernoulli distributions for each edge. Allows for self-loops.
The probability of an edge existing is pᵢⱼ = σ(γᵢⱼ). where γᵢⱼ is the logit the edge exists and is independent for
each i, j.
"""
arg_constraints = {}
def __init__(self, logits: torch.Tensor, validate_args: Optional[bool] = None):
num_nodes = logits.shape[-1]
super().__init__(num_nodes, validate_args=validate_args)
self.logits = logits
@staticmethod
def base_dist(logits: torch.Tensor) -> td.Distribution:
"""A matrix of independent Bernoulli distributions.
Args:
logits: The logits for the existence of an edge; shape == batch_shape + (num_nodes, num_nodes)
"""
return td.Independent(td.Bernoulli(logits=logits), 2)
def relaxed_sample(self, sample_shape: torch.Size = torch.Size(), temperature: float = 0.0) -> torch.Tensor:
"""
Sample from a relaxed distribution. We use a Gumbel Softmax.
Args:
sample_shape: the shape of the samples to return
Returns:
A tensor of shape sample_shape + batch_shape + (num_nodes, num_nodes)
"""
expanded_logits = self.logits.expand(*(sample_shape + self.logits.shape))
return gumbel_softmax_binary(logits=expanded_logits, tau=temperature, hard=True)
def sample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
"""
Sample from the underyling independent Bernoulli distribution.
Gradients will not flow through this method, use relaxed_sample instead.
Args:
sample_shape: the shape of the samples to return
Returns:
A tensor of shape sample_shape + batch_shape + (num_nodes, num_nodes)
"""
return self.base_dist(self.logits).sample(sample_shape=sample_shape)
def entropy(self) -> torch.Tensor:
"""
Get the entropy of the independent Bernoulli distribution.
Returns:
A tensor of shape batch_shape, with the entropy of the distribution
"""
entropy = F.binary_cross_entropy_with_logits(
self.logits, logits_to_probs(self.logits, is_binary=True), reduction="none"
)
return torch.sum(entropy, dim=(-2, -1))
@property
def mean(self) -> torch.Tensor:
"""
Return the mean of the underlying independent Bernoulli distribution.
This will be a matrix with all entries in the interval [0, 1].
Returns:
A tensor of shape batch_shape + (num_nodes, num_nodes)
"""
return self.base_dist(self.logits).mean
@property
def mode(self) -> torch.Tensor:
"""
Return the mode of the underlying independent Bernoulli distribution.
This will be an adjacency matrix.
Returns:
A tensor of shape batch_shape + (num_nodes, num_nodes)
"""
# bernoulli mode can be nan for very small logits, favour sparseness and set to 0
return torch.nan_to_num(self.base_dist(self.logits).mode, nan=0.0)
def log_prob(self, value: torch.Tensor) -> torch.Tensor:
"""
Get the log probability of each tensor from the sample space
Args:
value: a binary matrix of shape value_shape + batch_shape + (num_nodes, num_nodes)
Returns:
A tensor of shape value_shape + batch_shape, with the log probabilities of each tensor in the batch.
"""
return self.base_dist(self.logits).log_prob(value)
class BernoulliDigraphDistributionModule(DistributionModule[BernoulliDigraphDistribution]):
"""Represents an `BernoulliDigraphDistribution` distribution with learnable logits."""
def __init__(self, num_nodes: int) -> None:
super().__init__()
self.logits = nn.Parameter(torch.zeros(num_nodes, num_nodes), requires_grad=True) # prob 0.5
def forward(self) -> BernoulliDigraphDistribution:
return BernoulliDigraphDistribution(logits=self.logits)
|
causica/research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py/0
|
{
"file_path": "causica/research_experiments/scotch/src/scotch/latent_learning/graph_distribution.py",
"repo_id": "causica",
"token_count": 1766
}
| 601 |
from enum import Enum
import torch
class VariableTypeEnum(Enum):
CONTINUOUS = "continuous"
CATEGORICAL = "categorical"
BINARY = "binary"
DTYPE_MAP = {
VariableTypeEnum.CONTINUOUS: torch.float32,
VariableTypeEnum.CATEGORICAL: torch.int32,
VariableTypeEnum.BINARY: torch.int32,
}
|
causica/src/causica/datasets/variable_types.py/0
|
{
"file_path": "causica/src/causica/datasets/variable_types.py",
"repo_id": "causica",
"token_count": 126
}
| 602 |
import abc
from typing import Generic, Optional, TypeVar
import torch
import torch.distributions as td
from causica.distributions.distribution_module import DistributionModule
SampleType = TypeVar("SampleType")
class Noise(Generic[SampleType], abc.ABC, td.Distribution):
"""
Extend Distributions to allow the noise (usually unparametrized) to be extracted from samples and vice versa.
This class is used for Counterfactuals, where we want to keep the noise fixed but fix some values of the SEM.
"""
@abc.abstractmethod
def sample_to_noise(self, samples: SampleType) -> SampleType:
"""
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
"""
@abc.abstractmethod
def noise_to_sample(self, noise: SampleType) -> SampleType:
"""
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
"""
BaseNoiseType_co = TypeVar("BaseNoiseType_co", bound=Noise, covariant=True)
class IndependentNoise(Generic[BaseNoiseType_co], td.Independent, Noise[torch.Tensor]):
"""Like `td.Idenpendent` but also forwards `Noise` specific methods."""
base_dist: BaseNoiseType_co
def __init__(
self,
base_distribution: BaseNoiseType_co,
reinterpreted_batch_ndims: int,
validate_args: Optional[bool] = None,
):
super().__init__(
base_distribution=base_distribution,
reinterpreted_batch_ndims=reinterpreted_batch_ndims,
validate_args=validate_args,
)
def sample_to_noise(self, samples: torch.Tensor) -> torch.Tensor:
"""
Transform from the sample observations to corresponding noise variables.
This just passes through to the underlying distribution.
Args:
samples: Tensor of shape sample_shape + batch_shape + event_shape
Returns:
The generated samples with shape sample_shape + batch_shape + event_shape
"""
return self.base_dist.sample_to_noise(samples)
def noise_to_sample(self, noise: torch.Tensor) -> torch.Tensor:
"""
Generate samples using the given exogenous noise.
This just passes through to the underlying distribution.
Args:
noise: noise variable with shape sample_shape + batch_shape.
Returns:
The generated samples with shape sample_shape + batch_shape + event_shape
"""
return self.base_dist.noise_to_sample(noise)
NoiseType_co = TypeVar("NoiseType_co", bound=Noise, covariant=True)
class NoiseModule(abc.ABC, Generic[NoiseType_co], DistributionModule[NoiseType_co]):
"""Module for producing `Noise`, where sampling from the distribution corresponds to adding noise to the input.
All subclasses must allow sampling without input, which should correspond as closely as possible to adding the noise
to a compatible zero vector.
"""
@abc.abstractmethod
def forward(self, x=None) -> NoiseType_co:
pass
|
causica/src/causica/distributions/noise/noise.py/0
|
{
"file_path": "causica/src/causica/distributions/noise/noise.py",
"repo_id": "causica",
"token_count": 1227
}
| 603 |
from typing import Optional
import torch
from tensordict import TensorDict
from causica.functional_relationships.functional_relationships import FunctionalRelationships
class LinearFunctionalRelationships(FunctionalRelationships):
"""
A simple linear functional relationship.
"""
def __init__(
self,
shapes: dict[str, torch.Size],
initial_linear_coefficient_matrix: torch.Tensor,
initial_bias: Optional[torch.Tensor] = None,
trainable: bool = False,
) -> None:
"""
Args:
shapes: Dict of node shapes (how many dimensions a variable has)
Order corresponds to the order in graph(s).
initial_linear_coefficient_matrix: the linear coefficients [output_shape, output_shape]
initial_bias: Optional, None or a tensor containing the bias [output_shape,]
trainable: whether the coefficient matrix should be learnable
"""
super().__init__(shapes=shapes)
shape = self.tensor_to_td.output_shape
assert initial_linear_coefficient_matrix.shape == (shape, shape)
if initial_bias is not None:
assert initial_bias.shape[0] == shape
else:
initial_bias = torch.zeros(shape)
self.bias = torch.nn.Parameter(initial_bias, requires_grad=trainable)
self.linear_coefficients = torch.nn.Parameter(initial_linear_coefficient_matrix, requires_grad=trainable)
def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
"""
Args:
samples: Batched inputs, size batch_size_x + (concatenated_shape).
graphs: Weighted adjacency matrix, size batch_size_g + (n, n)
Returns:
A Dict of tensors of shape batch_shape_x + batch_shape_g + (concatenated_shape)
"""
return self.tensor_to_td(self.linear_map(self.tensor_to_td.inv(samples), graphs))
def linear_map(self, samples: torch.Tensor, graph: torch.Tensor) -> torch.Tensor:
"""
Applies the linear function to a concatenated tensor of samples.
Args:
samples: tensor of shape batch_shape_x + batch_shape_f + batch_shape_g + [n_cols]
graph: tensor of shape batch_shape_g + [n_nodes, n_nodes]
Returns:
tensor of shape batch_shape_x + batch_shape_f + batch_shape_g + [n_cols]
"""
batch_shape_samples = samples.shape[:-1]
batch_shape_g = graph.shape[:-2]
batch_shape_x_f = batch_shape_samples[: -len(batch_shape_g)]
if len(batch_shape_g) > 0 and batch_shape_samples[-len(batch_shape_g) :] != batch_shape_g:
raise ValueError(
f"Batch shape of samples and graph must match but got {batch_shape_samples} and {batch_shape_g}"
)
masked_graph = torch.einsum("ji,...jk,kl->...il", self.stacked_key_masks, graph, self.stacked_key_masks)
graph_broad = masked_graph.expand(*(batch_shape_x_f + (-1,) * len(masked_graph.shape)))
return self.bias + torch.einsum("...i,...ij->...j", samples, graph_broad * self.linear_coefficients)
|
causica/src/causica/functional_relationships/linear_functional_relationships.py/0
|
{
"file_path": "causica/src/causica/functional_relationships/linear_functional_relationships.py",
"repo_id": "causica",
"token_count": 1314
}
| 604 |
import logging
from dataclasses import asdict, is_dataclass
from typing import Any, Mapping, Optional, Sequence, Union
import fsspec
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.utilities.types import STEP_OUTPUT
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import CounterfactualWithEffects, InterventionWithEffects
from causica.distributions import (
AdjacencyDistribution,
ConstrainedAdjacency,
DistributionModule,
ENCOAdjacencyDistributionModule,
ExpertGraphContainer,
GibbsDAGPrior,
JointNoiseModule,
create_noise_modules,
)
from causica.distributions.noise.joint import ContinuousNoiseDist
from causica.functional_relationships import DECIEmbedFunctionalRelationships
from causica.graph.dag_constraint import calculate_dagness
from causica.graph.evaluation_metrics import adjacency_f1, orientation_f1
from causica.lightning.callbacks import AuglagLRCallback
from causica.lightning.data_modules.deci_data_module import DECIDataModule
from causica.lightning.modules.variable_spec_module import VariableSpecModule
from causica.sem.sem_distribution import SEMDistributionModule
from causica.sem.structural_equation_model import SEM
from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig
from causica.training.evaluation import (
eval_ate_rmse,
eval_intervention_likelihoods,
eval_ite_rmse,
list_logsumexp,
list_mean,
)
logging.basicConfig(level=logging.INFO)
NUM_GRAPH_SAMPLES = 100
NUM_ATE_ITE_SEMS = 10
class DECIModule(VariableSpecModule):
"""A PyTorch Lightning Module for running a DECI model."""
def __init__(
self,
noise_dist: ContinuousNoiseDist = ContinuousNoiseDist.SPLINE,
embedding_size: int = 32,
out_dim_g: int = 32,
num_layers_g: int = 2,
num_layers_zeta: int = 2,
init_alpha: float = 0.0,
init_rho: float = 1.0,
prior_sparsity_lambda: float = 0.05,
gumbel_temp: float = 0.25,
auglag_config: Optional[AugLagLRConfig] = None,
expert_graph_container: Optional[ExpertGraphContainer] = None,
constraint_matrix_path: Optional[str] = None,
disable_auglag_epochs: Optional[int] = None,
test_metric_prefix: Optional[str] = "eval",
):
"""DECI Module
Args:
noise_dist: The type of the noise distribution to use. The noise type will be used for each variable in the
SEM.
embedding_size: The size of the embedding used in the functional relationships. This specifies the
flexibility of the trainable embeddings for each node.
out_dim_g: The size of the output of the functional relationships encoder g.
num_layers_g: The number of layers in the functional relationships encoder g.
num_layers_zeta: The number of layers in the decoder zeta.
init_alpha: The initial value of the augmented lagrangian parameter alpha. The augmented lagrangian is
\rho h(G)^2 + \alpha h(G), where h(G) is the DAG constraint.
init_rho: The initial value of the augmented lagrangian parameter rho.
prior_sparsity_lambda: The sparsity coefficient in the graph prior. Larger value prefers sparser graphs.
gumbel_temp: The temperature used in the gumbel softmax relaxation.
auglag_config: The configuration for the augmented lagrangian scheduler.
expert_graph_container: The expert graph container to use. It contains the prior knowledge graph that
the inferred graph should be close to.
constraint_matrix_path: The path to the constraint matrix. The constraint matrix specifies the edge
directions that are prohibited or required.
disable_auglag_epochs: The number of epochs to disable the augmented lagrangian for.
test_metric_prefix: Prefix used for logging. The logged metric name will be
{test_metric_prefix}/{metric_name}.
"""
super().__init__()
self.auglag_config = auglag_config if auglag_config is not None else AugLagLRConfig()
self.disable_auglag_epochs = disable_auglag_epochs
self.lr_scheduler = AugLagLR(config=self.auglag_config)
self.constraint_matrix_path = constraint_matrix_path
self.constraint_matrix: Optional[torch.Tensor] = None
self.embedding_size = embedding_size
self.out_dim_g = out_dim_g
self.num_layers_g = num_layers_g
self.num_layers_zeta = num_layers_zeta
self.expert_graph_container: Optional[ExpertGraphContainer] = expert_graph_container
self.gumbel_temp = gumbel_temp
self.is_setup = False
self.noise_dist = noise_dist
self.prior_sparsity_lambda = prior_sparsity_lambda
self.test_metric_prefix = test_metric_prefix
self.auglag_loss: AugLagLossCalculator = AugLagLossCalculator(init_alpha=init_alpha, init_rho=init_rho)
# Inferred once the datamodule is available using `self.infer_missing_state_from_dataset()`
self.num_samples = None
self.variable_group_shapes = None
self.variable_types = None
self.save_hyperparameters(logger=False)
def on_train_start(self) -> None:
super().on_train_start()
# Log a version of hparams that are easier to handle for loggers, by converting dataclasses to dicts
if self.logger is not None:
hyperparams = {
k: asdict(v) if is_dataclass(v) else v
for k, v in self.hparams.items()
if not isinstance(v, torch.nn.Module)
}
self.logger.log_hyperparams(hyperparams)
def prepare_data(self) -> None:
"""Set the constraint matrix (if necessary)."""
if self.constraint_matrix_path:
with fsspec.open(self.constraint_matrix_path) as f:
if self.constraint_matrix_path.endswith("npy"):
constraint_matrix = np.load(f)
else:
raise ValueError(f"Unsupported constraint matrix format: {self.constraint_matrix_path}")
self.constraint_matrix = torch.tensor(constraint_matrix)
return super().prepare_data()
def infer_missing_state_from_dataset(self):
dataset_defined_members = [
self.num_samples,
self.variable_group_shapes,
self.variable_types,
]
if any(member is None for member in dataset_defined_members):
datamodule = getattr(self.trainer, "datamodule", None)
if not isinstance(datamodule, DECIDataModule):
raise TypeError(
f"Incompatible data module {datamodule}, requires a DECIDataModule but is "
f"{type(datamodule).mro()}"
)
if self.num_samples is None:
self.num_samples = len(datamodule.dataset_train)
if self.variable_group_shapes is None:
self.variable_group_shapes = datamodule.variable_shapes
if self.variable_types is None:
self.variable_types = datamodule.variable_types
def setup(self, stage: Optional[str] = None):
_ = stage
if self.is_setup:
return # Already setup
self.infer_missing_state_from_dataset()
assert self.num_samples is not None
assert self.variable_group_shapes is not None
assert self.variable_types is not None
num_nodes = len(self.variable_group_shapes)
adjacency_dist: DistributionModule[AdjacencyDistribution] = ENCOAdjacencyDistributionModule(num_nodes)
if self.constraint_matrix is not None:
adjacency_dist = ConstrainedAdjacency(adjacency_dist, self.constraint_matrix)
functional_relationships = DECIEmbedFunctionalRelationships(
shapes=self.variable_group_shapes,
embedding_size=self.embedding_size,
out_dim_g=self.out_dim_g,
num_layers_g=self.num_layers_g,
num_layers_zeta=self.num_layers_zeta,
)
noise_submodules = create_noise_modules(self.variable_group_shapes, self.variable_types, self.noise_dist)
noise_module = JointNoiseModule(noise_submodules)
self.sem_module: SEMDistributionModule = SEMDistributionModule(
adjacency_dist, functional_relationships, noise_module
)
self.prior: GibbsDAGPrior = GibbsDAGPrior(
num_nodes=num_nodes,
sparsity_lambda=self.prior_sparsity_lambda,
expert_graph_container=self.expert_graph_container,
)
self.is_setup = True
def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
_ = kwargs
batch, *_ = args
batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True))
sem_distribution = self.sem_module()
sem, *_ = sem_distribution.relaxed_sample(torch.Size([]), temperature=self.gumbel_temp) # soft sample
batch_log_prob = sem.log_prob(batch).mean()
sem_distribution_entropy = sem_distribution.entropy()
prior_term = self.prior.log_prob(sem.graph)
objective = (-sem_distribution_entropy - prior_term) / self.num_samples - batch_log_prob
constraint = calculate_dagness(sem.graph)
step_output = {
"alpha": self.auglag_loss.alpha,
"rho": self.auglag_loss.rho,
"loss": self.auglag_loss(objective, constraint / self.num_samples),
"batch_log_prob": batch_log_prob,
"constraint": constraint,
"num_edges": (sem.graph > 0.0).count_nonzero(),
"vardist_entropy": sem_distribution_entropy,
"prior_term": prior_term,
}
self.log_dict(step_output, on_epoch=True) # Only log this on epoch end
return step_output
def configure_optimizers(self):
"""Set the learning rates for different sets of parameters."""
modules = {
"functional_relationships": self.sem_module.functional_relationships,
"vardist": self.sem_module.adjacency_module,
"noise_dist": self.sem_module.noise_module,
}
parameter_list = [
{
"params": module.parameters(),
"lr": self.auglag_config.lr_init_dict[name],
"name": name,
}
for name, module in modules.items()
]
# Check that all modules are added to the parameter list
check_modules = set(modules.values())
for module in self.parameters(recurse=False):
assert module in check_modules, f"Module {module} not in module list"
return torch.optim.Adam(parameter_list)
def configure_callbacks(self) -> Union[Sequence[pl.Callback], pl.Callback]:
"""Create a callback for the auglag callback."""
disabled_epochs = set(range(self.disable_auglag_epochs)) if self.disable_auglag_epochs else None
return [AuglagLRCallback(self.lr_scheduler, log_auglag=True, disabled_epochs=disabled_epochs)]
def test_step_observational(self, batch: TensorDict, *args, **kwargs):
"""Evaluate the log prob of the model on the test set using multiple graph samples."""
_, _ = args, kwargs
batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True))
sems = self.sem_module().sample(torch.Size([NUM_GRAPH_SAMPLES]))
dataset_size = self.trainer.datamodule.dataset_test.batch_size # type: ignore
assert len(dataset_size) == 1, "Only one batch size is supported"
# Estimate log prob for each sample using graph samples and report the mean over graphs
log_prob_test = list_logsumexp([sem.log_prob(batch) for sem in sems]) - np.log(NUM_GRAPH_SAMPLES)
self.log(
f"{self.test_metric_prefix}/test_LL",
torch.sum(log_prob_test, dim=-1).item() / dataset_size[0],
reduce_fx=sum,
add_dataloader_idx=False,
)
def test_step_graph(self, true_adj_matrix: torch.Tensor, *args, **kwargs):
"""Evaluate the graph reconstruction performance of the model against the true graph."""
_, _ = args, kwargs
sem_samples = self.sem_module().sample(torch.Size([NUM_GRAPH_SAMPLES]))
graph_samples = [sem.graph for sem in sem_samples]
adj_f1 = list_mean([adjacency_f1(true_adj_matrix, graph) for graph in graph_samples]).item()
orient_f1 = list_mean([orientation_f1(true_adj_matrix, graph) for graph in graph_samples]).item()
self.log(f"{self.test_metric_prefix}/adjacency.f1", adj_f1, add_dataloader_idx=False)
self.log(f"{self.test_metric_prefix}/orientation.f1", orient_f1, add_dataloader_idx=False)
def test_step_interventions(self, interventions: InterventionWithEffects, *args, **kwargs):
"""Evaluate the ATE and Interventional log prob performance of the model"""
_, _ = args, kwargs
sems_list: list[SEM] = list(self.sem_module().sample(torch.Size([NUM_ATE_ITE_SEMS])))
interventional_log_prob = eval_intervention_likelihoods(sems_list, interventions)
self.log(
f"{self.test_metric_prefix}/Interventional_LL",
torch.mean(interventional_log_prob).item(),
add_dataloader_idx=False,
)
mean_ate_rmse = eval_ate_rmse(sems_list, interventions)
self.log(
f"{self.test_metric_prefix}/ATE_RMSE",
list_mean(list(mean_ate_rmse.values())).item(),
add_dataloader_idx=False,
)
def test_step_counterfactuals(self, counterfactuals: CounterfactualWithEffects, *args, **kwargs):
"""Evaluate the ITE performance of the model"""
_, _ = args, kwargs
sems_list = list(self.sem_module().sample(torch.Size([NUM_ATE_ITE_SEMS])))
ite_rmse = eval_ite_rmse(sems_list, counterfactuals)
self.log(
f"{self.test_metric_prefix}/ITE_RMSE",
list_mean(list(ite_rmse.values())).item(),
add_dataloader_idx=False,
)
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
# initialise all the parameters, we can
super().load_state_dict(state_dict, strict=False)
# setup the model
self.setup()
# load the state dict again to fill in the parameters
return super().load_state_dict(state_dict, strict=strict)
def set_extra_state(self, state: Any):
self.variable_group_shapes = state["shapes"]
self.variable_types = state["types"]
self.constraint_matrix = state["constraint_matrix"]
self.num_samples = state["num_samples"]
def get_extra_state(self) -> Any:
return {
"shapes": self.variable_group_shapes,
"types": self.variable_types,
"constraint_matrix": self.constraint_matrix,
"num_samples": self.num_samples,
}
|
causica/src/causica/lightning/modules/deci_module.py/0
|
{
"file_path": "causica/src/causica/lightning/modules/deci_module.py",
"repo_id": "causica",
"token_count": 6493
}
| 605 |
import os
import tempfile
import numpy as np
import pytest
import torch
import torch.distributions as td
from tensordict import TensorDict
from causica.data_generation.generate_data import (
generate_sem_sampler,
get_variable_definitions,
plot_dataset,
sample_counterfactual,
sample_dataset,
sample_intervention,
sample_treatment_and_effect,
)
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.causica_dataset_format import DataEnum, Variable, VariablesMetadata, save_data
from causica.distributions import ErdosRenyiDAGDistribution
try:
import seaborn as sns
except ImportError:
sns = None
@pytest.fixture(name="five_variable_one_dim_metadata")
def fixture_five_variable_one_dim_metadata() -> VariablesMetadata:
return VariablesMetadata([Variable(f"x_{i}", f"x_{i}") for i in range(5)])
@pytest.fixture(name="five_variable_two_dim_metadata")
def fixture_five_variable_two_dim_metadata() -> VariablesMetadata:
return VariablesMetadata([Variable(f"x_{i // 2}", f"x_{i}") for i in range(10)])
@pytest.fixture(name="five_node_graph")
def fixture_five_node_graph() -> torch.Tensor:
graph = torch.zeros(5, 5)
graph[0, 1] = 1
graph[1, 2] = 1
graph[2, 3] = 1
return graph
@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_sample_interventions(mixture_sem_sampler_and_shapes: tuple[SEMSampler, dict[str, torch.Size]]):
mixture_sem_sampler, shapes_dict = mixture_sem_sampler_and_shapes
sem = mixture_sem_sampler.sample()
noise = sem.sample_noise(torch.Size([5]))
observations = sem.noise_to_sample(noise)
num_intervention_samples = 3
intervention = sample_intervention(sem, observations, num_intervention_samples, "x_1")
for v, shape in shapes_dict.items():
assert intervention.intervention_data[v].shape == torch.Size([num_intervention_samples]) + shape
# test ordering of intervention data
assert list(intervention.intervention_data.keys()) == list(shapes_dict.keys())
assert all(val.ndim == 1 for val in intervention.intervention_values.values())
assert intervention.intervention_values["x_1"].shape == shapes_dict["x_1"]
assert all(intervention.intervention_values["x_1"] >= observations["x_1"].min())
assert all(intervention.intervention_values["x_1"] <= observations["x_1"].max())
def test_sample_counterfactuals(mixture_sem_sampler_and_shapes: tuple[SEMSampler, dict[str, torch.Size]]):
mixture_sem_sampler, shapes_dict = mixture_sem_sampler_and_shapes
sem = mixture_sem_sampler.sample()
num_samples = 5
noise = sem.sample_noise(torch.Size([num_samples]))
observations = sem.noise_to_sample(noise)
counterfactual = sample_counterfactual(sem, observations, noise, "x_1")
for v, shape in shapes_dict.items():
assert counterfactual.counterfactual_data[v].shape == torch.Size([num_samples]) + shape
# test ordering of intervention data
assert list(counterfactual.counterfactual_data.keys()) == ["x_0", "x_1", "x_2", "x_3", "x_4"]
assert all(val.ndim == 1 for val in counterfactual.intervention_values.values())
assert counterfactual.intervention_values["x_1"].shape == shapes_dict["x_1"]
assert all(counterfactual.intervention_values["x_1"] >= observations["x_1"].min())
assert all(counterfactual.intervention_values["x_1"] <= observations["x_1"].max())
torch.testing.assert_close(counterfactual.factual_data, observations)
torch.testing.assert_close(
sem.do(counterfactual.intervention_values).noise_to_sample(noise), counterfactual.counterfactual_data
)
def test_generate_sem_er(five_variable_one_dim_metadata: VariablesMetadata):
variables = get_variable_definitions(num_nodes=5)
sem = generate_sem_sampler(
variables=variables, graph_file=None, num_edges=5, graph_type="er", function_type="linear"
).sample()
assert len(variables.variables) == 5
assert variables == five_variable_one_dim_metadata
samples = sem.sample(torch.Size([5]))
assert samples["x_0"].shape == torch.Size([5, 1])
assert samples["x_1"].shape == torch.Size([5, 1])
assert samples["x_2"].shape == torch.Size([5, 1])
assert samples["x_3"].shape == torch.Size([5, 1])
assert samples["x_4"].shape == torch.Size([5, 1])
def test_generate_sem_variable_groups(five_variable_two_dim_metadata: VariablesMetadata):
tmpdir = tempfile.mkdtemp()
save_data(tmpdir, five_variable_two_dim_metadata, DataEnum.VARIABLES_JSON)
variable_json_path = os.path.join(tmpdir, DataEnum.VARIABLES_JSON.value)
variables = get_variable_definitions(variable_json_path=variable_json_path)
sem = generate_sem_sampler(
variables=variables,
graph_file=None,
num_edges=5,
graph_type="er",
function_type="linear",
).sample()
assert len(variables.variables) == 10
assert len(sem.node_names) == 5
samples = sem.sample(torch.Size([5]))
assert samples["x_0"].shape == torch.Size([5, 2])
assert samples["x_1"].shape == torch.Size([5, 2])
assert samples["x_2"].shape == torch.Size([5, 2])
assert samples["x_3"].shape == torch.Size([5, 2])
assert samples["x_4"].shape == torch.Size([5, 2])
def test_generate_sem_numpy(five_node_graph: torch.Tensor, five_variable_one_dim_metadata: VariablesMetadata):
tmpfile = tempfile.mkstemp(suffix=".npy")[1]
np.save(tmpfile, five_node_graph.numpy())
assert os.path.exists(tmpfile)
variables = get_variable_definitions(num_nodes=5)
sem = generate_sem_sampler(
variables=variables,
graph_file=tmpfile,
num_edges=None,
graph_type="numpy",
function_type="linear",
).sample()
assert len(variables.variables) == 5
assert variables == five_variable_one_dim_metadata
samples = sem.sample(torch.Size([5]))
assert samples["x_0"].shape == torch.Size([5, 1])
assert samples["x_1"].shape == torch.Size([5, 1])
assert samples["x_2"].shape == torch.Size([5, 1])
assert samples["x_3"].shape == torch.Size([5, 1])
assert samples["x_4"].shape == torch.Size([5, 1])
torch.testing.assert_close(sem.graph, five_node_graph)
def test_generate_sem_er_with_probs(five_variable_one_dim_metadata: VariablesMetadata):
num_nodes = 5
num_sem_samples = 1000
probs = 0.5
expected_num_edges = num_nodes * (num_nodes - 1) / 2 * probs
variables = get_variable_definitions(num_nodes=num_nodes)
sem_sampler = generate_sem_sampler(
variables=variables,
graph_file=None,
num_edges=None,
probs=probs,
graph_type="er",
function_type="linear",
)
num_edges_list = []
for _ in range(num_sem_samples):
num_edges_list.append(sem_sampler.sample().graph.sum().item())
avg_num_edges = sum(num_edges_list) / num_sem_samples
assert len(variables.variables) == 5
assert variables == five_variable_one_dim_metadata
assert np.isclose(avg_num_edges, expected_num_edges, atol=0.05 * expected_num_edges)
def test_sample_dataset(mixture_sem_sampler_and_shapes: tuple[SEMSampler, dict[str, torch.Size]]):
mixture_sem_sampler, shapes_dict = mixture_sem_sampler_and_shapes
sem = mixture_sem_sampler.sample()
num_train = 5
num_test = 3
num_interventions = 2
dataset = sample_dataset(
sem=sem,
sample_dataset_size=torch.Size([num_train + num_test * 2]),
num_interventions=num_interventions,
num_intervention_samples=num_test,
sample_interventions=True,
sample_counterfactuals=True,
)
assert dataset.noise is not None
for k, v in shapes_dict.items():
assert dataset.observations[k].shape == torch.Size([num_train + num_test * 2]) + v
assert dataset.noise[k].shape == torch.Size([num_train + num_test * 2]) + v
assert dataset.interventions is not None
assert len(dataset.interventions) == num_interventions
assert dataset.counterfactuals is not None
assert len(dataset.counterfactuals) == num_interventions
def test_plot_data(five_variable_one_dim_metadata: VariablesMetadata):
batch_size = 5
data = TensorDict({f"x_{i}": torch.rand([batch_size, 1]) for i in range(5)}, batch_size=torch.Size([batch_size]))
datadir = tempfile.mkdtemp()
if sns is not None:
plot_dataset(data, five_variable_one_dim_metadata, plot_kind="scatter", datadir=datadir)
assert os.path.exists(os.path.join(datadir, "data_distribution.png"))
else:
with pytest.raises(ImportError):
plot_dataset(data, five_variable_one_dim_metadata, plot_kind="scatter", datadir=datadir)
def test_plot_data_multi_dim(five_variable_two_dim_metadata: VariablesMetadata):
batch_size = 5
data = TensorDict({f"x_{i}": torch.rand([batch_size, 2]) for i in range(5)}, batch_size=torch.Size([batch_size]))
datadir = tempfile.mkdtemp()
if sns is not None:
plot_dataset(data, five_variable_two_dim_metadata, plot_kind="scatter", datadir=datadir)
assert os.path.exists(os.path.join(datadir, "data_distribution.png"))
else:
with pytest.raises(ImportError):
plot_dataset(data, five_variable_two_dim_metadata, plot_kind="scatter", datadir=datadir)
def test_sample_treatment_and_effect():
graph = torch.zeros(5, 5)
node_names = [f"x_{i}" for i in range(5)]
with pytest.warns(UserWarning, match="No edges found."):
treatment, effects = sample_treatment_and_effect(graph, node_names)
graph[0, 1] = 1
graph[2, 3] = 1
treatment, effects = sample_treatment_and_effect(graph, node_names)
assert (treatment == "x_0" and effects == ["x_1"]) or (treatment == "x_2" and effects == ["x_3"])
|
causica/test/data_generation/test_generate_data.py/0
|
{
"file_path": "causica/test/data_generation/test_generate_data.py",
"repo_id": "causica",
"token_count": 4695
}
| 606 |
"""
Spline Tests - Note these only compare the behaviour to the old behaviour.
TODO(JJ) update these tests to be standalone
"""
from functools import partial
import torch
from causica.distributions.noise.spline import SplineNoiseModule
INPUT_DIM = 4
torch.manual_seed(123)
assert_close = partial(torch.testing.assert_close, rtol=2e-6, atol=2e-5)
def test_sample_to_noise():
"""Test sample to noise and noise to sample work as expected."""
module = SplineNoiseModule(dim=INPUT_DIM, flow_steps=1)
output_bias = torch.randn(INPUT_DIM)
dist = module(output_bias)
samples = dist.sample((100,))
noise = dist.sample_to_noise(samples)
recon_samples = dist.noise_to_sample(noise)
torch.testing.assert_close(samples, recon_samples)
torch.testing.assert_close(noise, dist.sample_to_noise(recon_samples))
|
causica/test/distributions/noise/spline/test_splines.py/0
|
{
"file_path": "causica/test/distributions/noise/spline/test_splines.py",
"repo_id": "causica",
"token_count": 311
}
| 607 |
import os
import pytest
import torch
from tensordict import TensorDict
from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, load_data
from causica.datasets.tensordict_utils import tensordict_shapes
from causica.datasets.variable_types import VariableTypeEnum
from causica.distributions import ContinuousNoiseDist, JointNoiseModule, create_noise_modules
from causica.functional_relationships.linear_functional_relationships import LinearFunctionalRelationships
from causica.sem.distribution_parameters_sem import DistributionParametersSEM
from causica.training.evaluation import eval_ate_rmse, eval_intervention_likelihoods, eval_ite_rmse
def sem(data_dir: str) -> DistributionParametersSEM:
graph = load_data(data_dir, DataEnum.TRUE_ADJACENCY).to(torch.float32)
assert isinstance(graph, torch.Tensor)
variables_metadata = load_data(data_dir, DataEnum.VARIABLES_JSON)
train_data = load_data(data_dir, DataEnum.TRAIN, variables_metadata)
assert isinstance(train_data, TensorDict)
shapes: dict = tensordict_shapes(train_data)
total_dim = sum(size[-1] for size in shapes.values())
coef_matrix = torch.rand((total_dim, total_dim))
func = LinearFunctionalRelationships(shapes, coef_matrix)
noise_dist = JointNoiseModule(
create_noise_modules(
shapes=shapes,
types=dict.fromkeys(shapes, VariableTypeEnum.CONTINUOUS),
continuous_noise_dist=ContinuousNoiseDist.GAUSSIAN,
)
)
return DistributionParametersSEM(graph=graph, noise_dist=noise_dist, func=func)
@pytest.mark.parametrize("dataset", ["csuite_linexp_2", "csuite_cts_to_cat"])
def test_evaluation_interventional_likelihood(dataset):
root = os.path.join(CAUSICA_DATASETS_PATH, dataset)
data = load_data(root, DataEnum.INTERVENTIONS)
lik = eval_intervention_likelihoods([sem(root)], data[0])
# this is double as there are two interventional datasets in the data
assert lik.shape == torch.Size([data[0][0].intervention_data.shape[0] * 2])
@pytest.mark.parametrize("dataset", ["csuite_linexp_2", "csuite_cts_to_cat"])
def test_evaluation_ate(dataset):
root = os.path.join(CAUSICA_DATASETS_PATH, dataset)
ate = eval_ate_rmse([sem(root)], load_data(root, DataEnum.INTERVENTIONS)[0])
assert ate.shape == tuple()
@pytest.mark.parametrize("dataset", ["csuite_linexp_2"]) # not testing csuite_cts_to_cat as cf file missing
def test_evaluation_ite(dataset):
root = os.path.join(CAUSICA_DATASETS_PATH, dataset)
ite = eval_ite_rmse([sem(root)], load_data(root, DataEnum.COUNTERFACTUALS)[0])
assert ite.shape == tuple()
|
causica/test/integration/test_evaluation.py/0
|
{
"file_path": "causica/test/integration/test_evaluation.py",
"repo_id": "causica",
"token_count": 1002
}
| 608 |
from typing import Callable, List, Tuple, Union
import torch
from torch import nn
from cliffordlayers.nn.modules.gcan import (
CliffordG3Conv2d,
CliffordG3ConvTranspose2d,
CliffordG3GroupNorm,
CliffordG3LinearVSiLU,
CliffordG3SumVSiLU,
CliffordG3MeanVSiLU,
)
def get_activation(activation: str, channels: int) -> Callable:
if activation == "vsum":
return CliffordG3SumVSiLU()
elif activation == "vmean":
return CliffordG3MeanVSiLU()
elif activation == "vlin":
return CliffordG3LinearVSiLU(channels)
else:
raise NotImplementedError(f"Activation {activation} not implemented")
class CliffordG3BasicBlock2d(nn.Module):
"""
Basic block for G3 convolutions on 2D grids, comprising two G3 Clifford convolutional layers.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3.
stride (int, optional): Stride of the convolution operation. Defaults to 1.
padding (int, optional): Padding added to both sides of the input. Defaults to 1.
activation (str, optional): Type of activation function. Defaults to "vlin".
norm (bool, optional): If True, normalization is applied. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
"""
expansion: int = 1
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int = 1,
activation: str = "vlin",
norm: bool = True,
num_groups: int = 1,
prenorm: bool = True,
):
super().__init__()
self.conv1 = CliffordG3Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=True,
)
self.conv2 = CliffordG3Conv2d(
out_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=True,
)
self.norm1 = CliffordG3GroupNorm(num_groups, in_channels, 3) if norm else nn.Identity()
self.norm2 = CliffordG3GroupNorm(num_groups, out_channels, 3) if norm else nn.Identity()
if in_channels != out_channels:
self.shortcut = CliffordG3Conv2d(
in_channels,
out_channels,
kernel_size=1,
)
else:
self.shortcut = nn.Identity()
self.act1 = get_activation(activation, in_channels)
self.act2 = get_activation(activation, out_channels)
self.prenorm = prenorm
def forward(self, x):
if self.prenorm:
out = self.conv1(self.act1(self.norm1(x)))
out = self.conv2(self.act2(self.norm2(out)))
else:
out = self.conv1(self.norm1(self.act1(x)))
out = self.conv2(self.norm2(self.act2(out)))
return out + self.shortcut(x)
class CliffordG3ResNet2d(nn.Module):
"""
ResNet for G3 Clifford convolutions on 2D grids.
Args:
num_blocks (list): Number of blocks at each resolution.
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
hidden_channels (int): Number of hidden channels.
activation (str, optional): Type of activation function. Defaults to "vlin".
block (nn.Module, optional): Type of block. Defaults to CliffordG3BasicBlock2d.
norm (bool, optional): If True, normalization is applied. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
"""
padding = 9
def __init__(
self,
num_blocks: list,
in_channels: int,
out_channels: int,
hidden_channels: int,
activation: str = "vlin",
block: nn.Module = CliffordG3BasicBlock2d,
norm: bool = False,
num_groups: int = 1,
prenorm=True,
):
super().__init__()
# Embedding layers
self.conv_in1 = CliffordG3Conv2d(
in_channels,
hidden_channels,
kernel_size=1,
padding=0,
)
self.conv_in2 = CliffordG3Conv2d(
hidden_channels,
hidden_channels,
)
# Output layers
self.conv_out1 = CliffordG3Conv2d(
hidden_channels,
hidden_channels,
)
self.conv_out2 = CliffordG3Conv2d(
hidden_channels,
out_channels,
kernel_size=1,
padding=0,
)
# ResNet blocks
self.layers = nn.ModuleList(
[
self._make_layer(
block,
hidden_channels,
num_blocks[i],
activation=activation,
norm=norm,
num_groups=num_groups,
prenorm=prenorm,
)
for i in range(len(num_blocks))
]
)
self.act1 = get_activation(activation, hidden_channels)
self.act2 = get_activation(activation, hidden_channels)
def _make_layer(
self,
block: nn.Module,
channels: int,
num_blocks: int,
activation: str,
num_groups: int,
norm: bool = True,
prenorm: bool = True,
) -> nn.Sequential:
layers = []
for _ in range(num_blocks):
layers.append(
block(channels, channels, activation=activation, norm=norm, prenorm=prenorm, num_groups=num_groups)
)
return nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
assert x.dim() == 5
h = self.conv_in1(x)
h = self.act1(h)
# Second embedding layer
h = self.conv_in2(h)
for layer in self.layers:
h = layer(h)
# Output layers
h = self.conv_out1(h)
h = self.act2(h)
h = self.conv_out2(h)
# return output
return h
class CliffordG3DownBlock(nn.Module):
"""
UNet encoder block for G3 Clifford convolutions on 2D grids.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
activation (str): Type of activation function.
norm (bool, optional): If True, normalization is applied. Defaults to False.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
activation: str,
norm: bool = False,
prenorm: bool = True,
num_groups: int = 1,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.block = CliffordG3BasicBlock2d(
in_channels, out_channels, activation=activation, norm=norm, prenorm=prenorm, num_groups=num_groups
)
def forward(self, x):
return self.block(x)
class CliffordG3Downsample(nn.Module):
"""
Scale down the two-dimensional G3 Clifford feature map by a half.
Args:
n_channels (int): Number of channels.
"""
def __init__(self, n_channels):
super().__init__()
self.n_channels = n_channels
self.conv = CliffordG3Conv2d(
n_channels,
n_channels,
kernel_size=3,
stride=2,
padding=1,
)
def forward(self, x):
return self.conv(x)
class CliffordG3MiddleBlock(nn.Module):
"""
UNet middle block for G3 Clifford convolutions on 2D grids.
Args:
n_channels (int): Number of channels.
activation (str): Type of activation function.
norm (bool, optional): If True, normalization is applied. Defaults to False.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
"""
def __init__(
self,
n_channels: int,
activation: str,
norm: bool = False,
prenorm: bool = True,
num_groups: int = 1,
):
super().__init__()
self.res1 = CliffordG3BasicBlock2d(
n_channels,
n_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
num_groups=num_groups,
)
self.res2 = CliffordG3BasicBlock2d(
n_channels,
n_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
num_groups=num_groups,
)
def forward(self, x):
x = self.res1(x)
x = self.res2(x)
return x
class CliffordG3UpBlock(nn.Module):
"""
UNet decoder block for G3 Clifford convolutions on 2D grids.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
activation (str): Type of activation function.
norm (bool, optional): If True, normalization is applied. Defaults to False.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
activation: str,
norm: bool = False,
prenorm: bool = True,
num_groups: int = 1,
):
super().__init__()
# The input has `in_channels + out_channels` because we concatenate the output of the same resolution
# from the first half of the U-Net
self.res = CliffordG3BasicBlock2d(
in_channels + out_channels,
out_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
num_groups=num_groups,
)
def forward(self, x):
return self.res(x)
class CliffordUpsample(nn.Module):
"""
Scale up the two-dimensional G3 Clifford feature map by a factor of two.
Args:
n_channels (int): Number of channels.
"""
def __init__(self, n_channels: int):
super().__init__()
self.conv = CliffordG3ConvTranspose2d(
n_channels,
n_channels,
4,
2,
1,
)
def forward(self, x):
return self.conv(x)
class CliffordG3UNet2d(nn.Module):
"""
U-Net architecture with Clifford G3 convolutions for 2D grids.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
hidden_channels (int): Number of channels in the first hidden convolutional layer.
activation (str, optional): Type of activation function. Defaults to "vlin".
norm (bool, optional): If True, normalization is applied. Defaults to False.
ch_mults (Union[Tuple[int, ...], List[int]], optional): Multipliers for the number of channels at each depth.
Defaults to (1, 2, 2, 2).
n_blocks (int, optional): Number of convolutional blocks at each resolution. Defaults to 2.
prenorm (bool, optional): If True, normalization is applied before activation, otherwise after. Defaults to True.
num_groups (int, optional): Number of groups for the group normalization. Defaults to 1.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
hidden_channels: int,
activation: str = "vlin",
norm: bool = False,
ch_mults: Union[Tuple[int, ...], List[int]] = (1, 2, 2, 2),
n_blocks: int = 2,
prenorm: bool = True,
num_groups: int = 1,
) -> None:
super().__init__()
self.out_channels = out_channels
# Number of resolutions
n_resolutions = len(ch_mults)
self.conv1 = CliffordG3Conv2d(
in_channels,
hidden_channels,
kernel_size=3,
padding=1,
)
# Decreasing resolution
down = []
# Number of channels
out_channels = in_channels = hidden_channels
# For each resolution
for i in range(n_resolutions):
# Number of output channels at this resolution
out_channels = in_channels * ch_mults[i]
for _ in range(n_blocks):
down.append(
CliffordG3DownBlock(
in_channels,
out_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
num_groups=num_groups,
)
)
in_channels = out_channels
# Down sample at all resolutions except the last
if i < n_resolutions - 1:
down.append(
CliffordG3Downsample(
in_channels,
)
)
# Combine the set of modules
self.down = nn.ModuleList(down)
# Middle block
self.middle = CliffordG3MiddleBlock(out_channels, activation=activation, norm=norm, prenorm=prenorm)
# Increasing resolution
up = []
# Number of channels
in_channels = out_channels
# For each resolution
for i in reversed(range(n_resolutions)):
# `n_blocks` at the same resolution
out_channels = in_channels
for _ in range(n_blocks):
up.append(
CliffordG3UpBlock(
in_channels,
out_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
num_groups=num_groups,
)
)
# Final block to reduce the number of channels
out_channels = in_channels // ch_mults[i]
up.append(
CliffordG3UpBlock(
in_channels,
out_channels,
activation=activation,
norm=norm,
prenorm=prenorm,
)
)
in_channels = out_channels
# Up sample at all resolutions except last
if i > 0:
up.append(
CliffordUpsample(
in_channels,
)
)
# Combine the set of modules
self.up = nn.ModuleList(up)
self.activation = get_activation(activation, out_channels)
if norm:
self.norm = CliffordG3GroupNorm(num_groups, out_channels, 3)
else:
self.norm = nn.Identity()
# Output layers
self.conv2 = CliffordG3Conv2d(
in_channels,
self.out_channels,
kernel_size=3,
padding=1,
)
def forward(self, x: torch.Tensor):
assert x.dim() == 5
x = self.conv1(x)
h = [x]
for m in self.down:
x = m(x)
h.append(x)
x = self.middle(x)
for m in self.up:
if isinstance(m, CliffordUpsample):
x = m(x)
else:
# Get the skip connection from first half of U-Net and concatenate
s = h.pop()
x = torch.cat((x, s), dim=1)
x = m(x)
x = self.activation(self.norm(x))
x = self.conv2(x)
return x
|
cliffordlayers/cliffordlayers/models/gca/twod.py/0
|
{
"file_path": "cliffordlayers/cliffordlayers/models/gca/twod.py",
"repo_id": "cliffordlayers",
"token_count": 8054
}
| 609 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from typing import Union
import torch
class CliffordSignature:
def __init__(self, g: Union[tuple, list, torch.Tensor]):
super().__init__()
self.g = self._g_tensor(g)
self.dim = self.g.numel()
if self.dim == 1:
self.n_blades = 2
elif self.dim == 2:
self.n_blades = 4
elif self.dim == 3:
self.n_blades = 8
else:
raise NotImplementedError("Wrong Clifford signature.")
def _g_tensor(self, g: Union[tuple, list, torch.Tensor]) -> torch.Tensor:
"""Convert Clifford signature to tensor.
Args:
g (Union[tuple, list, torch.Tensor]): Clifford signature.
Raises:
ValueError: Unknown metric.
Returns:
torch.Tensor: Clifford signature as torch.Tensor.
"""
if type(g) in (tuple, list):
g = torch.as_tensor(g, dtype=torch.float32)
elif isinstance(g, torch.Tensor):
pass
else:
raise ValueError("Unknown signature.")
if not torch.any(abs(g) == 1.0):
raise ValueError("Clifford signature should have at least one element as 1.")
return g
|
cliffordlayers/cliffordlayers/signature.py/0
|
{
"file_path": "cliffordlayers/cliffordlayers/signature.py",
"repo_id": "cliffordlayers",
"token_count": 595
}
| 610 |
# Clifford models
We provide exemplary 2D and 3D Clifford models as used in the paper.
All these modules are available for different algebras.
## 2D models
The following code snippet initializes a 2D Clifford ResNet.
```python
import torch.nn.functional as F
from cliffordlayers.models.basic.twod import (
CliffordFluidNet2d,
CliffordBasicBlock2d,
)
model = CliffordFluidNet2d(
g = [-1, -1],
block = CliffordBasicBlock2d,
num_blocks = [2, 2, 2, 2],
in_channels = in_channels,
out_channels = out_channels,
hidden_channels = 32,
activation = F.gelu,
norm = True,
rotation = False,
)
```
The following code snippet initializes a 2D rotational Clifford ResNet.
```python
import torch.nn.functional as F
from cliffordlayers.models.basic.twod import (
CliffordFluidNet2d,
CliffordBasicBlock2d,
)
model = CliffordNet2d(
g = [-1, -1],
block = CliffordBasicBlock2d,
num_blocks = [2, 2, 2, 2],
in_channels = in_channels,
out_channels = out_channels,
hidden_channels = 32,
activation = F.gelu,
norm = True,
rotation = True,
)
```
The following code snippet initializes a 2D Clifford FNO.
```python
import torch.nn.functional as F
from cliffordlayers.models.utils import partialclass
from cliffordlayers.models.basic.twod import (
CliffordFluidNet2d,
CliffordFourierBasicBlock2d,
)
model = CliffordFluidNet2d(
g = [-1, -1],
block = partialclass(
"CliffordFourierBasicBlock2d", CliffordFourierBasicBlock2d, modes1=32, modes2=32
),
num_blocks = [1, 1, 1, 1],
in_channels = in_channels,
out_channels = out_channels,
hidden_channels = 32,
activation = F.gelu,
norm = False,
rotation = False,
)
```
::: cliffordlayers.models.basic.twod
options:
heading_level: 3
::: cliffordlayers.models.gca.twod
options:
heading_level: 3
## 3D models
The following code snippet initializes a 3D Clifford FNO.
```python
import torch.nn.functional as F
from cliffordlayers.models.models_3d import (
CliffordMaxwellNet3d,
CliffordFourierBasicBlock3d,
)
model = CliffordMaxwellNet3d(
g = [1, 1, 1],
block = CliffordFourierBasicBlock3d,
num_blocks = [1, 1, 1, 1],
in_channels = 4,
out_channels = 1,
hidden_channels = 16,
activation = F.gelu,
norm = False,
)
```
::: cliffordlayers.models.basic.threed
options:
heading_level: 3
|
cliffordlayers/docs/reference/models.md/0
|
{
"file_path": "cliffordlayers/docs/reference/models.md",
"repo_id": "cliffordlayers",
"token_count": 1136
}
| 611 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import torch.nn as nn
import torch.nn.functional as F
from cliffordlayers.nn.modules.cliffordconv import (
CliffordConv1d,
CliffordConv2d,
CliffordConv3d,
)
from cliffordlayers.nn.modules.cliffordlinear import CliffordLinear
def test_complex_layer():
"""Test Clifford1d linear module against complex linear module using g = [-1]."""
in_channels = 8
out_channels = 16
x = torch.randn(4, in_channels, 2)
clifford_linear = CliffordLinear(g=[-1], in_channels=in_channels, out_channels=out_channels)
output_clifford_linear = clifford_linear(x)
w_c = torch.view_as_complex(clifford_linear.weight.permute(1, 2, 0).contiguous())
b_c = torch.view_as_complex(clifford_linear.bias.permute(1, 0).contiguous())
input_c = torch.view_as_complex(x)
output_c = F.linear(input_c, w_c, b_c)
torch.testing.assert_close(output_clifford_linear, torch.view_as_real(output_c))
def test_clifford_linear_vs_clifford_convolution_1d():
"""Test Clifford1d linear module against Clifford convolution module with kernel_size = 1."""
in_channels = 8
out_channels = 16
x = torch.randn(4, in_channels, 2)
clifford_conv = CliffordConv1d(g=[-1], in_channels=in_channels, out_channels=out_channels, kernel_size=1)
output_clifford_conv = clifford_conv(x[..., None, :])
clifford_linear = CliffordLinear(g=[-1], in_channels=in_channels, out_channels=out_channels)
clifford_linear.weight = nn.Parameter(
torch.Tensor(torch.stack([weight.clone().detach() for weight in clifford_conv.weight])).squeeze()
)
clifford_linear.bias = nn.Parameter(clifford_conv.bias)
output_clifford_linear = clifford_linear(x)
torch.testing.assert_close(output_clifford_conv.squeeze(), output_clifford_linear)
def test_clifford_linear_vs_clifford_convolution_2d():
"""Test Clifford1d linear module against Clifford convolution module with kernel_size = 1."""
in_channels = 8
out_channels = 16
x = torch.randn(4, in_channels, 4)
clifford_conv = CliffordConv2d(g=[-1, -1], in_channels=in_channels, out_channels=out_channels, kernel_size=1)
output_clifford_conv = clifford_conv(x[..., None, None, :])
clifford_linear = CliffordLinear(g=[-1, -1], in_channels=in_channels, out_channels=out_channels)
clifford_linear.weight = nn.Parameter(
torch.Tensor(torch.stack([weight.clone().detach() for weight in clifford_conv.weight])).squeeze()
)
clifford_linear.bias = nn.Parameter(clifford_conv.bias)
output_clifford_linear = clifford_linear(x)
torch.testing.assert_close(output_clifford_conv.squeeze(), output_clifford_linear)
def test_clifford_linear_vs_clifford_convolution_3d():
"""Test Clifford1d linear module against Clifford convolution module with kernel_size = 1."""
in_channels = 8
out_channels = 16
x = torch.randn(4, in_channels, 8)
clifford_conv = CliffordConv3d(g=[-1, -1, -1], in_channels=in_channels, out_channels=out_channels, kernel_size=1)
output_clifford_conv = clifford_conv(x[..., None, None, None, :])
clifford_linear = CliffordLinear(g=[-1, -1, -1], in_channels=in_channels, out_channels=out_channels)
clifford_linear.weight = nn.Parameter(
torch.Tensor(torch.stack([weight.clone().detach() for weight in clifford_conv.weight]).squeeze())
)
clifford_linear.bias = nn.Parameter(clifford_conv.bias)
output_clifford_linear = clifford_linear(x)
torch.testing.assert_close(output_clifford_conv.squeeze(), output_clifford_linear)
|
cliffordlayers/tests/test_clifford_linear.py/0
|
{
"file_path": "cliffordlayers/tests/test_clifford_linear.py",
"repo_id": "cliffordlayers",
"token_count": 1404
}
| 612 |
.git/
.vscode/
**/README.md
**/*.Dockerfile
**/Dockerfile
client/build/
# DB
db.sqlite
# Libraries
**/SafeMath.sol
**/SignedSafeMath.sol
blockchain_db/
node_modules/
client/node_modules/
# Generated contract configs
client/src/contracts/compiled/*.json
# Previous location before refactor:
client/src/contracts/*.json
# Training
**/embedding_cache.json
**/*.zip
client/src/ml-models/hot_dog-not/seefood/
client/coverage/
**/*~
|
0xDeCA10B/demo/.dockerignore/0
|
{
"file_path": "0xDeCA10B/demo/.dockerignore",
"repo_id": "0xDeCA10B",
"token_count": 174
}
| 0 |
const axios = require('axios')
const fs = require('fs')
const pjson = require('../package.json')
const { convertData, convertNum } = require('../src/float-utils-node.js')
const CollaborativeTrainer64 = artifacts.require("./CollaborativeTrainer64")
const DataHandler64 = artifacts.require("./data/DataHandler64")
const DensePerceptron = artifacts.require("./classification/DensePerceptron")
const Stakeable64 = artifacts.require("./incentive/Stakeable64")
module.exports = function (deployer) {
if (deployer.network === 'skipMigrations') {
return
}
const toFloat = 1E9
// Information to persist to the database.
const name = "Hot Dog Classifier"
const description = "Classifies pictures as hot dog or not hot dog."
const encoder = 'MobileNetV2'
const modelInfo = {
name,
description,
accuracy: '0.63',
modelType: 'Classifier64',
encoder,
}
// Low default times for testing.
const refundTimeWaitTimeS = 15
const anyAddressClaimWaitTimeS = 20
const ownerClaimWaitTimeS = 20
// Weight for deposit cost in wei.
const costWeight = 1E15
const weightChunkSize = 450
// Model
let model = fs.readFileSync('./src/ml-models/hot_dog-not/classifier-perceptron-400.json', 'utf8')
model = JSON.parse(model)
const { classifications, featureIndices } = model
const weights = convertData(model.weights, web3, toFloat)
const intercept = convertNum(model.bias, web3, toFloat)
const learningRate = convertNum(0.5, web3, toFloat)
console.log(`Deploying Hot Dog classifier.`)
// Trick to get await to work:
deployer.then(async () => {
const dataHandler = await deployer.deploy(DataHandler64)
console.log(` Deployed data handler to ${dataHandler.address}.`)
const incentiveMechanism = await deployer.deploy(Stakeable64,
refundTimeWaitTimeS,
ownerClaimWaitTimeS,
anyAddressClaimWaitTimeS,
costWeight)
console.log(` Deployed incentive mechanism to ${incentiveMechanism.address}.`)
console.log(` Deploying classifier with first ${Math.min(weights.length, weightChunkSize)} weights.`)
const classifier = await deployer.deploy(DensePerceptron,
classifications, weights.slice(0, weightChunkSize), intercept, learningRate,
// Block gasLimit by most miners as of May 2019.
{ gas: 8E6 })
// Add remaining weights.
for (let i = weightChunkSize; i < weights.length; i += weightChunkSize) {
console.log(` Deploying classifier weights [${i}, ${Math.min(i + weightChunkSize, weights.length)}).`)
await classifier.initializeWeights(weights.slice(i, i + weightChunkSize))
}
// Add feature indices to use.
if (featureIndices !== undefined) {
if (featureIndices.length !== weights.length) {
throw new Error("The number of features must match the number of weights.")
}
for (let i = 0; i < featureIndices.length; i += weightChunkSize) {
console.log(` Deploying classifier feature indices [${i}, ${Math.min(i + weightChunkSize, featureIndices.length)}).`)
await classifier.addFeatureIndices(featureIndices.slice(i, i + weightChunkSize))
}
}
console.log(`Deploying main entry point.`)
const instance = await deployer.deploy(CollaborativeTrainer64,
name, description, encoder,
dataHandler.address,
incentiveMechanism.address,
classifier.address
)
console.log(` Deployed Hot Dog collaborative classifier to ${instance.address}.`)
await dataHandler.transferOwnership(instance.address)
await incentiveMechanism.transferOwnership(instance.address)
await classifier.transferOwnership(instance.address)
modelInfo.address = instance.address
return axios.post(`${pjson.proxy}api/models`, modelInfo).then(() => {
console.log("Added model to the database.")
}).catch(err => {
if (process.env.CI !== "true" && process.env.REACT_APP_ENABLE_SERVICE_DATA_STORE === 'true') {
console.error("Error adding model to the database.")
console.error(err)
throw err
}
})
})
}
|
0xDeCA10B/demo/client/migrations/4_deploy_hot_dog_classifier.js/0
|
{
"file_path": "0xDeCA10B/demo/client/migrations/4_deploy_hot_dog_classifier.js",
"repo_id": "0xDeCA10B",
"token_count": 1319
}
| 1 |
pragma solidity ^0.6;
contract Migrations {
address public owner;
uint public last_completed_migration;
modifier restricted() {
if (msg.sender == owner) _;
}
constructor() public {
owner = msg.sender;
}
function setCompleted(uint completed) public restricted {
last_completed_migration = completed;
}
function upgrade(address new_address) public restricted {
Migrations upgraded = Migrations(new_address);
upgraded.setCompleted(last_completed_migration);
}
}
|
0xDeCA10B/demo/client/src/contracts/Migrations.sol/0
|
{
"file_path": "0xDeCA10B/demo/client/src/contracts/Migrations.sol",
"repo_id": "0xDeCA10B",
"token_count": 201
}
| 2 |
const _toFloat = 1E9
export function convertNum(num, _web3, toFloat = _toFloat) {
// We used to convert to BN here
// but it caused problems and inconsistencies when running tests vs. running in the browser (using MetaMask).
return Math.round(num * toFloat)
}
export function convertToHex(num, web3, toFloat = _toFloat) {
return web3.utils.toHex(Math.round(num * toFloat))
}
export function convertData(data, web3, toFloat = _toFloat) {
return data.map(num => convertNum(num, web3, toFloat))
}
export function convertDataToHex(data, web3, toFloat = _toFloat) {
return data.map(num => convertToHex(num, web3, toFloat))
}
|
0xDeCA10B/demo/client/src/float-utils.js/0
|
{
"file_path": "0xDeCA10B/demo/client/src/float-utils.js",
"repo_id": "0xDeCA10B",
"token_count": 212
}
| 3 |
/**
* The base class for a model to be deployed to a smart contract.
*/
export class Model {
/**
* @param type The type of model.
*/
constructor(public type: 'naive bayes'
| 'nearest centroid classifier' | 'sparse nearest centroid classifier' | 'dense nearest centroid classifier'
| 'perceptron' | 'dense perceptron' | 'sparse perceptron') {
}
}
/**
* A Multinomial Naive Bayes classifier.
*/
export class NaiveBayesModel extends Model {
/**
* @param type The type of model.
* @param classifications The classifications supported by the model.
* @param classCounts The number of occurrences of each class in the training data.
* @param featureCounts For each class, the number of times each feature occurs within that class.
* Each innermost list is a tuple of the feature index and the number of times that feature occurs within the class.
* For example: Class 0 has feature 3 13 times and feature 5 15 times. Class 1 has feature 8 18 times: [[[3, 13], [5, 15]], [8, 18]].
* @param totalNumFeatures The total number of features throughout all classes.
* @param smoothingFactor The smoothing factor (sometimes called alpha). Use 1 for Laplace smoothing.
*/
constructor(
type: 'naive bayes',
public classifications: string[],
public classCounts: number[],
public featureCounts: number[][][],
public totalNumFeatures: number,
public smoothingFactor: number,
) {
super(type)
}
}
/**
* A nearest centroid classifier that uses Euclidean distance to predict the closest centroid.
*
* https://en.wikipedia.org/wiki/Nearest_centroid_classifier
*/
export class NearestCentroidModel extends Model {
/**
* @param type The type of model.
* @param centroids A mapping of the classification name to the centroid for that classification.
*/
constructor(
type: 'nearest centroid classifier' | 'dense nearest centroid classifier',
public centroids: { [classification: string]: CentroidInfo },
) {
super(type)
}
}
/**
* Information for each centroid in a `{@link NearestCentroidModel}`.
*/
export class CentroidInfo {
/**
* @param centroid The average of all data points in the class.
* @param dataCount The number of samples in the class.
*/
constructor(
public centroid: number[],
public dataCount: number,
) {
}
}
export class SparseNearestCentroidModel extends Model {
/**
* @param type The type of model.
* @param centroids A mapping of the classification name to the centroid for that classification.
*/
constructor(
type: 'sparse nearest centroid classifier',
public centroids: { [classification: string]: SparseCentroidInfo },
) {
super(type)
}
}
/**
* Information for each centroid in a `{@link NearestCentroidModel}`.
*/
export class SparseCentroidInfo {
/**
* @param centroid The average of all data points in the class.
* The feature indices should be integers but they are strings for convenience when loading from JSON.
* @param dataCount The number of samples in the class.
*/
constructor(
public centroid: { [featureIndex: string]: number },
public dataCount: number,
) {
}
}
/**
* A Perceptron where the data given for updating and predicting is dense.
*/
export class DensePerceptronModel extends Model {
/**
* @param type The type of model. 'perceptron' defaults to a dense model.
* @param classifications The classifications supported by the model.
* @param weights The weights for the model. Can be used for dense or sparse models.
* @param intercept The bias to add to the multiplication of the weights and the data.
* @param learningRate (Optional, defaults to 1). The amount of impact that new training data has to the weights.
* @param featureIndices (Optional, default means to use all features)
* The indices of the features to use from some well-known shared encoder.
*/
constructor(
type: 'perceptron' | 'dense perceptron',
public classifications: string[],
public weights: number[],
public intercept: number,
public learningRate?: number,
public featureIndices?: number[],
) {
super(type)
}
}
/**
* A Perceptron where the data given for updating and predicting is dense.
*/
export class SparsePerceptronModel extends Model {
/**
* @param type The type of model. 'perceptron' defaults to a dense model.
* @param classifications The classifications supported by the model.
* @param weights The weights for the model. Can be used for dense or sparse models.
* @param sparseWeights Additional weights indexed for a sparse model.
* The feature indices should be integers but they are strings for convenience when loading from JSON.
* @param intercept The bias to add to the multiplication of the weights and the data.
* @param learningRate (Optional, defaults to 1). The amount of impact that new training data has to the weights.
* @param featureIndices (Optional, default means to use all features)
* The indices of the features to use from some well-known shared encoder.
*/
constructor(
type: 'sparse perceptron',
public classifications: string[],
public weights: number[],
public sparseWeights: { [featureIndex: string]: number } | null,
public intercept: number,
public learningRate?: number,
public featureIndices?: number[],
) {
super(type)
}
}
|
0xDeCA10B/demo/client/src/ml-models/model-interfaces.ts/0
|
{
"file_path": "0xDeCA10B/demo/client/src/ml-models/model-interfaces.ts",
"repo_id": "0xDeCA10B",
"token_count": 1508
}
| 4 |
export const BASE_TITLE = "Sharing Updatable Models (SUM)"
|
0xDeCA10B/demo/client/src/title.js/0
|
{
"file_path": "0xDeCA10B/demo/client/src/title.js",
"repo_id": "0xDeCA10B",
"token_count": 19
}
| 5 |
const path = require('path')
// Use NODE_ENVIRONMENT if set but remove . before it if production.
const env = process.env.NODE_ENVIRONMENT
let suffix = '.development'
if (env === 'production') {
suffix = ''
} else if (env !== undefined) {
suffix = `.${env}`
}
require('dotenv').config({ path: path.resolve(__dirname, `.env${suffix}`) })
module.exports = {
// See <https://truffleframework.com/docs/truffle/reference/configuration>
// for more about customizing your Truffle configuration!
// Following the directory structure for solidity (juanblanco.solidity) VS Code extension.
contracts_directory: path.join(__dirname, 'src/contracts'),
contracts_build_directory: path.join(__dirname, 'src/contracts/compiled'),
compilers: {
solc: {
version: "0.6.2",
},
},
networks: {
development: {
host: "127.0.0.1",
port: 7545,
// Match any network ID.
network_id: "*",
gas: 9E6 - 5000,
},
skipMigrations: {
host: "127.0.0.1",
port: 7545,
// Match any network ID.
network_id: "*",
gas: 9E6 - 5000,
}
},
mocha: {
enableTimeouts: false,
}
}
|
0xDeCA10B/demo/client/truffle.js/0
|
{
"file_path": "0xDeCA10B/demo/client/truffle.js",
"repo_id": "0xDeCA10B",
"token_count": 438
}
| 6 |
from dataclasses import dataclass, field
from logging import Logger
from typing import List
import numpy as np
from injector import inject, Module
from sklearn.utils import shuffle
from tqdm import trange
from .data_loader import DataLoader
@inject
@dataclass
class TicTacToeDataLoader(DataLoader):
"""
Load data for Tic-Tac-Toe games.
Data is flattened `width` x `length` games.
The players are 1 and -1. The data is from the perspective of player 1, opponent is -1.
0 means no one has played in that position.
"""
_logger: Logger
_seed: int = field(default=2, init=False)
_train_split: float = field(default=0.7, init=False)
width: int = field(default=3, init=False)
length: int = field(default=3, init=False)
def classifications(self) -> List[str]:
return list(map(str, map(self.map_pos, range(self.width * self.length))))
def get_winner(self, board):
def get_single_winner(line: set):
if len(line) == 1:
val = next(iter(line))
if val != 0:
return val
return None
for row in range(self.width):
result = get_single_winner(set(board[row]))
if result is not None:
return result
for col in range(self.length):
result = get_single_winner(set(board[:, col]))
if result is not None:
return result
result = get_single_winner(set(board.diagonal()))
if result is not None:
return result
diag_vals = set(board[i, self.length - 1 - i] for i in range(self.width))
result = get_single_winner(diag_vals)
if result is not None:
return result
return None
def map_pos(self, pos):
return pos // self.width, pos % self.width
def load_data(self, train_size: int = None, test_size: int = None) -> (tuple, tuple):
X, y = [], []
bad_moves = set()
players = (1, -1)
assert self.width == self.length, "The following code assumes that the board is square."
def fill(board, start_pos, next_player, path):
# See if there is a winning move.
winner = None
for pos in range(start_pos, self.width * self.length):
i, j = self.map_pos(pos)
if board[i, j] != 0:
continue
_board = board.copy()
_board[i, j] = next_player
winner = self.get_winner(_board)
if winner is not None:
path.append((board, pos, next_player))
break
if winner is not None:
# Only count wins for one of the players to make setting up games simpler.
if winner == players[0]:
for history_board, history_position, history_player in path:
history_board = history_board.flatten()
if history_player == winner:
X.append(history_board)
y.append(history_position)
else:
bad_moves.add((tuple(-history_board.flatten()), -history_position))
else:
# Recurse.
for pos in range(start_pos, self.width * self.length):
i, j = self.map_pos(pos)
if board[i, j] != 0:
continue
_path = list(path)
_path.append((board, pos, next_player))
_board = board.copy()
_board[i, j] = next_player
fill(_board, start_pos, next_player=-1 if next_player == 1 else 1, path=_path)
self._logger.info("Loading Tic Tac Toe data.")
for init_pos in trange(self.width * self.length,
desc="Making boards",
unit_scale=True, mininterval=2, unit=" start positions"
):
pos = self.map_pos(init_pos)
for player in players:
board = np.zeros((self.width, self.length), dtype=np.int8)
path = [(board.copy(), init_pos, player)]
board[pos] = player
fill(board, init_pos + 1, next_player=-1 if player == 1 else 1, path=path)
# Remove bad moves.
# Note this might not help much depending on the model.
X, y = zip(*[(X[i], y[i]) for i in range(len(X)) if (tuple(X[i]), y[i]) not in bad_moves])
X, y = shuffle(X, y, random_state=self._seed)
split = int(self._train_split * len(X))
x_train, y_train = np.array(X[:split]), np.array(y[:split])
x_test, y_test = np.array(X[split:]), np.array(y[split:])
if train_size is not None:
x_train, y_train = x_train[:train_size], y_train[:train_size]
if test_size is not None:
x_test, y_test = x_test[:test_size], y_test[:test_size]
# Show some data.
# import random
# for _ in range(10):
# i = random.randrange(len(X))
# print(X[i].reshape((self.width, self.length)), y[i])
self._logger.info("Done loading data.\nCreated %d boards.", len(X))
return (x_train, y_train), (x_test, y_test)
class TicTacToeDataModule(Module):
def configure(self, binder):
binder.bind(DataLoader, TicTacToeDataLoader)
|
0xDeCA10B/simulation/decai/simulation/data/ttt_data_loader.py/0
|
{
"file_path": "0xDeCA10B/simulation/decai/simulation/data/ttt_data_loader.py",
"repo_id": "0xDeCA10B",
"token_count": 2697
}
| 7 |
pendulum-apex-ddpg:
env: Pendulum-v0
run: APEX_DDPG
stop:
episode_reward_mean: -160
config:
# Works for both torch and tf.
framework: tf
use_huber: True
clip_rewards: False
num_workers: 4
n_step: 1
target_network_update_freq: 50000
tau: 1.0
evaluation_interval: 5
evaluation_num_episodes: 10
|
AI-System/Labs/AdvancedLabs/Lab9/config/pendulum-apex-ddpg-16.yaml/0
|
{
"file_path": "AI-System/Labs/AdvancedLabs/Lab9/config/pendulum-apex-ddpg-16.yaml",
"repo_id": "AI-System",
"token_count": 205
}
| 8 |
# Setup Environment
## 实验环境要求
### 操作系统
`Ubuntu 18.04 LTS x86_64`
### 编程语言
`python3.7.6` (`Anaconda3`环境)
### 学习框架
`PyTorch==1.7.0`
### 硬件环境
单GPU（with CUDA 10.1）
## 实验环境搭建
### 安装anaconda3
安装教程：https://docs.anaconda.com/anaconda/install/linux/#installation
下载地址：https://www.anaconda.com/distribution/#linux
1. 下载命令：(注意：$ 为linux系统下的命令提示符，不属于命令部分)
```
$ wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh
$ bash Anaconda3-2020.02-Linux-x86_64.sh
```
**注：** 请按照命令行提示安装anaconda，所有选项输入yes)
2. 激活conda环境：
```
$ source ~/.bashrc
```
3. 测试是否安装成功：
```
$ conda -V
```
若安装成功，则会显示： conda 4.8.2
### 安装python3.7.6（若使用Anaconda3的base环境，默认是python3.7.6，则不需额外安装）
1. 创建新的conda环境：
```
$ conda create -n py37 python=3.7.6
```
2. 激活python3.7:
```
$ conda activate py37
```
### 安装gcc（如果机器已经安装gcc，请忽略）
```
$ sudo apt-get update
$ sudo apt-get install build-essential
```
### 安装pytorch (version 1.7.0)
GPU（CUDA10.1）版本：
```
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch
```
Download issue:
```
from six.moves import urllib
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)
```
Steps:
```
git clone https://github.com/microsoft/AI-System.git
cd AI-System/Labs/BasicLabs/Lab2/
#execute basic version
python3 mnist_basic.py
#execute custom version
python3 mnist_custom_linear.py
#execute custom c++ version
cd mylinear_cpp_extension/
python3 setup.py install
cd ../
python3 mnist_custom_linear_cpp.py
#execute custom cuda version
cd ../Lab3/mylinear_cuda_extension
python3 setup.py install
cd ..
python3 mnist_custom_linear_cuda.py
```
|
AI-System/Labs/BasicLabs/Lab2/setup.md/0
|
{
"file_path": "AI-System/Labs/BasicLabs/Lab2/setup.md",
"repo_id": "AI-System",
"token_count": 1123
}
| 9 |
# Owner每当发现一种错误可以补充检查规则进而检查类似问题
# 模板标准检查
# 1 图号递增
# 2 章节号递增
# 3 应有章节和命名
# 术语表检查
# 中英文标点符号检查
# 英文大小写检查
# 中文多余空格检查
# 缩进检查
# 中文语法纠错
|
AI-System/Textbook/src/Checker.py/0
|
{
"file_path": "AI-System/Textbook/src/Checker.py",
"repo_id": "AI-System",
"token_count": 239
}
| 10 |
<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
## 2.5 深度神经网络
本小节主要围绕，深度神经网络，权重矩阵初始化，批量归一化，过拟合内容进行介绍。
- [2.5 深度神经网络](#25-深度神经网络)
- [2.5.1 抽象与设计](#251-抽象与设计)
- [2.5.2 权重矩阵初始化](#252-权重矩阵初始化)
- [零初始化](#零初始化)
- [标准初始化](#标准初始化)
- [Xavier初始化方法](#xavier初始化方法)
- [MSRA初始化方法](#msra初始化方法)
- [2.5.3 批量归一化](#253-批量归一化)
- [前向计算](#前向计算)
- [批量归一化的优点](#批量归一化的优点)
- [2.5.4 过拟合](#254-过拟合)
- [拟合程度比较](#拟合程度比较)
- [出现过拟合的原因](#出现过拟合的原因)
- [解决方案](#解决方案)
- [小结与讨论](#小结与讨论)
- [参考文献](#参考文献)
### 2.5.1 抽象与设计
比较神经网络的基础代码，我们可以看到大量的重复之处，比如前向计算中，都是：矩阵运算+激活/分类函数。再看看反向传播：每一层的模式也非常相近：计算本层的`dZ`，再根据`dZ`计算`dW`和`dB`。
因为三层网络比两层网络多了一层，所以会在初始化、前向、反向、更新参数等四个环节有所不同，但却是有规律的。再加上前面章节中，为了实现一些辅助功能，我们已经写了很多类。所以，现在可以动手搭建一个深度学习的迷你框架了。
图 2.5.1 是迷你框架的模块化设计，下面对各个模块做功能点上的解释。
<img src="./img/class.png" />
图 2.5.1 迷你框架设计
- NeuralNet：首先需要一个`NeuralNet`类，来包装基本的神经网络结构和功能
- Layer：是一个抽象类，以及更加需要增加的实际类
- Activator Layer：激活函数和分类函数
- Classification Layer：分类函数，包括`Sigmoid`二分类，`Softmax`多分类
- Parameters：基本神经网络运行参数
- LossFunction：损失函数及帮助方法
- Optimizer：优化器
- WeightsBias：权重矩阵，仅供全连接层使用
- DataReader：样本数据读取器
### 2.5.2 权重矩阵初始化
权重矩阵初始化是一个非常重要的环节，是训练神经网络的第一步，选择正确的初始化方法会带了事半功倍的效果。这就好比攀登喜马拉雅山，如果选择从南坡登山，会比从北坡容易很多。而初始化权重矩阵，相当于下山时选择不同的道路，在选择之前并不知道这条路的难易程度，只是知道它可以抵达山下。这种选择是随机的，即使你使用了正确的初始化算法，每次重新初始化时也会给训练结果带来很多影响。
比如第一次初始化时得到权重值为(0.12847，0.36453)，而第二次初始化得到(0.23334，0.24352)，经过试验，第一次初始化用了3000次迭代达到精度为96%的模型，第二次初始化只用了2000次迭代就达到了相同精度。这种情况在实践中是常见的。
常用的初始化方法有以下几种。
#### 零初始化
即把所有层的`W`值的初始值都设置为0。
$$
W = 0
$$
但是对于多层网络来说，绝对不能用零初始化，否则权重值不能学习到合理的结果。。
#### 标准初始化
标准正态初始化方法保证激活函数的输入均值为0，方差为1。将W按如下公式进行初始化：
$$
W \sim N \begin{bmatrix} 0, 1 \end{bmatrix}
$$
其中的W为权重矩阵，N表示高斯分布，Gaussian Distribution，也叫做正态分布，Normal Distribution，所以有的地方也称这种初始化为Normal初始化。
一般会根据全连接层的输入和输出数量来决定初始化的细节：
$$
W \sim N
\begin{pmatrix}
0, \frac{1}{\sqrt{n_{in}}}
\end{pmatrix}
$$
$$
W \sim U
\begin{pmatrix}
-\frac{1}{\sqrt{n_{in}}}, \frac{1}{\sqrt{n_{in}}}
\end{pmatrix}
$$
#### Xavier初始化方法
条件：正向传播时，激活值的方差保持不变；反向传播时，关于状态值的梯度的方差保持不变。
$$
W \sim N
\begin{pmatrix}
0, \sqrt{\frac{2}{n_{in} + n_{out}}}
\end{pmatrix}
$$
$$
W \sim U
\begin{pmatrix}
-\sqrt{\frac{6}{n_{in} + n_{out}}}, \sqrt{\frac{6}{n_{in} + n_{out}}}
\end{pmatrix}
$$
其中的W为权重矩阵，N表示正态分布（Normal Distribution），U表示均匀分布（Uniform Distribution)。下同。
假设激活函数关于0对称，且主要针对于全连接神经网络。适用于tanh和softsign。
即权重矩阵参数应该满足在该区间内的均匀分布。其中的W是权重矩阵，U是Uniform分布，即均匀分布。
#### MSRA初始化方法
条件：正向传播时，状态值的方差保持不变；反向传播时，关于激活值的梯度的方差保持不变。
“Xavier”是一种相对不错的初始化方法，但是，Xavier推导的时候假设激活函数在零点附近是线性的，显然我们目前常用的ReLU和PReLU并不满足这一条件。所以MSRA初始化主要是想解决使用ReLU激活函数后，方差会发生变化，因此初始化权重的方法也应该变化。
只考虑输入个数时，MSRA初始化是一个均值为0，方差为2/n的高斯分布，适合于ReLU激活函数：
$$
W \sim N
\begin{pmatrix}
0, \sqrt{\frac{2}{n}}
\end{pmatrix}
$$
$$
W \sim U
\begin{pmatrix}
-\sqrt{\frac{6}{n_{in}}}, \sqrt{\frac{6}{n_{out}}}
\end{pmatrix}
$$
### 2.5.3 批量归一化
既然可以把原始训练样本做归一化，那么如果在深度神经网络的每一层，都可以有类似的手段，也就是说把层之间传递的数据移到0点附近，那么训练效果就应该会很理想。这就是批归一化BN的想法的来源。
深度神经网络随着网络深度加深，训练起来越困难，收敛越来越慢，这是个在DL领域很接近本质的问题。很多论文都是解决这个问题的，比如ReLU激活函数，再比如Residual Network。BN本质上也是解释并从某个不同的角度来解决这个问题的。
BN就是在深度神经网络训练过程中使得每一层神经网络的输入保持相同的分布，致力于将每一层的输入数据正则化成$N(0,1)$的分布。因次，每次训练的数据必须是mini-batch形式，一般取32，64等数值。
具体的数据处理过程如图 2.5.2 所示。
<img src="./img/bn6.png" width="500" />
图 2.5.2 数据处理过程
1. 数据在训练过程中，在网络的某一层会发生Internal Covariate Shift，导致数据处于激活函数的饱和区；
2. 经过均值为0、方差为1的变换后，位移到了0点附近。但是只做到这一步的话，会带来两个问题：
a. 在[-1,1]这个区域，Sigmoid激活函数是近似线性的，造成激活函数失去非线性的作用；
b. 在二分类问题中我们学习过，神经网络把正类样本点推向了右侧，把负类样本点推向了左侧，如果再把它们强行向中间集中的话，那么前面学习到的成果就会被破坏；
3. 经过$\gamma,\beta$的线性变换后，把数据区域拉宽，则激活函数的输出既有线性的部分，也有非线性的部分，这就解决了问题a；而且由于$\gamma,\beta$也是通过网络进行学习的，所以以前学到的成果也会保持，这就解决了问题b。
在实际的工程中，我们把BN当作一个层来看待，一般架设在全连接层（或卷积层）与激活函数层之间。
#### 前向计算
表 2.5.1 中，m表示batch_size的大小，比如32或64个样本/批；n表示features数量，即样本特征值数量。
表 2.5.1 各个参数的含义和数据形状
|符号|数据类型|数据形状|
|:---------:|:-----------:|:---------:|
|$X$| 输入数据矩阵 | [m, n] |
|$x_i$|输入数据第i个样本| [1, n] |
|$N$| 经过归一化的数据矩阵 | [m, n] |
|$n_i$| 经过归一化的单样本 | [1, n] |
|$\mu_B$| 批数据均值 | [1, n] |
|$\sigma^2_B$| 批数据方差 | [1, n] |
|$m$|批样本数量| [1] |
|$\gamma$|线性变换参数| [1, n] |
|$\beta$|线性变换参数| [1, n] |
|$Z$|线性变换后的矩阵| [1, n] |
|$z_i$|线性变换后的单样本| [1, n] |
|$\delta$| 反向传入的误差 | [m, n] |
如无特殊说明，以下乘法为元素乘，即element wise的乘法。
在训练过程中，针对每一个batch数据，m是批的大小。进行的操作是，将这组数据正则化，之后对其进行线性变换。
具体的算法步骤是：
$$
\mu_B = \frac{1}{m}\sum_1^m x_i
$$
$$
\sigma^2_B = \frac{1}{m} \sum_1^m (x_i-\mu_B)^2
$$
$$
n_i = \frac{x_i-\mu_B}{\sqrt{\sigma^2_B + \epsilon}}
$$
$$
z_i = \gamma n_i + \beta
$$
其中，$\gamma,\beta$是训练出来的，$\epsilon$是防止$\sigma_B^2$为0时加的一个很小的数值，通常为`1e-5`。
#### 批量归一化的优点
1. 可以选择比较大的初始学习率，让你的训练速度提高。
以前还需要慢慢调整学习率，甚至在网络训练到一定程度时，还需要想着学习率进一步调小的比例选择多少比较合适，现在我们可以采用初始很大的学习率，因为这个算法收敛很快。当然这个算法即使你选择了较小的学习率，也比以前的收敛速度快，因为它具有快速训练收敛的特性；
2. 减少对初始化的依赖
一个不太幸运的初始化，可能会造成网络训练实际很长，甚至不收敛。
3. 减少对正则的依赖
在第16章中，我们将会学习正则化知识，以增强网络的泛化能力。采用BN算法后，我们会逐步减少对正则的依赖，比如令人头疼的dropout、L2正则项参数的选择问题，或者可以选择更小的L2正则约束参数了，因为BN具有提高网络泛化能力的特性；
### 2.5.4 过拟合
#### 拟合程度比较
在深度神经网络中，我们遇到的另外一个挑战，就是网络的泛化问题。所谓泛化，就是模型在测试集上的表现要和训练集上一样好。经常有这样的例子：一个模型在训练集上千锤百炼，能到达99%的准确率，拿到测试集上一试，准确率还不到90%。这说明模型过度拟合了训练数据，而不能反映真实世界的情况。解决过度拟合的手段和过程，就叫做泛化。
神经网络的两大功能：回归和分类。这两类任务，都会出现欠拟合和过拟合现象，如图16-1和16-2所示。
<img src="./img/fitting.png" />
图 2.5.3 回归任务中的欠拟合、正确的拟合、过拟合
图 2.5.3 是回归任务中的三种情况，依次为：欠拟合、正确的拟合、过拟合。
<img src="./img/classification.png" />
图 2.5.4 分类任务中的欠拟合、正确的拟合、过拟合
图 2.5.4 是分类任务中的三种情况，依次为：分类欠妥、正确的分类、分类过度。由于分类可以看作是对分类边界的拟合，所以我们经常也统称其为拟合。
上图中对于“深入敌后”的那颗绿色点样本，正确的做法是把它当作噪音看待，而不要让它对网络产生影响。而对于上例中的欠拟合情况，如果简单的（线性）模型不能很好地完成任务，我们可以考虑使用复杂的（非线性或深度）模型，即加深网络的宽度和深度，提高神经网络的能力。
但是如果网络过于宽和深，就会出现第三张图展示的过拟合的情况。
#### 出现过拟合的原因
1. 训练集的数量和模型的复杂度不匹配，样本数量级小于模型的参数
2. 训练集和测试集的特征分布不一致
3. 样本噪音大，使得神经网络学习到了噪音，正常样本的行为被抑制
4. 迭代次数过多，过分拟合了训练数据，包括噪音部分和一些非重要特征
既然模型过于复杂，那么我们简化模型不就行了吗？为什么要用复杂度不匹配的模型呢？有两个原因：
1. 因为有的模型以及非常成熟了，比如VGG16，可以不调参而直接用于你自己的数据训练，此时如果你的数据数量不够多，但是又想使用现有模型，就需要给模型加正则项了。
2. 使用相对复杂的模型，可以比较快速地使得网络训练收敛，以节省时间。
最终我们可以得到如图 2.5.5 所示的训练曲线。
<img src="./img/overfit_result.png" />
图 2.5.5 过拟合例子二的训练曲线
在训练集上（蓝色曲线），很快就达到了损失函数值趋近于0，准确度100%的程度。而在验证集上（红色曲线），损失函数值却越来越大，准确度也在下降。这就造成了一个典型的过拟合网络，即所谓U型曲线，无论是损失函数值和准确度，都呈现出了这种分化的特征。
#### 解决方案
有了直观感受和理论知识，下面我们看看如何解决过拟合问题：
1. 数据扩展（Data Augmentation)
2. L2 / L1 正则 (Regularization)
3. 丢弃法 (Dropout)
4. 早停法 (Early Stop)
5. 集成学习法 (Ensemble Learning)
6. 特征工程（属于传统机器学习范畴，不在此处讨论）
7. 简化模型，减小网络的宽度和深度
篇幅有限不再展开，有兴趣的读者请参考《智能之门》一书。
## 小结与讨论
本小节主要介绍了深度神经网络，权重矩阵初始化，批量归一化，过拟合内容。
请读者尝试通过以上方法调优已有的一个模型训练过程，观察模型的收敛性。并思考能否自动化调优当前的一些配置？
## 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
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<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 5.5 跨算子的全局调度优化
- [5.5 跨算子的全局调度优化](#55-跨算子的全局调度优化)
- [5.5.1 任意算子的融合](#551-任意算子的融合)
- [5.5.2 编译时全局算子调度](#552编译时全局算子调度)
- [小结与讨论](#小结与讨论)
- [参考文献](#参考文献)
前面的优化和算子生成分别在计算图和算子表达式两个层次完成。这种分层的优化给编译器的设计和实现带来更清楚的模块化和可维护性，但是同时也由于上下层的分离损失了一些更进一步的优化机会，例如硬件利用率低，无法完全发挥硬件的计算性能。造成这些低效的主要原因包括：
- 单个Op的调度时间与计算时间相比不可忽略，造成较大的调度开销；
- OP的并行度不足以占满GPU的计算核心。
## 5.5.1 任意算子的融合
为了解决上述问题，一个很自然的想法就是：能不能对任意的算子进行融合，从而提高硬件利用率，降低算子的调度开销。一种最简单的方法就是实现更加激进的自动算子融合。如图5-5-1所示的为一个简单的计算图，与前面的算子融合不同的是，我们为了对任意算子做融合，引入了非element-wise算子，如矩阵乘法，这会给之前的融合方法增加难度。
<center> <img src="./img/5-5-1-example.png" /></center>
<center>图5-5-1. 一个简单的计算图</center>
为了实现任意算子融合，我们需要每一个算子的内核函数，如该例子中需要的Sigmoid、Relu和MatMul算子，下面展示的为这三个算子的内核函数：
```
__device__ float sigmoidf(float in) {
return 1.f / (1.f + expf(-in));
}
__device__ float reluf(float in) {
return fmaxf(0.f, in);
}
__device__ float MatMul(float *a, float *b, float *c, int m, int n, int k) {
if (thread_ix < m && thread_iy < k) {
float temp = 0.f;
for (int i = 0; i < n; ++i) {
temp += a[tix*n+i] * b[k*i+tiy];
}
c[k * tix + tiy] = temp;
}
}
```
为了按照前面讲到的的方法进行这三个算子的融合，我们需要将上述三个函数生成到同一个全局核函数内，如下图示例。值的注意的是，为了保证任意算子之前有正确的数据依赖，我们有时候需要在两个算子之间插入一个全局的数据同步。
```
//融合三个算子后的全局内核函数
__global__
void kernel_0(float *A, float *B, float *C) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// 计算Sigmoid算子
if (idx < 1024) {
float temp0 = sigmoidf(A[idx]);
buffer0[idx] = temp0;
}
//为保证正确的数据依赖引入的同步操作
GlobalSync();
//计算矩阵乘法算子
for (int tix = bx; tix < ey; tix += offx) {
for (int tiy = by; tiy < ey; tiy += offy) {
MatMul(buffer0, B, buffer1, 1024, 1024, 128);
}
}
//计算Relu算子
if (idx < 1024) {
float temp2 = reluf(temp1);
C[idx] = temp2;
}
}
```
上面的实现可以看出有很大的局限性和问题，例如这种方法打破了现有的模块化设计，内核融合过程需要对每个算子的内核函数有一定要求，并需进行二次修改，还需要获取其一些额外的隐式参数：如threadBlock的个数，大小等。更进一步，这种方法也引入了一些“非标准”的GPU用法，如在kernel内部做全局同步可能会引入死锁的问题
。 尽管有学术界可以使用持久化线程（Persistent threads）的方法来实现同步，但这种方法和GPU有较强的绑定，无法把优化过程通用化到其它硬件上，有大量GPU相关的实现细节混在其中。
## 5.5.2 编译时全局算子调度
为了更好的解决这种问题，我们就是需要一种能根据计算流图中的并行度以及算子内部的并行度的整体信息来进行一个全局的任务调度方法。本章中以Rammer的技术为例，介绍一种全局算子调度的优化方法。
首先，在计算表达层，为了打开现有算子的黑盒实现，Rammer引入rOperator来代替原有算子抽象，暴露出每一个算子中的所有并行任务（rTask）。
在硬件层，引入虚拟设备（vDevice）的抽象，并提供计算单元（vEU）级别的的粒度调度接口，中以允许将一个rTask调度到任意指定的vEU上。然而，rTask粒度的调度可能带来更严重的调度开销，Rammer利用DNN计算性能有较强的确定性，即算子的计算时间在数据流图运行前就可以通过测量得到。因此，在编译时可以将整个数据流图的所有rTask静态的编排成一个确定性执行方案，通过vDevice映射到物理Device的执行单元进行执行，如图5-5-2所示。
<center> <img src="./img/5-5-2-co.png" /></center>
<center>图5-5-2. 全局算子调度编译</center>
这种全局调度的抽象解耦了调度机制与优化策略，通过暴露出rTask粒度的调度接口，从而可以基于该接口设计任意的编排方案来优化整体性能。
## 小结与讨论
本章我们主要通过介绍了全局算子调度层能解决的一些编译优化问题，我们首先通过扩展前面介绍过的算子融合的方法来解决该问题，并讨论了一些不足，进而介绍了以Rammer为例的全局编译时调度优化等内容。
请读者思考为何进行编译时调度而不是运行时调度？如何实现运行时调度最可行的方案是什么？
## 参考文献
1. Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks
2. Juggler: A dependence-aware task-based execution framework for gpus
3. Clipper: A low-latency online prediction serving system
4. A study of persistent threads style gpu programming for gpgpu workloads.
5. Enabling and exploiting flexible task assignment on gpu through sm-centric program transformations.
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<!--Copyright © Microsoft Corporation. All rights reserved.
适用于[License](https://github.com/microsoft/AI-System/blob/main/LICENSE)版权许可-->
# 深度学习推理系统 (Deep Learning Inference Systems)
# 简介
推理系统（Inference System）是用于部署人工智能模型，执行推理预测任务的人工智能系统，类似传统 Web 服务或移动端应用系统的作用。通过推理系统，可以将深度学习模型部署到云（Cloud）端或者边缘（Edge）端，并服务和处理用户的请求。模型训练过程好比是传统软件工程中的代码开发的过程，而开发完的代码势必要打包，部署给用户使用，那么推理系统就负责应对模型部署和服务生命周期中遇到的挑战和问题。
当推理系统将完成训练的模型进行部署和服务时，需要考虑设计和提供模型压缩，负载均衡，请求调度，加速优化，多副本和生命周期管理等支持。相比深度学习框架等为训练而设计的系统，推理系统不仅关注低延迟，高吞吐，可靠性等设计目标，同时受到资源，服务等级协议（Service-Level Agreement），功耗等约束。本章将围绕深度学习推理系统的设计，实现与优化内容展开，同时还会在最后部分介绍部署和 MLOps 等内容。
# 内容概览
本章将围绕以下内容展开：
- [8.1 推理系统简介](8.1-推理系统简介)
- [8.2 模型推理的离线优化](8.2-模型推理的离线优化.md)
- [8.3 部署](8.3-部署.md)
- [8.4 推理系统的运行期优化](8.4-推理系统的运行期优化.md)
- [8.5 开发、训练与部署的全生命周期管理-MLOps](8.5-开发、训练与部署的全生命周期管理-MLOps.md)
- [8.6 推理专有芯片](8.6-推理专有芯片.md)
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| 13 |
parameters:
Agent: Hosted Ubuntu 1604
stageName: 'defaultStageName'
jobDisplayName: 'defaultDisplayName'
jobTimeoutInMinutes: 180
TridentWorkloadTypeShort: #
DeployLocation: #
TestPostfix: # "" | "-release" | "-preview"
Deploy_Location_Short: #
DefaultWorkingDirectory: #
Template: #
azureSubscription: #
azure_subscription: #
aksimagename: #
ProjectLocation: #
PythonPath: #
cluster_name: #
flighting_release: false
flighting_preview: false
doCleanup: False
stages:
- stage: ${{parameters.stageName}}
dependsOn: []
jobs:
- job: deploy_notebook_steps
displayName: ${{parameters.jobDisplayName}}
pool:
name: ${{parameters.Agent}}
timeoutInMinutes: ${{parameters.jobTimeoutInMinutes}}
workspace:
clean: all
variables:
AIResourceGroupName: ${{parameters.TridentWorkloadTypeShort}}-${{parameters.DeployLocation}}${{parameters.TestPostfix}}
aks_name: ${{parameters.TridentWorkloadTypeShort}}${{parameters.TestPostfix}}
cluster_name: ${{parameters.TridentWorkloadTypeShort}}${{parameters.TestPostfix}}
python_path: ${{parameters.DefaultWorkingDirectory}}${{parameters.PythonPath}}
steps:
- template: ../steps/deploy_notebook_steps.yml
parameters:
template: ${{parameters.Template}}
azureSubscription: ${{parameters.azureSubscription}}
azure_subscription: ${{parameters.azure_subscription}}
azureresourcegroup: ${{variables.AIResourceGroupName}}
workspacename: ${{parameters.TridentWorkloadTypeShort}}-${{parameters.DeployLocation}}
azureregion: ${{parameters.DeployLocation}}
aksimagename: ${{parameters.aksimagename}}
aks_name: ${{variables.aks_name}}
location: ${{parameters.ProjectLocation}}
python_path: ${{variables.python_path}}
cluster_name: ${{variables.cluster_name}}
flighting_release: ${{parameters.flighting_release}}
flighting_preview: ${{parameters.flighting_preview}}
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/stage/deploy_notebooks_stage.yml/0
|
{
"file_path": "AI/.ci/stage/deploy_notebooks_stage.yml",
"repo_id": "AI",
"token_count": 780
}
| 14 |
parameters:
azureSubscription: ''
azure_subscription: ''
location: submodules/DeployDeepModelKubernetes/{{cookiecutter.project_name}}/Keras_Tensorflow
azureresourcegroup: dcibhpdl
workspacename: dcibhpwsdl
azureregion: westus2
aksimagename: dcibhpaksdl
aks_name: dcibhpaksdl
aks_service_name: dcibhpaksdlapi
conda: deployment_aml
doCleanup: true
python_path: "$(System.DefaultWorkingDirectory)/submodules/DeployDeepModelKubernetes/{{cookiecutter.project_name}}/Keras_Tensorflow"
flighting_release: false
flighting_preview: false
flighting_master: false
steps:
- template: config_conda.yml
parameters:
conda_location: ${{parameters.location}}
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: 00_AMLSetup.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
azure_subscription: ${{parameters.azure_subscription}}
azureresourcegroup: ${{parameters.azureresourcegroup}}
workspacename: ${{parameters.workspacename}}
azureregion: ${{parameters.azureregion}}
aksimagename: ${{parameters.aksimagename}}
- template: azpapermill.yml
parameters:
notebook: 01_DevelopModel.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
- template: azpapermill.yml
parameters:
notebook: 02_DevelopModelDriver.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
- template: azpapermill.yml
parameters:
notebook: 03_BuildImage.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
- template: azpapermill.yml
parameters:
notebook: aks/04_DeployOnAKS.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
aks_name: ${{parameters.aks_name}}
aks_location: ${{parameters.azureregion}}
aks_service_name: ${{parameters.aks_service_name}}
python_path: ${{parameters.python_path}}
- template: azpapermill.yml
parameters:
notebook: aks/05_TestWebApp.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
aks_name: ${{parameters.aks_name}}
aks_location: ${{parameters.azureregion}}
aks_service_name: ${{parameters.aks_service_name}}
python_path: ${{parameters.python_path}}
- template: azpapermill.yml
parameters:
notebook: aks/06_SpeedTestWebApp.ipynb
location: ${{parameters.location}}
azureSubscription: ${{parameters.azureSubscription}}
conda: ${{parameters.conda}}
aks_name: ${{parameters.aks_name}}
aks_location: ${{parameters.azureregion}}
aks_service_name: ${{parameters.aks_service_name}}
python_path: ${{parameters.python_path}}
- template: cleanuptask.yml
parameters:
azureSubscription: ${{parameters.azureSubscription}}
conda: deployment_aml
azureresourcegroup: ${{parameters.azureresourcegroup}}
doCleanup: ${{parameters.doCleanup}}
|
AI/.ci/steps/DLAKSDeployAMLJob.yml/0
|
{
"file_path": "AI/.ci/steps/DLAKSDeployAMLJob.yml",
"repo_id": "AI",
"token_count": 1285
}
| 15 |
parameters:
azureSubscription: 'AICAT-VB-E2E (989b90f7-da4f-41f9-84c9-44848802052d)'
conda: MLAKSDeployAML
azureresourcegroup: "-"
doCleanup : true
steps:
- task: AzureCLI@1
displayName: "Cleanup Task"
condition: and(always(), eq('${{parameters.doCleanup}}','true'))
inputs:
azureSubscription: ${{parameters.azureSubscription}}
scriptLocation: inlineScript
inlineScript: |
echo Execute Resource Group Delete
existResponse=$(az group exists -n ${{parameters.azureresourcegroup}})
if [ "$existResponse" == "true" ]; then
echo Deleting project resource group
az group delete --name ${{parameters.azureresourcegroup}} --yes --no-wait
else
echo Project resource group did not exist
fi
echo Done Cleanup
|
AI/.ci/steps/cleanuptask.yml/0
|
{
"file_path": "AI/.ci/steps/cleanuptask.yml",
"repo_id": "AI",
"token_count": 299
}
| 16 |
variables:
TridentWorkloadTypeShort: aidlrts
DeployLocation: eastus
ProjectLocation: "{{cookiecutter.project_name}}/Keras_Tensorflow"
PythonPath: /{{cookiecutter.project_name}}/Keras_Tensorflow
Template: DLAKSDeployAMLJob.yml
|
AI/.ci/vars/dl_realtime_scoring.yml/0
|
{
"file_path": "AI/.ci/vars/dl_realtime_scoring.yml",
"repo_id": "AI",
"token_count": 83
}
| 17 |
mkdir ../data/raw_data/
cd ../data/raw_data/
# download MSMARCO passage data
wget https://msmarco.blob.core.windows.net/msmarcoranking/collectionandqueries.tar.gz
tar -zxvf collectionandqueries.tar.gz
rm collectionandqueries.tar.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-passagetest2019-top1000.tsv.gz
gunzip msmarco-passagetest2019-top1000.tsv.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/top1000.dev.tar.gz
tar -zxvf top1000.dev.tar.gz
rm top1000.dev.tar.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/triples.train.small.tar.gz
tar -zxvf triples.train.small.tar.gz
rm triples.train.small.tar.gz
# download MSMARCO doc data
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-docs.tsv.gz
gunzip msmarco-docs.tsv.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-doctrain-queries.tsv.gz
gunzip msmarco-doctrain-queries.tsv.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-doctrain-qrels.tsv.gz
gunzip msmarco-doctrain-qrels.tsv.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-test2019-queries.tsv.gz
gunzip msmarco-test2019-queries.tsv.gz
wget https://trec.nist.gov/data/deep/2019qrels-docs.txt
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-doctest2019-top100.gz
gunzip msmarco-doctest2019-top100.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-docdev-top100.gz
gunzip msmarco-docdev-top100.gz
wget https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-docdev-queries.tsv.gz
gunzip msmarco-docdev-queries.tsv.gz
# clone DPR repo and download NQ and TriviaQA datasets
cd ../../../
git clone https://github.com/facebookresearch/DPR
cd DPR
python data/download_data.py --resource data.wikipedia_split.psgs_w100
python data/download_data.py --resource data.retriever.nq
python data/download_data.py --resource data.retriever.trivia
python data/download_data.py --resource data.retriever.qas.nq
python data/download_data.py --resource data.retriever.qas.trivia
python data/download_data.py --resource checkpoint.retriever.multiset.bert-base-encoder
|
ANCE/commands/data_download.sh/0
|
{
"file_path": "ANCE/commands/data_download.sh",
"repo_id": "ANCE",
"token_count": 844
}
| 18 |
This repository provides the fine-tuning stage on Marco ranking task for [SEED-Encoder](https://arxiv.org/abs/2102.09206) and is based on ANCE (https://github.com/microsoft/ANCE).
# Requirements and Installation
* [PyTorch](http://pytorch.org/) version >= 1.4.0
* Python version >= 3.6
## Requirements
To install requirements, run the following commands:
```setup
cd SEED_Encoder
python setup.py install
```
# Fine-tuning for SEED-Encoder
* We follow the ranking experiments in ANCE ([Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf) ) as our downstream tasks.
## Our Checkpoints
[Pretrained SEED-Encoder with 3-layer decoder, attention span = 2 ](https://fastbertjp.blob.core.windows.net/release-model/SEED-Encoder-3-decoder-layers.tar)
[Pretrained SEED-Encoder with 1-layer decoder, attention span = 8 ](https://fastbertjp.blob.core.windows.net/release-model/SEED-Encoder-1-decoder-layer.tar)
[SEED-Encoder warmup checkpoint](https://fastbertjp.blob.core.windows.net/release-model/SEED-Encoder-warmup-90000.tar)
[ANCE finetuned SEED-Encoder checkpoint on passage ranking task](https://fastbertjp.blob.core.windows.net/release-model/SEED-Encoder-pass-440000.tar)
[ANCE finetuned SEED-Encoder checkpoint on document ranking task](https://fastbertjp.blob.core.windows.net/release-model/SEED-Encoder-doc-800000.tar)
## Data Preprocessing
seq_length=512
tokenizer_type="seed-encoder"
base_data_dir={}
data_type={}
model_path={}
preprocessed_data_dir=${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/
preprocess_cmd="\
python ../data/msmarco_data.py --data_dir $base_data_dir --out_data_dir $preprocessed_data_dir --model_name_or_path $model_path --model_type seeddot_nll --max_seq_length $seq_length --data_type $data_type "
echo $preprocess_cmd
eval $preprocess_cmd
## Warmup for Training
DATA_DIR=../../data/raw_data
SAVE_DIR=../../temp/
LOAD_DIR=$your_path/SEED-Encoder-1-decoder-layer/
python3 -m torch.distributed.launch --nproc_per_node=8 ../drivers/run_warmup.py \
--train_model_type seeddot_nll --model_name_or_path $LOAD_DIR --task_name MSMarco --do_train \
--evaluate_during_training --data_dir $DATA_DIR \
--max_seq_length 128 --per_gpu_eval_batch_size=256 --per_gpu_train_batch_size=32 --learning_rate 2e-4 --logging_steps 100 --num_train_epochs 2.0 \
--output_dir $SAVE_DIR --warmup_steps 1000 --overwrite_output_dir --save_steps 20000 --gradient_accumulation_steps 1 --expected_train_size 35000000 \
--logging_steps_per_eval 100 --fp16 --optimizer lamb --log_dir $SAVE_DIR/log --do_lower_case --fp16
DATA_DIR=../../data/raw_data
SAVE_DIR=../../temp/
LOAD_DIR=$your_path/SEED-Encoder-3-decoder-layers/
python3 -m torch.distributed.launch --nproc_per_node=8 ../drivers/run_warmup.py \
--train_model_type seeddot_nll --model_name_or_path $LOAD_DIR --task_name MSMarco --do_train \
--evaluate_during_training --data_dir $DATA_DIR \
--max_seq_length 128 --per_gpu_eval_batch_size=256 --per_gpu_train_batch_size=32 --learning_rate 2e-4 --logging_steps 100 --num_train_epochs 2.0 \
--output_dir $SAVE_DIR --warmup_steps 1000 --overwrite_output_dir --save_steps 20000 --gradient_accumulation_steps 1 --expected_train_size 35000000 \
--logging_steps_per_eval 100 --fp16 --optimizer lamb --log_dir $SAVE_DIR/log --do_lower_case --fp16
## ANCE Training (passage, you may first use the second command to generate the initial data)
gpu_no=4
seq_length=512
tokenizer_type={}
model_type=seeddot_nll
base_data_dir={}
preprocessed_data_dir="${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/"
job_name=21_09_04_try
pretrained_checkpoint_dir=${you_model_dir}/SEED-Encoder-pass-440000/
data_type=1
warmup_steps=5000
per_gpu_train_batch_size=16
gradient_accumulation_steps=1
learning_rate=1e-6
model_dir="${base_data_dir}${job_name}/"
model_ann_data_dir="${model_dir}ann_data/"
CUDA_VISIBLE_DEVICES=4,5,6,7 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_dir}cache/" --data_dir $preprocessed_data_dir --max_seq_length $seq_length \
--per_gpu_eval_batch_size 64 --topk_training 200 --negative_sample 20
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=$gpu_no --master_addr 127.0.0.2 --master_port 35000 ../drivers/run_ann.py --model_type $model_type \
--model_name_or_path $pretrained_checkpoint_dir --task_name MSMarco --triplet --data_dir $preprocessed_data_dir \
--ann_dir $model_ann_data_dir --max_seq_length $seq_length --per_gpu_train_batch_size=$per_gpu_train_batch_size \
--gradient_accumulation_steps $gradient_accumulation_steps --learning_rate $learning_rate --output_dir $model_dir \
--warmup_steps $warmup_steps --logging_steps 100 --save_steps 100000000 --optimizer lamb --single_warmup --cache_dir "${model_dir}cache/" --do_lower_case
## ANCE Training (document)
gpu_no=4
seq_length=512
tokenizer_type={}
model_type=seeddot_nll
base_data_dir={}
preprocessed_data_dir="${base_data_dir}ann_data_${tokenizer_type}_${seq_length}/"
job_name=21_09_04_try2
pretrained_checkpoint_dir=${you_model_dir}/SEED-Encoder-doc-800000/
data_type=0
warmup_steps=3000
per_gpu_train_batch_size=4
gradient_accumulation_steps=4
learning_rate=5e-6
model_dir="${base_data_dir}${job_name}/"
model_ann_data_dir="${model_dir}ann_data/"
CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch --nproc_per_node=$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_dir}cache/" --data_dir $preprocessed_data_dir --max_seq_length $seq_length \
--per_gpu_eval_batch_size 16 --topk_training 200 --negative_sample 20
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=$gpu_no --master_addr 127.0.0.2 --master_port 35000 ../drivers/run_ann.py --model_type $model_type \
--model_name_or_path $pretrained_checkpoint_dir --task_name MSMarco --triplet --data_dir $preprocessed_data_dir \
--ann_dir $model_ann_data_dir --max_seq_length $seq_length --per_gpu_train_batch_size=$per_gpu_train_batch_size \
--gradient_accumulation_steps $gradient_accumulation_steps --learning_rate $learning_rate --output_dir $model_dir \
--warmup_steps $warmup_steps --logging_steps 100 --save_steps 100000000 --optimizer lamb --single_warmup --cache_dir "${model_dir}cache/" --do_lower_case
|
ANCE/model/SEED_Encoder/SEED-Encoder.md/0
|
{
"file_path": "ANCE/model/SEED_Encoder/SEED-Encoder.md",
"repo_id": "ANCE",
"token_count": 2893
}
| 19 |
"""
This is official eval script opensourced on MSMarco site (not written or owned by us)
This module computes evaluation metrics for MSMARCO dataset on the ranking task.
Command line:
python msmarco_eval_ranking.py <path_to_reference_file> <path_to_candidate_file>
Creation Date : 06/12/2018
Last Modified : 1/21/2019
Authors : Daniel Campos <[email protected]>, Rutger van Haasteren <[email protected]>
"""
import sys
import statistics
from collections import Counter
MaxMRRRank = 10
def load_reference_from_stream(f):
"""Load Reference reference relevant passages
Args:f (stream): stream to load.
Returns:qids_to_relevant_passageids (dict): dictionary mapping from query_id (int) to relevant passages (list of ints).
"""
qids_to_relevant_passageids = {}
for l in f:
try:
l = l.strip().split('\t')
qid = int(l[0])
if qid in qids_to_relevant_passageids:
pass
else:
qids_to_relevant_passageids[qid] = []
qids_to_relevant_passageids[qid].append(int(l[2]))
except:
raise IOError('\"%s\" is not valid format' % l)
return qids_to_relevant_passageids
def load_reference(path_to_reference):
"""Load Reference reference relevant passages
Args:path_to_reference (str): path to a file to load.
Returns:qids_to_relevant_passageids (dict): dictionary mapping from query_id (int) to relevant passages (list of ints).
"""
with open(path_to_reference,'r') as f:
qids_to_relevant_passageids = load_reference_from_stream(f)
return qids_to_relevant_passageids
def load_candidate_from_stream(f):
"""Load candidate data from a stream.
Args:f (stream): stream to load.
Returns:qid_to_ranked_candidate_passages (dict): dictionary mapping from query_id (int) to a list of 1000 passage ids(int) ranked by relevance and importance
"""
qid_to_ranked_candidate_passages = {}
for l in f:
try:
l = l.strip().split('\t')
qid = int(l[0])
pid = int(l[1])
rank = int(l[2])
if qid in qid_to_ranked_candidate_passages:
pass
else:
# By default, all PIDs in the list of 1000 are 0. Only override those that are given
tmp = [0] * 1000
qid_to_ranked_candidate_passages[qid] = tmp
qid_to_ranked_candidate_passages[qid][rank-1]=pid
except:
raise IOError('\"%s\" is not valid format' % l)
return qid_to_ranked_candidate_passages
def load_candidate(path_to_candidate):
"""Load candidate data from a file.
Args:path_to_candidate (str): path to file to load.
Returns:qid_to_ranked_candidate_passages (dict): dictionary mapping from query_id (int) to a list of 1000 passage ids(int) ranked by relevance and importance
"""
with open(path_to_candidate,'r') as f:
qid_to_ranked_candidate_passages = load_candidate_from_stream(f)
return qid_to_ranked_candidate_passages
def quality_checks_qids(qids_to_relevant_passageids, qids_to_ranked_candidate_passages):
"""Perform quality checks on the dictionaries
Args:
p_qids_to_relevant_passageids (dict): dictionary of query-passage mapping
Dict as read in with load_reference or load_reference_from_stream
p_qids_to_ranked_candidate_passages (dict): dictionary of query-passage candidates
Returns:
bool,str: Boolean whether allowed, message to be shown in case of a problem
"""
message = ''
allowed = True
# Create sets of the QIDs for the submitted and reference queries
candidate_set = set(qids_to_ranked_candidate_passages.keys())
ref_set = set(qids_to_relevant_passageids.keys())
# Check that we do not have multiple passages per query
for qid in qids_to_ranked_candidate_passages:
# Remove all zeros from the candidates
duplicate_pids = set([item for item, count in Counter(qids_to_ranked_candidate_passages[qid]).items() if count > 1])
if len(duplicate_pids-set([0])) > 0:
message = "Cannot rank a passage multiple times for a single query. QID={qid}, PID={pid}".format(
qid=qid, pid=list(duplicate_pids)[0])
allowed = False
return allowed, message
def compute_metrics(qids_to_relevant_passageids, qids_to_ranked_candidate_passages):
"""Compute MRR metric
Args:
p_qids_to_relevant_passageids (dict): dictionary of query-passage mapping
Dict as read in with load_reference or load_reference_from_stream
p_qids_to_ranked_candidate_passages (dict): dictionary of query-passage candidates
Returns:
dict: dictionary of metrics {'MRR': <MRR Score>}
"""
all_scores = {}
MRR = 0
qids_with_relevant_passages = 0
ranking = []
for qid in qids_to_ranked_candidate_passages:
if qid in qids_to_relevant_passageids:
ranking.append(0)
target_pid = qids_to_relevant_passageids[qid]
candidate_pid = qids_to_ranked_candidate_passages[qid]
for i in range(0,MaxMRRRank):
if candidate_pid[i] in target_pid:
MRR += 1/(i + 1)
ranking.pop()
ranking.append(i+1)
break
if len(ranking) == 0:
raise IOError("No matching QIDs found. Are you sure you are scoring the evaluation set?")
MRR = MRR/len(qids_to_relevant_passageids)
all_scores['MRR @10'] = MRR
all_scores['QueriesRanked'] = len(qids_to_ranked_candidate_passages)
return all_scores
def compute_metrics_from_files(path_to_reference, path_to_candidate, perform_checks=True):
"""Compute MRR metric
Args:
p_path_to_reference_file (str): path to reference file.
Reference file should contain lines in the following format:
QUERYID\tPASSAGEID
Where PASSAGEID is a relevant passage for a query. Note QUERYID can repeat on different lines with different PASSAGEIDs
p_path_to_candidate_file (str): path to candidate file.
Candidate file sould contain lines in the following format:
QUERYID\tPASSAGEID1\tRank
If a user wishes to use the TREC format please run the script with a -t flag at the end. If this flag is used the expected format is
QUERYID\tITER\tDOCNO\tRANK\tSIM\tRUNID
Where the values are separated by tabs and ranked in order of relevance
Returns:
dict: dictionary of metrics {'MRR': <MRR Score>}
"""
qids_to_relevant_passageids = load_reference(path_to_reference)
qids_to_ranked_candidate_passages = load_candidate(path_to_candidate)
if perform_checks:
allowed, message = quality_checks_qids(qids_to_relevant_passageids, qids_to_ranked_candidate_passages)
if message != '': print(message)
return compute_metrics(qids_to_relevant_passageids, qids_to_ranked_candidate_passages)
def main():
"""Command line:
python msmarco_eval_ranking.py <path_to_reference_file> <path_to_candidate_file>
"""
print("Eval Started")
if len(sys.argv) == 3:
path_to_reference = sys.argv[1]
path_to_candidate = sys.argv[2]
metrics = compute_metrics_from_files(path_to_reference, path_to_candidate)
print('#####################')
for metric in sorted(metrics):
print('{}: {}'.format(metric, metrics[metric]))
print('#####################')
else:
print('Usage: msmarco_eval_ranking.py <reference ranking> <candidate ranking>')
exit()
if __name__ == '__main__':
main()
|
ANCE/utils/msmarco_eval.py/0
|
{
"file_path": "ANCE/utils/msmarco_eval.py",
"repo_id": "ANCE",
"token_count": 3200
}
| 20 |
"""
Code for self-training with weak supervision.
Author: Giannis Karamanolakis ([email protected])
"""
import json
import os
import glob
import numpy as np
import joblib
from collections import defaultdict
#import matplotlib.pyplot as plt
# Help functions used in main.py
def to_one_hot(x, num_classes):
targets = np.array([x]).reshape(-1)
return np.eye(num_classes)[targets]
def evaluate_ran(model, dataset, evaluator, comment="test"):
pred_dict = model.predict_ran(dataset=dataset)
res = evaluator.evaluate(preds=pred_dict['preds'],
labels=dataset.data['labels'],
proba=pred_dict['proba'],
comment=comment)
return res, pred_dict
def evaluate(model, dataset, evaluator, comment="test"):
pred_dict = model.predict(dataset=dataset)
res = evaluator.evaluate(preds=pred_dict['preds'],
labels=dataset.data['labels'],
proba=pred_dict['proba'],
comment=comment)
return res
def evaluate_test(model, dataset, evaluator, comment="test"):
pred_dict = model.predict(dataset=dataset)
res = evaluator.evaluate(preds=pred_dict['preds'],
labels=dataset.data['labels'],
proba=pred_dict['proba'],
comment=comment)
return res, pred_dict
def save_and_report_results(args, results, logger):
logger.info("\t*** Final Results ***")
for res, values in results.items():
logger.info("\n{}:\t{}".format(res, values))
savepath = os.path.join(args.logdir, 'results.pkl')
logger.info('Saving results at {}'.format(savepath))
joblib.dump(results, savepath)
txt_savepath = os.path.join(args.logdir, 'results.txt')
with open(txt_savepath, 'w') as f:
def pinfo(text):
logger.info(text)
f.write(text + "\n")
pinfo("Dataset: {}".format(args.dataset))
pinfo("Weak Sources: {}".format(args.weak_sources))
pinfo("Model: {}\n".format(args.student_name))
if 'teacher_train' in results:
pinfo("Teacher Train {}: {:.1f}".format(args.metric, results['teacher_train']['perf']))
if 'teacher_dev' in results:
pinfo("Teacher Dev {}: {:.1f}".format(args.metric, results['teacher_dev']['perf']))
if 'teacher_test' in results:
pinfo("Teacher Test {}: {:.1f}\n".format(args.metric, results['teacher_test']['perf']))
if 'student_dev' in results:
pinfo("Student Dev {}: {:.1f}".format(args.metric, results['student_dev']['perf']))
if 'student_test' in results:
pinfo("Student Test {}: {:.1f}".format(args.metric, results['student_test']['perf']))
args_savepath = os.path.join(args.logdir, 'args.json')
with open(args_savepath, 'w') as f:
args.device = -1
json.dump(vars(args), f)
logger.info('Saved report at {}'.format(txt_savepath))
return
def get_results(resfolder):
params_file = os.path.join(resfolder, 'args.json')
with open(params_file, 'r') as f:
data = json.load(f)
metric = data['metric']
results_file = os.path.join(resfolder, 'results.pkl')
res = joblib.load(results_file)
if 'teacher_dev_iter' in res:
teacher_all_dev = [x['perf'] for x in res['teacher_dev_iter']]
teacher_all_test = [x['perf'] for x in res['teacher_test_iter']]
teacher_all_dev_acc = [x['acc'] for x in res['teacher_dev_iter']]
teacher_all_test_acc = [x['acc'] for x in res['teacher_test_iter']]
teacher_perf_str = ["{}:\t{:.2f}\t{:.2f}".format(i, teacher_all_dev[i], teacher_all_test[i]) for i in np.arange(len(teacher_all_dev))]
print("TEACHER performance for each iteration:\n{}".format("\n".join(teacher_perf_str)))
else:
teacher_all_dev, teacher_all_test, teacher_all_dev_acc, teacher_all_test_acc, teacher_perf_str = [0], [0], [0], [0], ''
student_all_dev = [x['perf'] for x in res['student_dev_iter']]
student_all_test = [x['perf'] for x in res['student_test_iter']]
student_all_dev_acc = [x['acc'] for x in res['student_dev_iter']]
student_all_test_acc = [x['acc'] for x in res['student_test_iter']]
student_perf_str = ["{}:\t{:.2f}\t{:.2f}".format(i, student_all_dev[i], student_all_test[i]) for i in np.arange(len(student_all_dev))]
print("STUDENT performance for each iteration:\n{}".format("\n".join(student_perf_str)))
return {
'teacher_dev_perf': teacher_all_dev,
'teacher_test_perf': teacher_all_test,
'student_dev_perf': student_all_dev,
'student_test_perf': student_all_test,
'teacher_dev_acc': teacher_all_dev_acc,
'teacher_test_acc': teacher_all_test_acc,
'student_dev_acc': student_all_dev_acc,
'student_test_acc': student_all_test_acc,
'metric': metric
}
def summarize_results(basefolder, dataset):
seedfolders = glob.glob(basefolder + '/*seed*')
print("parsing {} seed folders".format(len(seedfolders)))
savefile = os.path.join(basefolder, 'all_res.txt')
with open(savefile, 'w') as f:
def pinfo(text):
print(text)
f.write(text + "\n")
all_teacher_perf = []
all_student_perf = []
all_teacher_dev_perf = []
all_student_dev_perf = []
best_students = []
best_teachers = []
student_iters = []
teacher_iters = []
best_students_acc = []
best_teachers_acc = []
for seedfolder in seedfolders:
print(seedfolder)
resfolders = glob.glob(seedfolder + '/*')
if len(resfolders) > 1:
print('found more than 1 folders.. multiple experiments..')
print(resfolders)
return "multiple folders"
resfolder = resfolders[0]
res = get_results(resfolder)
all_teacher_perf.append(res['teacher_test_perf'])
all_student_perf.append(res['student_test_perf'])
all_teacher_perf.append(res['teacher_test_perf'])
all_student_perf.append(res['student_test_perf'])
all_teacher_dev_perf.append(res['teacher_dev_perf'])
all_student_dev_perf.append(res['student_dev_perf'])
metric = res['metric']
print("results for {}".format(seedfolder.split('/')[-1]))
best_student_iter = np.argmax(res['student_dev_perf'])
best_teacher_iter = np.argmax(res['teacher_dev_perf'])
best_student = res['student_test_perf'][best_student_iter]
best_teacher = res['teacher_test_perf'][best_teacher_iter]
pinfo("Best Student: {:.2f} (iter={})".format(best_student, best_student_iter))
pinfo("Best Teacher: {:.2f} (iter={})".format(best_teacher, best_teacher_iter))
best_students.append(best_student)
best_teachers.append(best_teacher)
best_students_acc.append(res['student_test_acc'][best_student_iter])
best_teachers_acc.append(res['teacher_test_acc'][best_teacher_iter])
student_iters.append(best_student_iter)
teacher_iters.append(best_teacher_iter)
avg_teacher_perf = np.average(all_teacher_perf, axis=0)
std_teacher_perf = np.std(all_teacher_perf, axis=0)
avg_student_perf = np.average(all_student_perf, axis=0)
std_student_perf = np.std(all_student_perf, axis=0)
avg_teacher_dev_perf = np.average(all_teacher_dev_perf, axis=0)
# std_teacher_dev_perf = np.std(all_teacher_dev_perf, axis=0)
avg_student_dev_perf = np.average(all_student_dev_perf, axis=0)
# std_student_dev_perf = np.std(all_student_dev_perf, axis=0)
num_iter = len(avg_student_perf)
# iters = np.arange(num_iter)
best_student_iter = np.argmax(avg_student_dev_perf)
best_teacher_iter = np.argmax(avg_teacher_dev_perf)
pinfo("\n\nAverage results across splits")
if 'acc' in metric:
pinfo("Best Student: {:.2f} (std={:.2f}, iter={})".format(np.average(best_students), np.std(best_students),
int(np.average(student_iters))))
pinfo("Best Teacher: {:.2f} (std={:.2f}, iter={})".format(np.average(best_teachers), np.std(best_teachers),
best_teacher_iter,
int(np.average(teacher_iters))))
else:
pinfo("Best Student: {:.2f} (std={:.2f}, iter={}), acc={:.2f}, std={:.2f}".format(np.average(best_students),
np.std(best_students),
int(np.average(
student_iters)),
np.average(
best_students_acc),
np.std(
best_students_acc)))
pinfo("Best Teacher: {:.2f} (std={:.2f}, iter={}), acc={:.2f}, std={:.2f}".format(np.average(best_teachers),
np.std(best_teachers),
int(np.average(
teacher_iters)),
np.average(
best_teachers_acc),
np.std(
best_teachers_acc)))
"""
# plot performance across iterations
plt.figure()
plt.title(dataset)
if len(avg_teacher_perf) > 1:
plt.errorbar(iters, avg_teacher_perf, std_teacher_perf, linestyle='-', marker='^', label='teacher')
plt.fill_between(iters, avg_teacher_perf - std_teacher_perf, avg_teacher_perf + std_teacher_perf,
facecolor='#F0F8FF', edgecolor='#8F94CC', alpha=1.0)
plt.errorbar(iters, avg_student_perf, std_student_perf, linestyle='-', marker='^', label='student')
plt.fill_between(iters, avg_student_perf - std_student_perf, avg_student_perf + std_student_perf,
facecolor='#F0F8FF', edgecolor='#bc5a45', alpha=1.0)
plt.legend(loc='lower right')
plt.xlabel('iter')
plt.ylabel(metric)
plt.show()
"""
print('avg_student_perf["{}"] = [{}]'.format(dataset, ', '.join(['{:.2f}'.format(x) for x in avg_student_perf])))
print('std_student_perf["{}"] = [{}]'.format(dataset, ', '.join(['{:.2f}'.format(x) for x in std_student_perf])))
print('\n\navg_teacher_perf["{}"] = [{}]'.format(dataset, ', '.join(['{:.2f}'.format(x) for x in avg_teacher_perf])))
print('std_teacher_perf["{}"] = [{}]'.format(dataset, ', '.join(['{:.2f}'.format(x) for x in std_teacher_perf])))
print("results stored at {}".format(savefile))
return {
'student_perf': avg_student_perf[best_student_iter],
'student_std': std_student_perf[best_student_iter],
'teacher_perf': avg_teacher_perf[best_teacher_iter],
'teacher_std': std_teacher_perf[best_teacher_iter],
}
def analyze_rule_attention_scores(res, logger, savefolder, name='test', verbose=False):
if not 'att_scores' in res or res['att_scores'] is None:
return
preds = res['preds']
proba = res['proba']
att_scores = res['att_scores']
rule_mask = res['rule_mask']
rule_ids = [(np.nonzero(x)[0]).tolist() for x in rule_mask]
rule_att_scores = [att_score_list[:len(rule_ids[i])].tolist() for i, att_score_list in enumerate(att_scores)]
flat_att_scores = [score for scores in rule_att_scores for score in scores]
low_scores = [score for score in flat_att_scores if score < 0.5]
rule2att = defaultdict(list)
for i, rids in enumerate(rule_ids):
for j, rid in enumerate(rids):
rule2att[rid].append(rule_att_scores[i][j])
if verbose:
for rule, scores in sorted(rule2att.items()):
logger.info("Rule {}: lower att scores = {}, higher att scores = {}".format(rule, sorted(scores)[:10], sorted(scores)[-10:]))
for rule, scores in sorted(rule2att.items()):
logger.info("Rule {}: min={:.2f}\tmax={:.2f}\tmean={:.2f}\tmedian={:.2f}\tsupport={:.2f}".format(rule, min(scores), max(scores),
np.average(scores), np.median(scores), len(scores)))
logger.info("First 100 rules :\n{}".format(rule_ids[:100]))
logger.info("First 100 rule attention scores:\n{}".format([float("{:.1f}".format(x)) for x in flat_att_scores[:100]]))
logger.info("Low scores ({}/{})\t{}".format(len(low_scores), len(flat_att_scores), [float("{:.1f}".format(x)) for x in low_scores[:100]]))
savefolder = os.path.join(savefolder, 'teacher_dump')
logger.info("Saving attention scores at {}".format(savefolder))
os.makedirs(savefolder, exist_ok=True)
savefile = os.path.join(savefolder, 'att_scores_{}.pkl'.format(name))
joblib.dump(att_scores, savefile)
savefile = os.path.join(savefolder, 'teacher_preds_{}.pkl'.format(name))
joblib.dump(preds, savefile)
savefile = os.path.join(savefolder, 'teacher_proba_{}.pkl'.format(name))
joblib.dump(proba, savefile)
savefile = os.path.join(savefolder, 'rule_mask_{}.pkl'.format(name))
joblib.dump(rule_mask, savefile)
savefile = os.path.join(savefolder, 'rule2att_dict_{}.pkl'.format(name))
joblib.dump(rule2att, savefile)
return
|
ASTRA/astra/utils.py/0
|
{
"file_path": "ASTRA/astra/utils.py",
"repo_id": "ASTRA",
"token_count": 7622
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},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport's runway name is \"18R/36L\".",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"18R/36L\"",
"property": "runwayName",
"subject": "Allama_Iqbal_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"18R/36L\"",
"property": "runwayDesignation",
"subject": "Allama_Iqbal_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"13": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The runway length of Alpena County Regional Airport is 1,533.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Alpena County Regional airport is 1533.0.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1533.0",
"property": "runwayLength",
"subject": "Alpena_County_Regional_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1532.53\"^^xsd:double",
"property": "runwayLength",
"subject": "Alpena_County_Regional_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"14": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alpena Country Regional Airport runway name is 1/19/.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "1/19 is the runway name of Alpena County Regional Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name of Alpena County Regional Airport is 1/19.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"1/19\"",
"property": "runwayName",
"subject": "Alpena_County_Regional_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1/19\"",
"property": "runwayDesignation",
"subject": "Alpena_County_Regional_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id14"
}
},
{
"15": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schiphol serves the city of Amsterdam.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam airport, Schipol serves the city of Amsterdam.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam Airport Schuphol serves Amsterdam.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "city",
"subject": "Amsterdam_Airport_Schiphol"
}
],
[
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id15"
}
},
{
"16": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County, Texas is country seat to Andrews, Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews, Texas is the country seat of Andrews County, Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County, Texas has its county seat in Andrews, Texas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Andrews,_Texas",
"property": "countySeat",
"subject": "Andrews_County,_Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Andrews,_Texas",
"property": "countySeat",
"subject": "Andrews_County,_Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id16"
}
},
{
"17": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews Country Airport's city is served by Andrews, Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport serves Andrews, Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport serves the city of Andrews in Texas.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Andrews,_Texas",
"property": "cityServed",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Andrews,_Texas",
"property": "city",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
}
},
{
"18": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews Country Airport runway name is 11/29.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "11/29 is the runway name of Andrews County Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway name of Andrews County Airport is 11/29.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"11/29\"",
"property": "runwayName",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"11/29\"",
"property": "runwayDesignation",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id18"
}
},
{
"19": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The name of the runway at Andrews County Airport is Helipad.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Helipad is the runway name of Andrews County Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The runway at Andrews County Airport is called Helipad.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Helipad\"",
"property": "runwayName",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Helipad\"",
"property": "runwayDesignation",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id19"
}
},
{
"20": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport city served is Luanda.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport serves the city of Luanda.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport serves Luanda.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Luanda",
"property": "city",
"subject": "Angola_International_Airport"
}
],
[
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id20"
}
},
{
"21": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Ardmore Airport, New Zealand is in Auckland.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport is located in Auckland, New Zealand.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Auckland",
"property": "location",
"subject": "Ardmore_Airport_(New_Zealand)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Auckland",
"property": "location",
"subject": "Ardmore_Airport_(New_Zealand)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
}
},
{
"22": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Athens mayor is Giorgos Kaminis.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Giorgos Kaminis is the mayor of Athens.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The mayor of Athens is Giorgos Kaminis.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Giorgos_Kaminis",
"property": "mayor",
"subject": "Athens"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Giorgos_Kaminis",
"property": "mayor",
"subject": "Athens"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
}
},
{
"23": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport is in Spata.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport is located in Spata.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spata",
"property": "location",
"subject": "Athens_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spata",
"property": "location",
"subject": "Athens_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id23"
}
},
{
"24": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Belgium language is German language.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "German is the language of Belgium.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The language spoken is Belgium is German.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "German_language",
"property": "language",
"subject": "Belgium"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "German_language",
"property": "language",
"subject": "Belgium"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id24"
}
},
{
"25": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Philippe of Belgium is the leader of Belgium.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of Belgium is Philippe of Belgium.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of Belgium is Phillipe of Belgium.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippe_of_Belgium",
"property": "leaderName",
"subject": "Belgium"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippe_of_Belgium",
"property": "leaderName",
"subject": "Belgium"
}
],
[
{
"object": "Philippe_of_Belgium",
"property": "leader",
"subject": "Belgium"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id25"
}
},
{
"26": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The capital of Denmark is Copenhagen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Copenhagen is the capital of Denmark.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Copenhagen",
"property": "capital",
"subject": "Denmark"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Copenhagen",
"property": "capital",
"subject": "Denmark"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id26"
}
},
{
"27": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Denmark's leader name is Lars Løkke Rasmussen.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lars Løkke Rasmussen leads Denmark.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Lars Løkke Rasmussen is the leader of Denmark.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lars_Løkke_Rasmussen",
"property": "leaderName",
"subject": "Denmark"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lars_Løkke_Rasmussen",
"property": "leader",
"subject": "Denmark"
}
],
[
{
"object": "Lars_Løkke_Rasmussen",
"property": "leaderName",
"subject": "Denmark"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id27"
}
},
{
"28": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Flemish region is led by the Flemish government.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Flemish Government leads the Flemish Region.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Flemish_Government",
"property": "leaderName",
"subject": "Flemish_Region"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Flemish_Government",
"property": "leaderName",
"subject": "Flemish_Region"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id28"
}
},
{
"29": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Greek is the language spoken in Greece.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The language spoken in Greece is Greek.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greek_language",
"property": "language",
"subject": "Greece"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Greek_language",
"property": "language",
"subject": "Greece"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id29"
}
},
{
"30": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Greece is Nikos Voutsis.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Nikos_Voutsis",
"property": "leaderName",
"subject": "Greece"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
}
],
[
{
"object": "Nikos_Voutsis",
"property": "leaderName",
"subject": "Greece"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id30"
}
},
{
"31": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Harrietstown, New York is part of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Harrietstown, N.Y. is part of the U.S.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "isPartOf",
"subject": "Harrietstown,_New_York"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "subdivisionName",
"subject": "Harrietstown,_New_York"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id31"
}
},
{
"32": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Pakistan is Anwar Zaheer Jamali.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Pakistan has a leader called Anwar Zaheer Jamali.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Anwar_Zaheer_Jamali",
"property": "leaderName",
"subject": "Pakistan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Anwar_Zaheer_Jamali",
"property": "leader",
"subject": "Pakistan"
}
],
[
{
"object": "Anwar_Zaheer_Jamali",
"property": "leaderName",
"subject": "Pakistan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id32"
}
},
{
"33": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Paracuellos de Jarama is part of the community of Madrid.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Community_of_Madrid",
"property": "isPartOf",
"subject": "Paracuellos_de_Jarama"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Community_of_Madrid",
"property": "isPartOf",
"subject": "Paracuellos_de_Jarama"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id33"
}
},
{
"34": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Port Authority of New York and New Jersey headquarter is Four World Trade Center.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Four World Trade Center is the headquarters of the Port Authority of New York and New Jersey.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Four_World_Trade_Center",
"property": "headquarter",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Four_World_Trade_Center",
"property": "headquarter",
"subject": "Port_Authority_of_New_York_and_New_Jersey"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id34"
}
},
{
"35": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "San Sebastián de los Reyes is in Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "San Sebastian de los Reyes is located in Spain.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "San_Sebastián_de_los_Reyes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "San_Sebastián_de_los_Reyes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id35"
}
},
{
"36": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Saranac Lake, New York is part of the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Saranac Lake, New York, is part of the United States.",
"xml_id": "Id2"
}
],
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},
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}
},
{
"61": {
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{
"comment": "good",
"lang": "",
"lex": "Ampara Hospital is located in Sri Lanka.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ampara Hospital is in Sri Lanka.",
"xml_id": "Id2"
}
],
"links": [],
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{
"object": "Sri_Lanka",
"property": "country",
"subject": "Ampara_Hospital"
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],
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{
"object": "Sri_Lanka",
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]
},
"shape": "NA",
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"size": "1",
"xml_id": "Id15"
}
},
{
"62": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asher and Mary Isabelle Richardson House was added to the National Register of Historic Places on \"1988-11-22\".",
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},
{
"comment": "good",
"lang": "",
"lex": "The Asher and Mary Isabelle Richardson House was added to the National Register of Historic Places, on 22nd November 1988.",
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}
],
"links": [],
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{
"object": "\"1988-11-22\"",
"property": "added to the National Register of Historic Places",
"subject": "Asher_and_Mary_Isabelle_Richardson_House"
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"object": "\"1988-11-22\"^^xsd:date",
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]
]
},
"shape": "NA",
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}
},
{
"63": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Asser Levy Public Baths was built in 1904.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asser Levy Public Baths were constructed in 1904.",
"xml_id": "Id2"
}
],
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"subject": "Asser_Levy_Public_Baths"
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{
"object": "1904",
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{
"object": "\"1904\"^^xsd:gYear",
"property": "yearOfConstruction",
"subject": "Asser_Levy_Public_Baths"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
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},
{
"64": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Birmingham has the postcode area 'B'.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The B postcode area is the postal code of Birmingham.",
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"object": "B_postcode_area",
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id18"
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},
{
"65": {
"category": "Building",
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{
"comment": "good",
"lang": "",
"lex": "Chicago is part of DuPage County in Illinois.",
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},
{
"comment": "good",
"lang": "",
"lex": "Chicago is part of DuPage County Illinois.",
"xml_id": "Id2"
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],
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id19"
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},
{
"66": {
"category": "Building",
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{
"comment": "good",
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"lex": "Dublin is in the Republic of Ireland.",
"xml_id": "Id1"
}
],
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{
"object": "Republic_of_Ireland",
"property": "country",
"subject": "Dublin"
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],
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"object": "Republic_of_Ireland",
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},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id20"
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},
{
"67": {
"category": "Building",
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{
"comment": "good",
"lang": "",
"lex": "Dublin is part of Leinster.",
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}
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"property": "isPartOf",
"subject": "Dublin"
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],
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{
"object": "Leinster",
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"subject": "Dublin"
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],
[
{
"object": "Leinster",
"property": "subdivisionName",
"subject": "Dublin"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
}
},
{
"68": {
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{
"comment": "good",
"lang": "",
"lex": "Sumitra Mahajan is an Indian leader.",
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},
{
"comment": "good",
"lang": "",
"lex": "The name of the leader in India is Sumitra Mahajan.",
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"object": "Sumitra_Mahajan",
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"subject": "India"
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{
"object": "Sumitra_Mahajan",
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"object": "Sumitra_Mahajan",
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"subject": "India"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
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},
{
"69": {
"category": "Building",
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"lexicalisations": [
{
"comment": "toFix",
"lang": "",
"lex": "One of the ethnic groups in Japan in the Brazilians.",
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},
{
"comment": "toFix",
"lang": "",
"lex": "The Brazilians in Japan are an ethnic group found in Japan.",
"xml_id": "Id2"
}
],
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"object": "Brazilians_in_Japan",
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"object": "Brazilians_in_Japan",
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},
"shape": "NA",
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"xml_id": "Id23"
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},
{
"70": {
"category": "Building",
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{
"comment": "good",
"lang": "",
"lex": "The leader of Japan is Tarō Asō.",
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}
],
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"object": "Tarō_Asō",
"property": "leaderName",
"subject": "Japan"
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],
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{
"object": "Tarō_Asō",
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},
"shape": "NA",
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},
{
"71": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bill Marriott is the key person at Marriott International.",
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},
{
"comment": "good",
"lang": "",
"lex": "One of the key people in Marriott International is Bill Marriott.",
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}
],
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"subject": "Marriott_International"
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],
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"object": "Bill_Marriott",
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[
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"object": "Bill_Marriott",
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"subject": "Marriott_International"
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id25"
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},
{
"72": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ireland official language is Irish English.",
"xml_id": "Id1"
},
{
"comment": "toFix",
"lang": "",
"lex": "English language is the main language of the Republic of Ireland.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "One language used in the Republic of Ireland is English.",
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],
"links": [],
"modifiedtripleset": [
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"object": "English_language",
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"subject": "Republic_of_Ireland"
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"object": "English_language",
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"object": "English_language",
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"subject": "Republic_of_Ireland"
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},
"shape": "NA",
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"xml_id": "Id26"
}
},
{
"73": {
"category": "Building",
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{
"comment": "good",
"lang": "",
"lex": "Cape Town is the capital of South Africa.",
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},
{
"comment": "good",
"lang": "",
"lex": "The capital of South Africa is Cape Town.",
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],
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"object": "Cape_Town",
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},
"shape": "NA",
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"xml_id": "Id27"
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},
{
"74": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The capital of the United Kingdom is London.",
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},
{
"comment": "good",
"lang": "",
"lex": "London is the capital of the United Kingdom.",
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}
],
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"object": "London",
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id28"
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},
{
"75": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Native Americans are an ethnic group in the United States.",
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}
],
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"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
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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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"size": "1",
"xml_id": "Id29"
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},
{
"76": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Paul Ryan is the leader of the United States.",
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},
{
"comment": "good",
"lang": "",
"lex": "The leader of the United States is Paul Ryan.",
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}
],
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"modifiedtripleset": [
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"object": "Paul_Ryan",
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"object": "Paul_Ryan",
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"object": "Paul_Ryan",
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},
"shape": "NA",
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"xml_id": "Id30"
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},
{
"77": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The area code of Abilene, in Texas, is 325.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "325 is the area code for Abilene, Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The area code for Abilene in Texas is 325.",
"xml_id": "Id3"
}
],
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"object": "325",
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"subject": "Abilene,_Texas"
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],
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{
"object": "325",
"property": "areaCode",
"subject": "Abilene,_Texas"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
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},
{
"78": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Abilene is a part of Texas.",
"xml_id": "Id1"
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{
"comment": "good",
"lang": "",
"lex": "Abilene, Texas, is part of Texas.",
"xml_id": "Id2"
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],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "isPartOf",
"subject": "Abilene,_Texas"
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],
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{
"object": "Texas",
"property": "isPartOf",
"subject": "Abilene,_Texas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id2"
}
},
{
"79": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia has a Mayor as leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, Georgia is lead by a Mayor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The leader of Albany, Georgia is the Mayor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mayor",
"property": "leaderTitle",
"subject": "Albany,_Georgia"
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],
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{
"object": "\"Mayor\"@en",
"property": "leaderTitle",
"subject": "Albany,_Georgia"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
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},
{
"80": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Oregon has a total area of 45.97 square kilometres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The total area of Albany Oregon is 45.97 Sq K.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The total area of Albany, Oregon is 45.97 sq kms.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "45.97 (square kilometres)",
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"subject": "Albany,_Oregon"
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],
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[
{
"object": "4.59723e+07",
"property": "areaTotal",
"subject": "Albany,_Oregon"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
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},
{
"81": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Albany, Oregon is in the U.S.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albany, Oregon is located within the United States.",
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}
],
"links": [],
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{
"object": "United_States",
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],
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{
"object": "United_States",
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},
"shape": "NA",
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"size": "1",
"xml_id": "Id5"
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},
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"82": {
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"comment": "good",
"lang": "",
"lex": "Albuquerque, New Mexico is led by the State Senate.",
"xml_id": "Id1"
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],
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"object": "\"State Senate\"@en",
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id6"
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},
{
"83": {
"category": "City",
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"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "The Albuquerque City Council has a leadership role in Albuquerque, New Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Albuquerque in New Mexico is led by the Albuquerque City Council.",
"xml_id": "Id2"
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],
"links": [],
"modifiedtripleset": [
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"object": "Albuquerque_City_Council",
"property": "leaderTitle",
"subject": "Albuquerque,_New_Mexico"
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],
"originaltriplesets": {
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[
{
"object": "Albuquerque_City_Council",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id7"
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},
{
"84": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "Amarillo is in Texas, in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States is the home country to Amarillo,Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo, Texas is located within the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Amarillo,_Texas"
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],
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{
"object": "United_States",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id8"
}
},
{
"85": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amarillo is part of Potter County in Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo is part of Potter County, Texas.",
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],
"links": [],
"modifiedtripleset": [
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"object": "Potter_County,_Texas",
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"subject": "Amarillo,_Texas"
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],
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{
"object": "Potter_County,_Texas",
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],
[
{
"object": "Potter_County,_Texas",
"property": "isPartOf",
"subject": "Amarillo,_Texas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id9"
}
},
{
"86": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "Amarillo is in Texas, in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo, Texas is part of the United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amarillo, Texas, is part of the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "isPartOf",
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],
"originaltriplesets": {
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[
{
"object": "\"United States\"@en",
"property": "subdivisionName",
"subject": "Amarillo,_Texas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id10"
}
},
{
"87": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anaheim, in California, has the area code 657.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The area code of Anaheim, California is 657.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The area code for Anaheim, California, is 657.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "657",
"property": "areaCode",
"subject": "Anaheim,_California"
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],
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{
"object": "657",
"property": "areaCode",
"subject": "Anaheim,_California"
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]
},
"shape": "NA",
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"xml_id": "Id11"
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},
{
"88": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Anderson, Indiana is part of Adams Fall Creek, Lafayette, Richland, Union.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Adams, Fall Creek, Lafayette, Richland, Union\"",
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"subject": "Anderson,_Indiana"
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],
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[
{
"object": "\"Adams, Fall Creek, Lafayette, Richland, Union\"@en",
"property": "subdivisionName",
"subject": "Anderson,_Indiana"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"89": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola is in Pleasant Township which is part of Steuben County, in Indiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola, Indiana is part of Pleasant Township, which is in Steuben County.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "subdivisionName",
"subject": "Angola,_Indiana"
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],
[
{
"object": "Pleasant_Township,_Steuben_County,_Indiana",
"property": "isPartOf",
"subject": "Angola,_Indiana"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"90": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A City Administrator leads Ann Arbor, Michigan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The City Administrator leads Ann Arbor in Michigan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"City Administrator\"",
"property": "leaderTitle",
"subject": "Ann_Arbor,_Michigan"
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],
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"originaltripleset": [
[
{
"object": "\"City Administrator\"@en",
"property": "leaderTitle",
"subject": "Ann_Arbor,_Michigan"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id14"
}
},
{
"91": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Mayor, is the title of the leader in Ann Arbor, Michigan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ann Arbor, Michigan is led by the Mayor.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The leader title of Ann Arbor, Michigan, is Mayor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mayor",
"property": "leaderTitle",
"subject": "Ann_Arbor,_Michigan"
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],
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[
{
"object": "\"Mayor\"@en",
"property": "leaderTitle",
"subject": "Ann_Arbor,_Michigan"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id15"
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},
{
"92": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The city of Ann Arbor, Michigan, has a population of 1580.7 as per square kilometre.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AnnArbor, Michigan has a population of 1580.7 peer square kilometer.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The population density of Ann Arbor, Michigan is 1580.7 inhabitants per sq km.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1580.7 (inhabitants per square kilometre)",
"property": "populationDensity",
"subject": "Ann_Arbor,_Michigan"
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],
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"originaltripleset": [
[
{
"object": "\"1580.7\"^^xsd:double",
"property": "populationDensity",
"subject": "Ann_Arbor,_Michigan"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id16"
}
},
{
"93": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antioch, CA is 13 above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antioch, California is 13.0 metres above sea level.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antioch, California is located at 13.0 above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "13.0",
"property": "elevationAboveTheSeaLevel",
"subject": "Antioch,_California"
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],
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"originaltripleset": [
[
{
"object": "\"13.1064\"^^xsd:double",
"property": "elevation",
"subject": "Antioch,_California"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
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},
{
"94": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader title of Antioch, California, is California's 11th State Assembly district.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The leader title of Antioch California is California's 11th State Assembly district.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "California's_11th_State_Assembly_district",
"property": "leaderTitle",
"subject": "Antioch,_California"
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],
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"originaltripleset": [
[
{
"object": "California's_11th_State_Assembly_district",
"property": "leaderTitle",
"subject": "Antioch,_California"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id18"
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},
{
"95": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arlington Texas is has an elevation of 184 above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arlington in Texas is located at 184.0 above sea level.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "184.0",
"property": "elevationAboveTheSeaLevel",
"subject": "Arlington,_Texas"
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],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "\"184.099\"^^xsd:double",
"property": "elevation",
"subject": "Arlington,_Texas"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id19"
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},
{
"96": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlantic County, New Jersey is located in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic County in New Jersey is located in the United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Atlantic_County,_New_Jersey"
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],
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"originaltripleset": [
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{
"object": "United_States",
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"subject": "Atlantic_County,_New_Jersey"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id20"
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},
{
"97": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Attica is part of Fountain County Indiana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Attica, Indiana is in Fountain County, Indiana.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Fountain_County,_Indiana",
"property": "isPartOf",
"subject": "Attica,_Indiana"
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],
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{
"object": "Fountain_County,_Indiana",
"property": "isPartOf",
"subject": "Attica,_Indiana"
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],
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{
"object": "Fountain_County,_Indiana",
"property": "subdivisionName",
"subject": "Attica,_Indiana"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
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},
{
"98": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Auburn is part of Alabama.",
"xml_id": "Id1"
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{
"comment": "good",
"lang": "",
"lex": "Auburn is located in Alabama.",
"xml_id": "Id2"
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],
"links": [],
"modifiedtripleset": [
{
"object": "Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
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"originaltripleset": [
[
{
"object": "Alabama",
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"subject": "Auburn,_Alabama"
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[
{
"object": "Alabama",
"property": "isPartOf",
"subject": "Auburn,_Alabama"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
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},
{
"99": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "California is in the United States.",
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{
"comment": "good",
"lang": "",
"lex": "California is in the U.S.",
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],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "California"
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{
"object": "United_States",
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id23"
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},
{
"100": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Atlanta is the largest city of Fulton County, Georgia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Atlanta is the largest city in Fulton County, Georgia.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Atlanta",
"property": "largestCity",
"subject": "Fulton_County,_Georgia"
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],
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"originaltripleset": [
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{
"object": "Atlanta",
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"subject": "Fulton_County,_Georgia"
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id24"
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},
{
"101": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The state of Georgia is in the U.S.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Georgia is in the country of United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The state of Georgia is located within the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Georgia_(U.S._state)"
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],
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"originaltripleset": [
[
{
"object": "United_States",
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"subject": "Georgia_(U.S._state)"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id25"
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},
{
"102": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Indianapolis is the capital of Indiana.",
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}
],
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"modifiedtripleset": [
{
"object": "Indianapolis",
"property": "capital",
"subject": "Indiana"
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],
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"originaltripleset": [
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{
"object": "Indianapolis",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id26"
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},
{
"103": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Lee County is in Alabama.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lee County is in the state of Alabama.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Lee County is situated within the state of Alabama.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Alabama",
"property": "state",
"subject": "Lee_County,_Alabama"
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],
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"originaltripleset": [
[
{
"object": "Alabama",
"property": "state",
"subject": "Lee_County,_Alabama"
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id27"
}
},
{
"104": {
"category": "City",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Trenton is the capital of New Jersey.",
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},
{
"comment": "good",
"lang": "",
"lex": "The capital of the state of New Jersey is Trenton.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Trenton,_New_Jersey",
"property": "capital",
"subject": "New_Jersey"
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],
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{
"object": "Trenton,_New_Jersey",
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id28"
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},
{
"105": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Potter County is in Texas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Potter County is in the State of Texas.",
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}
],
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"modifiedtripleset": [
{
"object": "Texas",
"property": "state",
"subject": "Potter_County,_Texas"
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],
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{
"object": "Texas",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id29"
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},
{
"106": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tarrant County, Texas is situated in the United States.",
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},
{
"comment": "good",
"lang": "",
"lex": "Tarrant County in Texas is in the U.S.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tarrant County, Texas is located within the United States.",
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}
],
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"modifiedtripleset": [
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"object": "United_States",
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{
"object": "United_States",
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},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id30"
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},
{
"107": {
"category": "City",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ann Arbor is the largest city in Washtenaw County, Michigan.",
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},
{
"comment": "good",
"lang": "",
"lex": "The largest town in Washtenaw county Michigan is Ann Arbor.",
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},
{
"comment": "good",
"lang": "",
"lex": "The largest city in Washtenaw County, Michigan is Ann Arbor.",
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}
],
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"object": "Ann_Arbor,_Michigan",
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],
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{
"object": "Ann_Arbor,_Michigan",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id31"
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},
{
"108": {
"category": "ComicsCharacter",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asterix was created by Rene Goscinny.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The creator of Asterix (comics character) is René Goscinny.",
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},
{
"comment": "good",
"lang": "",
"lex": "The comic character Asterix, was created by René Goscinny.",
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}
],
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"modifiedtripleset": [
{
"object": "René_Goscinny",
"property": "creator",
"subject": "Asterix_(comicsCharacter)"
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{
"object": "René_Goscinny",
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],
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{
"object": "René_Goscinny",
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
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},
{
"109": {
"category": "ComicsCharacter",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The comic character Auron was created by Marv Wolfman.",
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},
{
"comment": "good",
"lang": "",
"lex": "Auron was created by Marv Wolfman.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic book character Auron was created by Marv Wolfman.",
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}
],
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{
"object": "Marv_Wolfman",
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"subject": "Auron_(comicsCharacter)"
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"object": "\"Marv Wolfman\"@en",
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{
"object": "Marv_Wolfman",
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id2"
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},
{
"110": {
"category": "ComicsCharacter",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The comic character, Balder, was created by Jack Kirby.",
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},
{
"comment": "good",
"lang": "",
"lex": "The comic character Balder was created by Jack Kirby.",
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},
{
"comment": "good",
"lang": "",
"lex": "Jack Kirby created the comic character Balder.",
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}
],
"links": [],
"modifiedtripleset": [
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"object": "Jack_Kirby",
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"subject": "Balder_(comicsCharacter)"
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],
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{
"object": "Jack_Kirby",
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},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
}
},
{
"111": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bananaman is broadcast by STV.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman is broadcasted by \"STV\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman was broadcasted by STV.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"STV\"",
"property": "broadcastedBy",
"subject": "Bananaman"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"STV\"@en",
"property": "network",
"subject": "Bananaman"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
}
},
{
"112": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bananaman first aired on 3rd October 1983.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman first aired on the 3rd of October, 1983.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman was first aired on 10/03/1983.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"1983-10-03\"",
"property": "firstAired",
"subject": "Bananaman"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1983-10-03\"^^xsd:date",
"property": "firstAired",
"subject": "Bananaman"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id5"
}
},
{
"113": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bill Oddie stars in Bananaman.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman starred Bill Oddie.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bill Oddie starred in Bananaman.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bill_Oddie",
"property": "starring",
"subject": "Bananaman"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bill_Oddie",
"property": "starring",
"subject": "Bananaman"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id6"
}
},
{
"114": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baymax is a character in Big Hero 6.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id7"
}
},
{
"115": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bill Everett has won the Eisner Award.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bill Everett has been awarded the Eisner award.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Eisner_Award",
"property": "award",
"subject": "Bill_Everett"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Eisner_Award",
"property": "award",
"subject": "Bill_Everett"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id8"
}
},
{
"116": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bill Oddie's daughter is Kate Hardie.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Kate Hardie is Bill Oddie's child.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kate_Hardie",
"property": "child",
"subject": "Bill_Oddie"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Kate_Hardie",
"property": "children",
"subject": "Bill_Oddie"
}
],
[
{
"object": "Kate_Hardie",
"property": "child",
"subject": "Bill_Oddie"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id9"
}
},
{
"117": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Dan Mishkin is one of the creators of the comic character, Bolt.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The comic book character Bolt was created by Dan Mishkin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic character Bolt was created by Dan Mishkin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dan_Mishkin",
"property": "creator",
"subject": "Bolt_(comicsCharacter)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dan_Mishkin",
"property": "creators",
"subject": "Bolt_(DC_Comics)"
}
],
[
{
"object": "Dan_Mishkin",
"property": "creator",
"subject": "Bolt_(DC_Comics)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id10"
}
},
{
"118": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bozo the Iron Man’s full name is Hugh Hazzard.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The full name of Bozo the Iron Man is \"Hugh Hazzard\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic book character Bozo the Iron Man's alter ego is Hugh Hazzard.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Hugh Hazzard\"",
"property": "fullName",
"subject": "Bozo_the_Iron_Man"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Hugh Hazzard\"@en",
"property": "fullName",
"subject": "Bozo_the_Iron_Man"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id11"
}
},
{
"119": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "John Buscema has won the comic book award, Eagle Award.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "John Buscema received the Eagle Award for his work in comics.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Eagle_Award_(comics)",
"property": "award",
"subject": "John_Buscema"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Eagle_Award_(comics)",
"property": "award",
"subject": "John_Buscema"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"120": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Marv Wolfman is a recipient of the Eagle Award.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Marv Wolfman won the Eagle Award for comics.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Marv Wolfman has received the Eagle Award for comics.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Eagle_Award_(comics)",
"property": "award",
"subject": "Marv_Wolfman"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Eagle_Award_(comics)",
"property": "award",
"subject": "Marv_Wolfman"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"121": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The alternative name for Ajoblanco is \"Ajo blanco\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ajo blanco is an alternative name to Ajoblanco.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ajo blanco is an alternative name of Ajoblanco.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Ajo blanco\"",
"property": "alternativeName",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Ajo blanco\"@en",
"property": "alias",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
}
},
{
"122": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in ajoblanco are bread, almonds, garlic, water and olive oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bread, almonds, garlic, water, and olive oil are the main ingredients in Ajoblanco.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Ajoblanco are bread, almonds, garlic, water, olive oil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Bread, almonds, garlic, water, olive oil\"",
"property": "mainIngredients",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Bread, almonds, garlic, water, olive oil\"@en",
"property": "mainIngredient",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id2"
}
},
{
"123": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is found nationwide in Indonesia, but is more specific to Java.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is nationwide in Indonesia, but more specific to Java.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"",
"property": "region",
"subject": "Arem-arem"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"@en",
"property": "region",
"subject": "Arem-arem"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
}
},
{
"124": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem arem comes from Indonesia.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "region",
"subject": "Arem-arem"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "region",
"subject": "Arem-arem"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
}
},
{
"125": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is made with tomatoes, red chili, garlic and olive oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "the main ingredients in Arrabbiata sauce are Tomatoes, red chili, garlic and olive oil.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Tomatoes, red chili, garlic, olive oil\"",
"property": "mainIngredients",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Tomatoes,red chili,garlic,olive oil\"",
"property": "ingredientName",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "\"Tomatoes, red chili, garlic, olive oil\"@en",
"property": "mainIngredient",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id5"
}
},
{
"126": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient in Arros negre.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient of the dish Arròs negre.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id6"
}
},
{
"127": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Squid is an ingredient of Arros negre.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id7"
}
},
{
"128": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id8"
}
},
{
"129": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The genus of the avocado is persea.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The genus of Avocado is Persea.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Persea",
"property": "genus",
"subject": "Avocado"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Persea",
"property": "genus",
"subject": "Avocado"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id9"
}
},
{
"130": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet should be served hot.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish that should be served hot.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "ayam penyet is served hot.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Hot\"",
"property": "servingTemperature",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Hot\"",
"property": "servingTemperature",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id10"
}
},
{
"131": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is a dessert.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id11"
}
},
{
"132": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient of bakewell tart is frangipane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Frangipane is an ingredient in a Bakewell tart.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Frangipane is an ingredient of Bakewell tart.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"133": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The dish Bakso contains noodles.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Noodle is an ingredient in Bakso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "bakso contains noodles.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Noodle",
"property": "ingredient",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Noodle",
"property": "ingredient",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"134": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is an ingredient in Bakso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is an ingredient of the dish Bakso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Vermicelli is included in bakso.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vermicelli",
"property": "ingredient",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Vermicelli",
"property": "ingredient",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id14"
}
},
{
"135": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Chorizo is an ingredient in the dish Bandeja paisa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Chorizo is an ingredient in Bandeja paisa.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chorizo",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chorizo",
"property": "ingredient",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id15"
}
},
{
"136": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Mondelez International is the creator of Barny cakes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes were created by Mondelez International.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes is created by Mondelez International.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mondelez_International",
"property": "creator",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mondelez_International",
"property": "creator",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id16"
}
},
{
"137": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes can be chocolate flavoured.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Chocolate is one variation of the Barny Cakes dish.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chocolate",
"property": "dishVariation",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chocolate",
"property": "hasVariant",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
}
},
{
"138": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny cakes contain 1.8 g of protein.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes contain 1.8g of protein.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1.8 g",
"property": "protein",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1.8\"^^xsd:double",
"property": "protein",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id18"
}
},
{
"139": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batchoy is eaten in the Philippines.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Philippines is the country Batchoy comes from.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id19"
}
},
{
"140": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Chicken is an ingredient in Batchoy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy includes chicken.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chicken",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chicken",
"property": "ingredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id20"
}
},
{
"141": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Vegetable are used in the preparation of Batchoy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Vegetables are an ingredient in Batchoy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Vegetable",
"property": "ingredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
}
},
{
"142": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Binignit is a dish from the region of Visayas.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Visayas",
"property": "region",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Visayas",
"property": "region",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
}
},
{
"143": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is a leader in the Derbyshire Dales.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is a leader in Derbyshire Dales.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is the leader of Derbyshire Dales.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id23"
}
},
{
"144": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A variation on dessert is cake.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cake",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cake",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id24"
}
},
{
"145": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ice cream is a type of dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A type of dessert is an ice cream.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ice cream is a dessert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ice_cream",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ice_cream",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id25"
}
},
{
"146": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jusuf Kalla is a leader in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jusuf Kalla is the leader of Indonesia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
[
{
"object": "Jusuf_Kalla",
"property": "leader",
"subject": "Indonesia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id26"
}
},
{
"147": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Italians are from Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italians is the name given to people from Italy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id27"
}
},
{
"148": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baduy is the ethnic group of Java.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baduy is an ethnic group in Java.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baduy is an ethnic group of Java.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id28"
}
},
{
"149": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Lemon is under the order of Rosids.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "the order of lemons is Rosids.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rosids",
"property": "order",
"subject": "Lemon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rosids",
"property": "order",
"subject": "Lemon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id29"
}
},
{
"150": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Mexico is inhabited by Mexicans.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Mexicans are people from Mexico.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexicans",
"property": "demonym",
"subject": "Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexicans",
"property": "demonym",
"subject": "Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id30"
}
},
{
"151": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "In Mexico, the spoken language is Spanish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Spanish is the language spoken in Mexico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The language of Mexico is Spanish.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spanish_language",
"property": "language",
"subject": "Mexico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spanish_language",
"property": "language",
"subject": "Mexico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id31"
}
},
{
"152": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "One of the languages used in the Philippines is Arabic.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arabic is a language spoken in the Philippines.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "One of the languages in Philippines is Arabic.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "Arabic is one of the languages spoken in the Philippines.",
"xml_id": "Id4"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id32"
}
},
{
"153": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Shumai is a variation of the dish Siomay.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Siomay and Shumai are variations of the same dish.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Siomay"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Shumai",
"property": "hasVariant",
"subject": "Siomay"
}
],
[
{
"object": "Shumai",
"property": "variations",
"subject": "Siomay"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id33"
}
},
{
"154": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Native Americans in the United States are one of the ethnic groups of the country.",
"xml_id": "Id1"
}
],
"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": "Id34"
}
},
{
"155": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument in Izmir was designed by Pietro Canonica.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Pietro Canonica designed the Atatürk Monument in Izmir.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Pietro Canonica is the designer of the Ataturk Monument (Izmir).",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "The Ataturk Monument was designed by Pietro Canonica.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "Ataturk Monument was designed by Pietro Canonica.",
"xml_id": "Id5"
},
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument (İzmir) was designed by Pietro Canonica.",
"xml_id": "Id6"
},
{
"comment": "good",
"lang": "",
"lex": "The designer of the Atatürk_Monument (İzmir) is Pietro Canonica.",
"xml_id": "Id7"
},
{
"comment": "good",
"lang": "",
"lex": "Pietro Canonica is the designer of the Ataturk Monument in Izmir.",
"xml_id": "Id8"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pietro_Canonica",
"property": "designer",
"subject": "Atatürk_Monument_(İzmir)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pietro_Canonica",
"property": "designer",
"subject": "Atatürk_Monument_(İzmir)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
}
},
{
"156": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument in Izmir is made of bronze.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The material of the Ataturk Monument (Izmir) is bronze.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Ataturk Monument is made from Bronze.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "The Atatürk Monument (İzmir) is made of Bronze.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "Ataturk Monument is made of Bronze.",
"xml_id": "Id5"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Bronze\"",
"property": "material",
"subject": "Atatürk_Monument_(İzmir)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Bronze\"@en",
"property": "material",
"subject": "Atatürk_Monument_(İzmir)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id2"
}
},
{
"157": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Azerbaijan leader's name is Artur Rasizade.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The name of the leader of Azerbaijan is Artur Rasizade.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
}
},
{
"158": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is located in Azerbaijan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyr's Memorial is in Azerbaijan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Turkish martyrs memorial is located in Baku, Azerbaijan.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial is located in Azerbaijan.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial can be found is Azerbaijan.",
"xml_id": "Id5"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is in Azerbaijan.",
"xml_id": "Id6"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is found in Azerbaijan.",
"xml_id": "Id7"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
}
},
{
"159": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The materials used for the Baku Turkish Martyrs' Memorial is red granite and white marble.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is created in red granite and white marble.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The material of the Baku Turkish Martyrs' Memorial is Red granite and white marble.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is made from red granite and white marble.",
"xml_id": "Id4"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial is made of red granite and white marble.",
"xml_id": "Id5"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial is made from red granite and white marble.",
"xml_id": "Id6"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is made with red granite and white marble.",
"xml_id": "Id7"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Red granite and white marble\"",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Red granite and white marble\"@en",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id5"
}
},
{
"160": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Peter Stoger is the manager of FC Kolin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Peter Stöger is manager of 1. FC Köln.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of 1. FC Koln is Peter Stoger.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peter_Stöger",
"property": "manager",
"subject": "1._FC_Köln"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peter_Stöger",
"property": "manager",
"subject": "1._FC_Köln"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id1"
}
},
{
"161": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1 FC Magdeburg played in the 2014 season.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014",
"property": "season",
"subject": "1._FC_Magdeburg"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2014",
"property": "season",
"subject": "1._FC_Magdeburg"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id2"
}
},
{
"162": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A C Lumezzane played the 2014-2015 season in Lega Pro.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lumezzane played in the Lega Pro during the 2014 season.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Lega_Pro",
"property": "season",
"subject": "A.C._Lumezzane"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2014–15_Lega_Pro",
"property": "season",
"subject": "A.C._Lumezzane"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id3"
}
},
{
"163": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.D. Isidro Metapan's full name is \"Asociación Deportiva\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.D. Isidro Metapán's full name is Asociación Deportiva.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The fullname of A.D. Isidro Metapan is Asociacion Deportiva.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Asociación Deportiva\"",
"property": "fullname",
"subject": "A.D._Isidro_Metapán"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Asociación Deportiva\"@en",
"property": "fullname",
"subject": "A.D._Isidro_Metapán"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id4"
}
},
{
"164": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis's full name is A.E Dimitra Efxeinoupolis.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "\"A.E Dimitra Efxeinoupolis\" is the full name of A.E Dimitra Efxeinoupolis.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis has the fullname A.E Dimitra Efxeinoupolis.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"A.E Dimitra Efxeinoupolis\"",
"property": "fullname",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"A.E Dimitra Efxeinoupolis\"@en",
"property": "fullname",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id5"
}
},
{
"165": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis played in the 2014 season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitia Efxeinoupolis were in the 2014 season.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis played in season 2014.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2014",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id6"
}
},
{
"166": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis were in the 2014–15 A EPSTH. Greece season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis were in the 2014-15 A EPSTH, Greece.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_A_EPSTH,_Greece",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2014–15_A_EPSTH,_Greece",
"property": "season",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id7"
}
},
{
"167": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.F.C. Blackpool has 1500 members.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1500",
"property": "numberOfMembers",
"subject": "A.F.C._Blackpool"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1500\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.F.C._Blackpool"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id8"
}
},
{
"168": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Fylde is the home ground of AFC Fylde.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Fylde has the home ground called The Fylde.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "The_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "The_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id9"
}
},
{
"169": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ground of A.S. Gubbio 1910 is located in Italy.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "ground",
"subject": "A.S._Gubbio_1910"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "ground",
"subject": "A.S._Gubbio_1910"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id10"
}
},
{
"170": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio has 19238 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AS Livorno Calcio has 19238 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A.S. Livorno Calcio has 19238 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "19238",
"property": "numberOfMembers",
"subject": "A.S._Livorno_Calcio"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "19238",
"property": "capacity",
"subject": "A.S._Livorno_Calcio"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id11"
}
},
{
"171": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.S. Roma were in Serie A in 2014-15.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Serie_A",
"property": "season",
"subject": "A.S._Roma"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2014–15_Serie_A",
"property": "season",
"subject": "A.S._Roma"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id12"
}
},
{
"172": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Frank de Boer manages the AFC Ajax team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax's manager is Frank de Boer.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of AFC Ajax is Frank de Boer.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Frank_de_Boer",
"property": "manager",
"subject": "AFC_Ajax"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Frank_de_Boer",
"property": "manager",
"subject": "AFC_Ajax"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id13"
}
},
{
"173": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs) have the nickname \"Joden , Godenzonen\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AFC Ajax team's nickname is Joden, Godenzonen.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs) nickname is Joden, Godenzonen.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Joden , Godenzonen\"",
"property": "nickname",
"subject": "AFC_Ajax_(amateurs)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Joden , Godenzonen\"@en",
"property": "nickname",
"subject": "AFC_Ajax_(amateurs)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id14"
}
},
{
"174": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax has 5000 Members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs) has 5000 members.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "5000",
"property": "numberOfMembers",
"subject": "AFC_Ajax_(amateurs)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"5000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AFC_Ajax_(amateurs)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id15"
}
},
{
"175": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZAL PFK is located at Shuvalan, Baku, Azerbaijan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AZAL PFK is located in Shuvalan, Baku, Azerbaijan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Shuvalan, Baku, Azerbaijan\"",
"property": "location",
"subject": "AZAL_PFK"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Shuvalan, Baku, Azerbaijan\"@en",
"property": "location",
"subject": "AZAL_PFK"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id16"
}
},
{
"176": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZ Alkmaar has 17023 members.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "17023",
"property": "numberOfMembers",
"subject": "AZ_Alkmaar"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"17023\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AZ_Alkmaar"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id17"
}
},
{
"177": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agremiacao Sportiva Arapiraquense are managed by Vica.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Vica",
"property": "manager",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Vica",
"property": "manager",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id18"
}
},
{
"178": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense competed in the 2015 Campeonato Brasileiro Série C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agremiacao Sportiva Arapiraquense were in Campeonato Brasileiro Serie C in 2015.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2015_Campeonato_Brasileiro_Série_C",
"property": "season",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "2015_Campeonato_Brasileiro_Série_C",
"property": "season",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id19"
}
},
{
"179": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault's ground is St. Vincent-St. Mary High School.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "St Vincent-St Mary High School is the ground of Akron Summit Assault.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "St._Vincent–St._Mary_High_School",
"property": "ground",
"subject": "Akron_Summit_Assault"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "St._Vincent–St._Mary_High_School",
"property": "ground",
"subject": "Akron_Summit_Assault"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id20"
}
},
{
"180": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault has 3000 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault has got 3000 members.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3000",
"property": "numberOfMembers",
"subject": "Akron_Summit_Assault"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "Akron_Summit_Assault"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id21"
}
},
{
"181": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam-Centrum is part of Amsterdam.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam-Centrum",
"property": "part",
"subject": "Amsterdam"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam-Centrum",
"property": "part",
"subject": "Amsterdam"
}
],
[
{
"object": "Amsterdam-Centrum",
"property": "p",
"subject": "Amsterdam"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id22"
}
},
{
"182": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Greece is Nikos Voutsis.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Nikos_Voutsis",
"property": "leaderName",
"subject": "Greece"
}
],
[
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id23"
}
},
{
"183": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Gus Poyet is in AEK Athens F.C.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Gus Poyet plays for AEK Athens F.C.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "AEK_Athens_F.C.",
"property": "club",
"subject": "Gus_Poyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "AEK_Athens_F.C.",
"property": "team",
"subject": "Gus_Poyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "1",
"xml_id": "Id24"
}
},
{
"184": {
"category": "SportsTeam",
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"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "3500.0",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Alcobendas",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"3499.71\"^^xsd:double",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id2"
}
},
{
"229": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Adolfo Suárez Madrid–Barajas Airport located at San Sebastian de los Reye has a runway length of 3500.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Adolfo Suárez Madrid–Barajas Airport is in San Sebastián de los Reyes and has a runway length of 3500.0 metres.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas airport is located at San Sebastian de los Reyes and has a runway length of 3500.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3500.0",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "San_Sebastián_de_los_Reyes",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3499.71\"^^xsd:double",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "San_Sebastián_de_los_Reyes",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id3"
}
},
{
"230": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is in India where one of its leaders is T.S. Thakur.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is in India, where the leader is TS Thakur.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is located in India, where the leader is T S Thakur.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
}
],
[
{
"object": "India",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id4"
}
},
{
"231": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is 618 ft above sea level and is located in Al Anbar Province, Iraq.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Al Asad Airbase which is elevated 618 feet above sea level, is located in the Al Anbar Province in Iraq.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Al Asad Airbase, which is 618 ft above sea level, is located in Al Anbar Province, Iraq.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "618",
"property": "elevationAboveTheSeaLevel_(in_feet)",
"subject": "Al_Asad_Airbase"
},
{
"object": "\"Al Anbar Province, Iraq\"",
"property": "location",
"subject": "Al_Asad_Airbase"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "618",
"property": "elevationF",
"subject": "Al_Asad_Airbase"
},
{
"object": "\"Al Anbar Province, Iraq\"@en",
"property": "location",
"subject": "Al_Asad_Airbase"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id5"
}
},
{
"232": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is operated by the United States Air Force who fought in The Operation of Enduring Freedom battle.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Operation Enduring Freedom was a battle involving the United States Air Force who operate Al Asad airbase.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "Operation_Enduring_Freedom",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States_Air_Force",
"property": "operator",
"subject": "Al_Asad_Airbase"
},
{
"object": "Operation_Enduring_Freedom",
"property": "battle",
"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id6"
}
},
{
"233": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States invasion of Panama was a battle involving the United States Air Force who operate the Al Asad Airbase.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States invasion of Panama was a battle involving the United States Air Force who are the operating organisation for Al Asad airbase.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force was involved in the invasion of Panama and is also the operating organisation for Al Asad air base.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "United_States_invasion_of_Panama",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States_Air_Force",
"property": "operator",
"subject": "Al_Asad_Airbase"
},
{
"object": "United_States_invasion_of_Panama",
"property": "battle",
"subject": "United_States_Air_Force"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id7"
}
},
{
"234": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport is located in Alcobendas which is part of the Community of Madrid.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport is in Alcobendas which is part of the community of Madrid.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Community_of_Madrid",
"property": "isPartOf",
"subject": "Alcobendas"
},
{
"object": "Alcobendas",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Community_of_Madrid",
"property": "isPartOf",
"subject": "Alcobendas"
},
{
"object": "Alcobendas",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id8"
}
},
{
"235": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Poaceae is the surface of the 1st runway at Alderney Airport which has a runway with the length of 877.0 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 1st runway length of Alderney Airport is 877.0 and the surface is poaceae.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Poaceae",
"property": "1st_runway_SurfaceType",
"subject": "Alderney_Airport"
},
{
"object": "877.0",
"property": "runwayLength",
"subject": "Alderney_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Poaceae",
"property": "r1Surface",
"subject": "Alderney_Airport"
},
{
"object": "\"877.0\"^^xsd:double",
"property": "runwayLength",
"subject": "Alderney_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id9"
}
},
{
"236": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport is operated by Pakistan Civil Aviation Authority, the HQ of which is at Jinnah International Airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Pakistan Civil Aviation Authority, which has its HQ at Jinnah International Airport, governs Allama Iqbal International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Allama Iqbal International Airport is operated by the Pakistan Civil Aviation Authority, which is headquartered at Jinnah International Airport.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Pakistan_Civil_Aviation_Authority",
"property": "operatingOrganisation",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Jinnah_International_Airport",
"property": "headquarter",
"subject": "Pakistan_Civil_Aviation_Authority"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Pakistan_Civil_Aviation_Authority",
"property": "operator",
"subject": "Allama_Iqbal_International_Airport"
},
{
"object": "Jinnah_International_Airport",
"property": "headquarter",
"subject": "Pakistan_Civil_Aviation_Authority"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id10"
}
},
{
"237": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The location of Alpena County Regional Airport is Wilson Township, Alpena County, Michigan, United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport is located in Wilson Township, Alpena County, Michigan, United States.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport is located in the Wilson Township, Alpena County, Michigan, USA.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Wilson_Township,_Alpena_County,_Michigan",
"property": "location",
"subject": "Alpena_County_Regional_Airport"
},
{
"object": "United_States",
"property": "isPartOf",
"subject": "Wilson_Township,_Alpena_County,_Michigan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Wilson_Township,_Alpena_County,_Michigan",
"property": "location",
"subject": "Alpena_County_Regional_Airport"
},
{
"object": "United_States",
"property": "subdivisionName",
"subject": "Wilson_Township,_Alpena_County,_Michigan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id11"
}
},
{
"238": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Schipol Group are the operators of Amsterdam Airport Schiphol, which is -3.3528 above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Schiphol Group operates the Amsterdam Airport Schiphol which is -3.3528m a.s.l.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amsterdam airport Schipol is operated by the Schipol group and is -3.3528 above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "Schiphol_Group",
"property": "operatingOrganisation",
"subject": "Amsterdam_Airport_Schiphol"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "Schiphol_Group",
"property": "operator",
"subject": "Amsterdam_Airport_Schiphol"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id12"
}
},
{
"239": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is 973 metres above sea level and The 4th runway is made of Asphalt.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The fourth runway at Andrews County Airport is made of asphalt and the airport is 973 below sea level.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Asphalt\"",
"property": "4th_runway_SurfaceType",
"subject": "Andrews_County_Airport"
},
{
"object": "973.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Andrews_County_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Asphalt\"@en",
"property": "r4Surface",
"subject": "Andrews_County_Airport"
},
{
"object": "\"973.0\"^^xsd:double",
"property": "elevation",
"subject": "Andrews_County_Airport"
}
],
[
{
"object": "\"Asphalt\"@en",
"property": "r4Surface",
"subject": "Andrews_County_Airport"
},
{
"object": "\"967.435\"^^xsd:double",
"property": "elevation",
"subject": "Andrews_County_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id13"
}
},
{
"240": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas, the capital of which is Austin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas which has the capital of Austin.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas, where Austin is the capital.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id14"
}
},
{
"241": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport serves the city of Luanda, it is 159m above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Angola International Airport, 159 m above sea level serves Luanda.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
},
{
"object": "159",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Angola_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Luanda",
"property": "cityServed",
"subject": "Angola_International_Airport"
},
{
"object": "\"159.106\"^^xsd:double",
"property": "elevation",
"subject": "Angola_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id15"
}
},
{
"242": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located in Ícolo e Bengo, Angola.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id16"
}
},
{
"243": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport, operated by The Flemish department of Mobility and Public Works, is 12 metres above sea level.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Flemish department of Mobility and Public Works\"",
"property": "operatingOrganisation",
"subject": "Antwerp_International_Airport"
},
{
"object": "12.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Antwerp_International_Airport"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Flemish department of Mobility and Public Works\"@en",
"property": "operator",
"subject": "Antwerp_International_Airport"
},
{
"object": "\"11.8872\"^^xsd:double",
"property": "elevation",
"subject": "Antwerp_International_Airport"
}
],
[
{
"object": "\"Flemish department of Mobility and Public Works\"@en",
"property": "operator",
"subject": "Antwerp_International_Airport"
},
{
"object": "\"12.0\"^^xsd:double",
"property": "elevation",
"subject": "Antwerp_International_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id17"
}
},
{
"244": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Appleton, Grand Chute, Wisconsin is a city which is served by Appleton International Airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton, Wisconsin is part of Grand Chute, Wisconsin and is served by Appletone International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport serves the city of Appleton which is part of Grand Chute, Wisconsin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
},
{
"object": "Grand_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
},
{
"object": "Grand_Chute,_Wisconsin",
"property": "subdivisionName",
"subject": "Appleton,_Wisconsin"
}
],
[
{
"object": "Grand_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
},
{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
}
],
[
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
},
{
"object": "Grand_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id18"
}
},
{
"245": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "New Zealand's Ardmore Airport's second runway has the surface type Poaceae which is part of the Poales order.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 2nd runway at Ardmore Airport (New Zealand) is made of Poaceae, which is a member of the order of Poales.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Poaceae (part of the Poales order) is the surface type of the second runway of Ardmore Airport, New Zealand.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "2nd_runway_SurfaceType",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poales",
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"subject": "Poaceae"
}
],
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{
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{
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]
]
},
"shape": "NA",
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"size": "2",
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}
},
{
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{
"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae which is included in the class Monocotyledon.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Ardmore Airport (New Zealand)'s 3rd runway surface type is Poaceae which is included in the class of Monocotyledon.",
"xml_id": "Id3"
}
],
"links": [],
"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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}
},
{
"247": {
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{
"comment": "good",
"lang": "",
"lex": "The runway length at Ashgabat International airport, located in Ashgabat, is 3800.0.",
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},
{
"comment": "good",
"lang": "",
"lex": "Ashgabat International Airport is located in Ashgabat and its runway length is 3800.",
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},
{
"comment": "good",
"lang": "",
"lex": "The runway length of Ashgabat International Airport in Ashgabat is 3800.0.",
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}
],
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{
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"subject": "Ashgabat_International_Airport"
},
{
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],
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]
]
},
"shape": "NA",
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"xml_id": "Id21"
}
},
{
"248": {
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"lexicalisations": [
{
"comment": "good",
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"lex": "Athens International Airport is in Spata and serves the city of Athens.",
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},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport, which is located in Spata, serves the city of Athens.",
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},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport in Spata serves the city of 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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],
[
{
"object": "Athens",
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{
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]
]
},
"shape": "NA",
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},
{
"249": {
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},
{
"comment": "good",
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"lex": "Athens International Airport serves the city of Athens and has a runway length of 3.800 metres.",
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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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"250": {
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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, part of the Community of Madrid.",
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},
{
"comment": "good",
"lang": "",
"lex": "The Adolfo Suárez Madrid–Barajas Airport is in San Sebastián de los Reyes which is part of the Community of Madrid.",
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}
],
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id24"
}
},
{
"251": {
"category": "Astronaut",
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{
"comment": "good",
"lang": "",
"lex": "Apollo 14 crew member Alan Shepard retired on the first of August, 1974.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Shepard, who served as a crew member of Apollo 14, retired 1974-08-01.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Apollo_14",
"property": "was a crew member of",
"subject": "Alan_Shepard"
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1"
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},
{
"252": {
"category": "Astronaut",
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"lexicalisations": [
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"comment": "good",
"lang": "",
"lex": "Buzz Aldrin is a US national because he was born in Glen Ridge, New Jersey.",
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},
{
"comment": "good",
"lang": "",
"lex": "American Buzz Aldrin hails from Glen Ridge, New Jersey.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was born in Glen Ridge, New Jersey in the United States.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was a US citizen born in Glen Ridge, New Jersey.",
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}
],
"links": [],
"modifiedtripleset": [
{
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"property": "birthPlace",
"subject": "Buzz_Aldrin"
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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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},
{
"253": {
"category": "Astronaut",
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"comment": "good",
"lang": "",
"lex": "Retiree Buzz Aldrin is a former fighter pilot.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin is now retired but he was once a fighter pilot.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin has since retired but he once served as a fighter pilot.",
"xml_id": "Id3"
},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin served as a fighter pilot and is now retired.",
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}
],
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"modifiedtripleset": [
{
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{
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],
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]
},
"shape": "NA",
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},
{
"254": {
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{
"comment": "good",
"lang": "",
"lex": "The Apollo 11 program was organized by NASA and included Buzz Aldrin as one of its crew members.",
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},
{
"comment": "good",
"lang": "",
"lex": "NASA operated the Apollo 11 program of which Buzz Aldrin was a member.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was on Apollo 11 which NASA operated.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin served as a crew member of Apollo 11 which is operated by NASA.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was a crew member of Apollo 11 - whose operator was NASA.",
"xml_id": "Id5"
},
{
"comment": "good",
"lang": "",
"lex": "buzz aldrin was a crew member of apollo 11, which was operated by NASA.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was a crew member of NASA's Apollo 11.",
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],
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},
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},
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},
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"lex": "Buzz Aldrin, who was a fighter pilot, was a crew member on Apollo 11.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was a fighter pilot who was also part of the Apollo 11 crew.",
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},
{
"comment": "good",
"lang": "",
"lex": "Buzz Aldrin was both a fighter pilot and member of the Apollo 11 flight crew.",
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},
{
"comment": "good",
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"lex": "Buzz Aldrin was a fighter pilot and crew member of Apollo 11.",
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],
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"shape": "NA",
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},
{
"256": {
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"comment": "good",
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"lex": "11 Diagonal Street is in South Africa. One South African group is the Coloured.",
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"comment": "good",
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"lex": "The address, 11 Diagonal Street is located in South Africa which is home to some coloured people.",
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],
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"modifiedtripleset": [
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],
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]
},
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},
{
"257": {
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"comment": "good",
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"lex": "There are 45 floors at 200 Public Square in Cleveland.",
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},
{
"comment": "good",
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],
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},
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},
{
"258": {
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"comment": "good",
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"lex": "20 Fenchurch Street is located in London, which is currently led by the European Parliament.",
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},
{
"comment": "good",
"lang": "",
"lex": "20 Fenchurch Street is located in London, which is led via the European Parliament.",
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},
{
"comment": "good",
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"lex": "20 Fenchurch Street is located in London which is lead via the European Parliament.",
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],
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},
"shape": "NA",
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},
{
"259": {
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{
"comment": "good",
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"lex": "250 Delaware Avenue is located in the United States, the leader of which was Barack Obama.",
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},
{
"comment": "good",
"lang": "",
"lex": "250 Delaware Avenue is located in the United States - which is lead by Barack Obama.",
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],
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},
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},
{
"260": {
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"comment": "good",
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"lex": "300 North LaSalle in Illinois has 60 floors.",
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{
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},
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},
{
"261": {
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"comment": "good",
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"lex": "HOK SVE was the architect of the 3Arena at North Wall, Dublin.",
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"comment": "good",
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"lex": "3Arena is located at North Wall, Dublin, and was built by HOK SVE.",
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"comment": "good",
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"lex": "The 3Arena is located at North Wall, Dublin and was designed by HOK SVE.",
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],
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{
"object": "North_Wall,_Dublin",
"property": "location",
"subject": "3Arena"
},
{
"object": "\"HOK SVE\"@en",
"property": "architect",
"subject": "3Arena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id6"
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},
{
"262": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "3Arena at East Link Bridge is owned by Live Nation Entertainment.",
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},
{
"comment": "good",
"lang": "",
"lex": "3 Arena is located at East Link Bridge and is owned by Live Nation Entertainment.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Live_Nation_Entertainment",
"property": "owner",
"subject": "3Arena"
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{
"object": "\"East Link Bridge\"",
"property": "location",
"subject": "3Arena"
}
],
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{
"object": "Live_Nation_Entertainment",
"property": "owner",
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},
{
"object": "\"East Link Bridge\"@en",
"property": "location",
"subject": "3Arena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id7"
}
},
{
"263": {
"category": "Building",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "3Arena, owned by Live Nation Entertainment, is located in Beverly Hills, California.",
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},
{
"comment": "good",
"lang": "",
"lex": "Live Nation Entertainment from Beverly Hills, California, are the owners of 3Arena.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Live_Nation_Entertainment",
"property": "owner",
"subject": "3Arena"
},
{
"object": "Beverly_Hills,_California",
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"subject": "Live_Nation_Entertainment"
}
],
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{
"object": "Live_Nation_Entertainment",
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{
"object": "Beverly_Hills,_California",
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]
]
},
"shape": "NA",
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}
},
{
"264": {
"category": "Building",
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{
"comment": "good",
"lang": "",
"lex": "Marriott International is the tenant of AC Hotel Bella Sky Copenhagen and it has 23 floors.",
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},
{
"comment": "good",
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"lex": "The tenant of the AC Hotel Bella Sky Copenhagen, which has 23 floors, is the Marriott International Hotel.",
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}
],
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{
"object": "Marriott_International",
"property": "tenant",
"subject": "AC_Hotel_Bella_Sky_Copenhagen"
},
{
"object": "23",
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}
],
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[
{
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"object": "\"23\"^^xsd:positiveInteger",
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],
[
{
"object": "Marriott_International",
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{
"object": "\"23\"^^xsd:positiveInteger",
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]
},
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},
{
"265": {
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"comment": "good",
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"lex": "Addis Ababa City Hall is 42 m high and houses the Government of Addis Ababa.",
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{
"comment": "good",
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"lex": "The \" Government of Addis Ababa\" are the current tenants of Addis Ababa City Hall which is 42 metres high.",
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},
{
"comment": "good",
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"lex": "Addis Ababa City Hall is 42 m high and is occupied by the Government of Addis Adaba.",
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}
],
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{
"object": "\"Government of Addis Ababa\"",
"property": "currentTenants",
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{
"object": "\"42 m\"",
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{
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{
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},
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},
{
"266": {
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{
"comment": "good",
"lang": "",
"lex": "Admisham Hall's architectural style is Tudor and Jacabian and the building is located in Haputale, Sri Lanka.",
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{
"comment": "good",
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"lex": "Adisham Hall, Haputale, Sri Lanka, is Tudor and Jacabian in style.",
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}
],
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"modifiedtripleset": [
{
"object": "\"Tudor and Jacabian\"",
"property": "architecturalStyle",
"subject": "Adisham_Hall"
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{
"object": "\"Haputale, Sri Lanka\"",
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"object": "\"Tudor and Jacabian\"@en",
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},
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},
{
"267": {
"category": "Building",
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"comment": "good",
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"lex": "Adisham Hall is located in Sri Lanka, where they speak the Tamil language.",
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},
{
"comment": "good",
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"lex": "Adisham Hall is located in the country of Sri Lanka where they speak Tamil.",
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],
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{
"object": "Sri_Lanka",
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"subject": "Adisham_Hall"
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"object": "Tamil_language",
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],
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{
"object": "Sri_Lanka",
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"object": "Tamil_language",
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],
[
{
"object": "Sri_Lanka",
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"subject": "Adisham_Hall"
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{
"object": "Tamil_language",
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},
"shape": "NA",
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}
},
{
"268": {
"category": "Building",
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{
"comment": "good",
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"lex": "The construction of Alan B Miller Hall, 101 Ukrop Way, began on 30th March 2007.",
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},
{
"comment": "good",
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"lex": "Alan B. Miller Hall's building start date was in 30th March 2007 and is located at 101 Ukrop Way.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan B. Miller Hall at 101 Ukrop Way opened on March 30, 2007.",
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"object": "\"30 March 2007\"",
"property": "buildingStartDate",
"subject": "Alan_B._Miller_Hall"
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{
"object": "\"101 Ukrop Way\"",
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{
"269": {
"category": "Building",
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{
"comment": "good",
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"lex": "Alan B Miller Hall is owned by The College of William and Mary and located in Williamsburg, Virginia.",
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},
{
"comment": "good",
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"lex": "Alan B. Miller Hall in Williamsburg, Virginia is owned by The College of William and Mary.",
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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 in Williamsburg, Virginia.",
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],
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"object": "College_of_William_&_Mary",
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{
"object": "Williamsburg,_Virginia",
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},
{
"270": {
"category": "Building",
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{
"comment": "good",
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"lex": "Robert A. M. Stern is the architect of the Alan B. Miller Hall where the Mason School of Business is located.",
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{
"comment": "good",
"lang": "",
"lex": "Robert A M Stern designed Alan B Miller Hall and the Mason School of Business is a tenant there.",
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},
{
"comment": "good",
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"lex": "Mason School of Business is a tenant of the Alan B. Miller Hall, which was designed by Robert A.M. Stern.",
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],
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},
{
"271": {
"category": "Building",
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{
"comment": "good",
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"lex": "Ampara Hospital is located in the Ampara District in Sri Lanka.",
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{
"comment": "good",
"lang": "",
"lex": "Ampara Hospital is located in the Ampara District of Sri Lanka.",
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}
],
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{
"object": "Sri_Lanka",
"property": "country",
"subject": "Ampara_Hospital"
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{
"object": "Ampara_District",
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"subject": "Ampara_Hospital"
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],
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[
{
"object": "Ampara_District",
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"object": "\"Sri Lanka\"@en",
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[
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"object": "Sri_Lanka",
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"object": "Ampara_District",
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},
"shape": "NA",
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"xml_id": "Id16"
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},
{
"272": {
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{
"comment": "good",
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"lex": "Sri Jayawardenepura Kotte is the capital of Sri Lanka, where Ampara Hospital is located.",
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"comment": "good",
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"lex": "Ampara Hospital is located in Sri Lanka whose capital is Sri Jayawardenepura Kotte.",
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},
{
"comment": "good",
"lang": "",
"lex": "Ampara Hospital is located in Sri Lanka whose capital is Sri Jayawardenepura.",
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],
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"object": "Sri_Lanka",
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{
"object": "Sri_Jayawardenepura_Kotte",
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},
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"273": {
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"comment": "good",
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"lex": "The Asher and Mary Isabelle Richardson House is located on U.S. Route 83 and has the reference number 88002539 in the National Register of Historic Places.",
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},
{
"comment": "good",
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"lex": "The National Register of Historic Places has the Asher and Mary Isabelle Richardson House, located at U.S. Route 83, referenced at 88002539.",
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},
{
"comment": "good",
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"lex": "Asher and Mary Isabelle Richardson House has the reference number 88002539 in the National Register of Historic Places, and is located at U.S. Route 83.",
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}
],
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{
"object": "U.S._Route_83",
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"subject": "Asher_and_Mary_Isabelle_Richardson_House"
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"object": "\"88002539\"",
"property": "ReferenceNumber in the National Register of Historic Places",
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},
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"274": {
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"comment": "good",
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"lex": "Asilomar Conference Grounds, Asilomar Blvd., Pacific Grove, California has a reference number in the National Register of Historic Places of: 87000823.",
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"comment": "good",
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"lex": "Asilomar Conference Grounds is located at Asilomar Blvd., Pacific Grove, California and it's reference number is 87000823 in the National Register of Historic Places.",
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},
{
"comment": "good",
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"lex": "\"87000823\" is Asilomar Conference Grounds' reference number in the National Register of Historic Places, and it is located on Asilomar Blvd, Pacific Grove, California.",
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],
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},
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"275": {
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"comment": "good",
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"comment": "good",
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"lex": "Asilomar Conference Grounds are located in Pacific Grove, California and was added to the National Register of Historic Places on February the 27nd 1987.",
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"comment": "good",
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"lex": "Asilomar Conference Grounds, Pacific Grove, California was added to the National Register of Historic Places on February 27, 1987.",
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},
{
"276": {
"category": "Building",
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"comment": "good",
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"lex": "Asser Levy Public Baths is located on 23rd Street, Manhattan, and was constructed in 1904.",
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"comment": "good",
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"lex": "Asser Levy Baths which was built in 1904 is located at 23rd Street in Manhattan.",
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{
"comment": "good",
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"lex": "The Asser Levy Public Baths, constructed in 1904, is located at 23rd Street (Manhattan).",
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],
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},
{
"277": {
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"comment": "good",
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"lex": "Anandiben Patel was the leader of Gujurat where Amdavad ni Gufa is located.",
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"comment": "good",
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"lex": "Amdavad Gufa is located in Gujarat and it's leader is Anandiben Patel.",
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},
{
"comment": "good",
"lang": "",
"lex": "Amdavad ni Gufa is located in Gujarat and it's leader is Anandiben Patel.",
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],
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"modifiedtripleset": [
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"object": "Anandiben_Patel",
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"object": "Gujarat",
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"shape": "NA",
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},
{
"278": {
"category": "ComicsCharacter",
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"comment": "good",
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"lex": "The comic strip character Asterix was created by Albert Uderzo and Rene Goscinny.",
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"comment": "good",
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"lex": "The comic book character Asterix was created by René Goscinny and Albert Uderzo.",
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"comment": "good",
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"lex": "The character Asterix was created by Reneé Goscinny and Albert Uderzo.",
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"modifiedtripleset": [
{
"object": "René_Goscinny",
"property": "creator",
"subject": "Asterix_(comicsCharacter)"
},
{
"object": "Albert_Uderzo",
"property": "creator",
"subject": "Asterix_(comicsCharacter)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "René_Goscinny",
"property": "creators",
"subject": "Asterix_(character)"
},
{
"object": "Albert_Uderzo",
"property": "creator",
"subject": "Asterix_(character)"
}
],
[
{
"object": "René_Goscinny",
"property": "creator",
"subject": "Asterix_(character)"
},
{
"object": "Albert_Uderzo",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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}
},
{
"279": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Created by Len Wein, Aurakles's alternative name is also Aurakles.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The character Aurakles, also known as Aurakles, was created by Len Wein.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Len_Wein",
"property": "creator",
"subject": "Aurakles"
},
{
"object": "\"Aurakles\"",
"property": "alternativeName",
"subject": "Aurakles"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Len_Wein",
"property": "creators",
"subject": "Aurakles"
},
{
"object": "\"Aurakles\"@en",
"property": "alterEgo",
"subject": "Aurakles"
}
],
[
{
"object": "Len_Wein",
"property": "creator",
"subject": "Aurakles"
},
{
"object": "\"Aurakles\"@en",
"property": "alterEgo",
"subject": "Aurakles"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id2"
}
},
{
"280": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Doug Moench was the creator of the comic character Ballistic, who has the alternative name Kelvin Mao.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ballistic, ( also known as Kelvin Mao ), is a fictional comic superhero created by Doug Moench.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Doug Moench created the character Ballistic, the fictional superhero whose alter ego is Kelvin Mao.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Doug_Moench",
"property": "creator",
"subject": "Ballistic_(comicsCharacter)"
},
{
"object": "\"Kelvin Mao\"",
"property": "alternativeName",
"subject": "Ballistic_(comicsCharacter)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Doug_Moench",
"property": "creators",
"subject": "Ballistic_(DC_Comics)"
},
{
"object": "\"Kelvin Mao\"@en",
"property": "alterEgo",
"subject": "Ballistic_(DC_Comics)"
}
],
[
{
"object": "Doug_Moench",
"property": "creator",
"subject": "Ballistic_(DC_Comics)"
},
{
"object": "\"Kelvin Mao\"@en",
"property": "alterEgo",
"subject": "Ballistic_(DC_Comics)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id3"
}
},
{
"281": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Starring Graeme Garden, Bananaman, the TV series was shown on the BBC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Graeme Garden starred in the TV series Bananman, which was shown on the BBC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman , which starred Graeme Garden, was broadcasted by the BBC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BBC",
"property": "broadcastedBy",
"subject": "Bananaman"
},
{
"object": "Graeme_Garden",
"property": "starring",
"subject": "Bananaman"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BBC",
"property": "network",
"subject": "Bananaman"
},
{
"object": "Graeme_Garden",
"property": "starring",
"subject": "Bananaman"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id4"
}
},
{
"282": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bill Oddie, who stars in Bananaman, was born in Lancashire.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman starred Bill Oddie who was born in Lancashire.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bill Oddie, who was born in Lancashire, starred in Bananaman.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bill_Oddie",
"property": "starring",
"subject": "Bananaman"
},
{
"object": "Lancashire",
"property": "birthPlace",
"subject": "Bill_Oddie"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bill_Oddie",
"property": "starring",
"subject": "Bananaman"
},
{
"object": "Lancashire",
"property": "birthPlace",
"subject": "Bill_Oddie"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id5"
}
},
{
"283": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bibbo Bibbowski was created by American, Jerry Ordway.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The creator of Bibbo Bibbowski is Jerry Ordway, an American.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Jerry Ordway, an American, is the creator of Bibbo Bibbowski.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jerry_Ordway",
"property": "creator",
"subject": "Bibbo_Bibbowski"
},
{
"object": "Americans",
"property": "nationality",
"subject": "Jerry_Ordway"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
"property": "nationality",
"subject": "Jerry_Ordway"
},
{
"object": "Jerry_Ordway",
"property": "creator",
"subject": "Bibbo_Bibbowski"
}
],
[
{
"object": "Jerry_Ordway",
"property": "creators",
"subject": "Bibbo_Bibbowski"
},
{
"object": "Americans",
"property": "nationality",
"subject": "Jerry_Ordway"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id6"
}
},
{
"284": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Black Pirate, also known as Jon Valor, was created by Sheldon Moldoff.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Black Pirate ( Jon Valor ), was created by Sheldon Moldoff.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sheldon Moldoff is the creator of Black Pirate, who is also known as Jon Valor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sheldon_Moldoff",
"property": "creator",
"subject": "Black_Pirate"
},
{
"object": "\"Jon Valor\"",
"property": "alternativeName",
"subject": "Black_Pirate"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sheldon_Moldoff",
"property": "creator",
"subject": "Black_Pirate"
},
{
"object": "\"Jon Valor\"@en",
"property": "alterEgo",
"subject": "Black_Pirate"
}
],
[
{
"object": "Sheldon_Moldoff",
"property": "creators",
"subject": "Black_Pirate"
},
{
"object": "\"Jon Valor\"@en",
"property": "alterEgo",
"subject": "Black_Pirate"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id7"
}
},
{
"285": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The comic character Blockbuster was created by Roger Stern and Tom Lyle.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Blockbuster is a comic book character which was created by both Roger Stern and Tom Lyle.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic book character Blockbuster was created by Roger Stern and Tom Lyle.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Roger_Stern",
"property": "creator",
"subject": "Blockbuster_(comicsCharacter)"
},
{
"object": "Tom_Lyle",
"property": "creator",
"subject": "Blockbuster_(comicsCharacter)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Roger_Stern",
"property": "creators",
"subject": "Blockbuster_(DC_Comics)"
},
{
"object": "Tom_Lyle",
"property": "creator",
"subject": "Blockbuster_(DC_Comics)"
}
],
[
{
"object": "Roger_Stern",
"property": "creator",
"subject": "Blockbuster_(DC_Comics)"
},
{
"object": "Tom_Lyle",
"property": "creators",
"subject": "Blockbuster_(DC_Comics)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id8"
}
},
{
"286": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Paris Culins and Gary Cohn are the creators of the comics character Bolt.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Both Paris Cullins and Gary Cohn were the creator's of the comic character Bolt.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic character Bolt, was created by Paris Cullins and Gary Cohn.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Paris_Cullins",
"property": "creator",
"subject": "Bolt_(comicsCharacter)"
},
{
"object": "Gary_Cohn_(comics)",
"property": "creator",
"subject": "Bolt_(comicsCharacter)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Paris_Cullins",
"property": "creators",
"subject": "Bolt_(DC_Comics)"
},
{
"object": "Gary_Cohn_(comics)",
"property": "creator",
"subject": "Bolt_(DC_Comics)"
}
],
[
{
"object": "Paris_Cullins",
"property": "creator",
"subject": "Bolt_(DC_Comics)"
},
{
"object": "Gary_Cohn_(comics)",
"property": "creators",
"subject": "Bolt_(DC_Comics)"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id9"
}
},
{
"287": {
"category": "Food",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bread is an ingredient of Ajoblanco which originates from Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "One of the ingredients of Ajoblanco is bread and it originates from Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bread is an ingredient of Ajoblanco which comes from Spain.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1"
}
},
{
"288": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco, from Andalusia, has almond as one of its ingredients.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Almond is an ingredient in ajoblanco, from Andalusia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is from the Andalusia region and has almond as one of its ingredients.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Almond",
"property": "ingredient",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
"property": "region",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Almond",
"property": "ingredient",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
"property": "region",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id2"
}
},
{
"289": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomato is an ingredient in Amatriciana sauce which comes from Lazio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce is from the Lazio region and tomato is one of the ingredients.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tomato is an ingredient in Amatriciana sauce which comes from the region of Lazio.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lazio",
"property": "region",
"subject": "Amatriciana_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lazio",
"property": "region",
"subject": "Amatriciana_sauce"
},
{
"object": "Tomato",
"property": "ingredient",
"subject": "Amatriciana_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id3"
}
},
{
"290": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa comes from the Antioquia Department of Colombia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a dish found in the Antioquia Department of Colombia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Colombia",
"property": "country",
"subject": "Antioquia_Department"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Colombia",
"property": "country",
"subject": "Antioquia_Department"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id4"
}
},
{
"291": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem-arem uses the ingredient banana leaf, it is a common dish in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Banana leaf is an ingredient in arem arem, which comes from Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arem arem which includes banana leaf, comes from Indonesia.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "region",
"subject": "Arem-arem"
},
{
"object": "Banana_leaf",
"property": "ingredient",
"subject": "Arem-arem"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "region",
"subject": "Arem-arem"
},
{
"object": "Banana_leaf",
"property": "ingredient",
"subject": "Arem-arem"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id5"
}
},
{
"292": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Olive oil is an ingredient used in the preparation of Arrabbiata sauce, found in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Olive Oil is used to create Italian Arrabbiata sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Olive oil is an ingredient in Arrabbiata sauce, which can be found in Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"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": "Olive_oil",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id6"
}
},
{
"293": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is found in Italy where the capital city is Rome.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rome is the capital of Italy which is where Arrabbiata sauce comes from.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is from Italy which has the capital city of Rome.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id7"
}
},
{
"294": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tomatoes are found in Arrabbiata sauce which comes from Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Tomatoes are an ingredient of Arrabbiata sauce from Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tomatoes are found in Arrabbiata sauce, which originated in Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Tomato",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id8"
}
},
{
"295": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is from Spain, it includes squid.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is a traditional dish from Spain, it includes squid.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id9"
}
},
{
"296": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient in Arros negre which comes from the region of Valencian Community.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient of arros negre is cuttlefish, the dish comes from the Valencian community region.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id10"
}
},
{
"297": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a food found in Java and the main ingredients are \"squeezed\" or \"smashed\" chicken served with sambal.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet (Javanese) includes squeezed/smashed fried chicken and is served with sambal.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
},
{
"object": "\"Squeezed\" or \"smashed\" fried chicken served with sambal",
"property": "mainIngredients",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
},
{
"object": "\"\\\"Squeezed\\\" or \\\"smashed\\\"fried chickenserved withsambal\"",
"property": "ingredientName",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id11"
}
},
{
"298": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A sausage is used when making a Bacon Explosion which has The United states to thank for inventing it.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion is a dish from the United States that contains sausage.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States and includes sausage among its ingredients.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id12"
}
},
{
"299": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States, where John Roberts is a leader, is the country of the Bacon Explosion.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion comes from the United States where John Roberts is a leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States (with a leader, John Roberts) boasts the Bacon Explosion.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leaderName",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leader",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id13"
}
},
{
"300": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon is an ingredient in the main course dish 'Bacon Explosion'.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon is an ingredient in a main course called Bacon Explosion.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion is an entree that includes bacon, the main ingredient.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "\"Main course\"",
"property": "course",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "\"Main course\"@en",
"property": "course",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id14"
}
},
{
"301": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A bacon sandwich on bread is also known as 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": "The bacon sandwich, which one of two ingedients is bread, 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": "Bread",
"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": "Bread",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id15"
}
},
{
"302": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is thought to have originated in the United States, France or China, and contains Christmas pudding as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "France, United States and China all claim to have invented Baked Alaska which can have Christmas pudding as an ingredient.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Christmas pudding is an ingredient in Baked Alaska which is from France, the United States and China.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"France, United States or China\"",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"France, United States or China\"@en",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id16"
}
},
{
"303": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from China, where Standard Chinese is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Standard Chinese is spoken in China where baked Alaska is a dish.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "China",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "China"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "China",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "China"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id17"
}
},
{
"304": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska and cookies are types of dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska and a cookie are both desserts.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A Baked Alaska is a dessert along with cookies which you can also eat as a dessert.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id18"
}
},
{
"305": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding was created in 1820 at the Rutland Arms, Bakewell and has ground almond, jam, butter and eggs as main ingredients.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bakewell pudding, which was invented in Rutland Arms, Bakewell, in 1820, are ground almond, jam, butter and eggs.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Rutland Arms, Bakewell, in 1820\"",
"property": "creator",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "mainIngredients",
"subject": "Bakewell_pudding"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Rutland Arms, Bakewell, in 1820\"@en",
"property": "creator",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"@en",
"property": "mainIngredient",
"subject": "Bakewell_pudding"
}
],
[
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "ingredientName",
"subject": "Bakewell_pudding"
},
{
"object": "\"Rutland Arms, Bakewell, in 1820\"@en",
"property": "creator",
"subject": "Bakewell_pudding"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id19"
}
},
{
"306": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart, made with shortcrust pastry, is a variation of Bakewell pudding.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A variant of bakewell pudding is bakewell tart, made with shortcrust pastry.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A variant of bakewell pudding is bakewell tart which uses shortcrust pastry.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Shortcrust_pastry",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Shortcrust_pastry",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id20"
}
},
{
"307": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Lemon (part of genus Citrus) is an ingredient in Bandeja paisa.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Lemon, from the genus citrus, is an ingredient in Bandeja paisa.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Lemon (genus; citrus) is an ingredient found in Bandeja paisa.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Citrus",
"property": "genus",
"subject": "Lemon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Citrus",
"property": "genus",
"subject": "Lemon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id21"
}
},
{
"308": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a food in the Philippines and one of its ingredient is shrimp.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Philippines is the country of origin for the dish Batchoy, of which shrimp is one of the main ingredients.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Philippines is the country that Batchoy, a dish containing shrimp, comes from.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Shrimp",
"property": "ingredient",
"subject": "Batchoy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Shrimp",
"property": "ingredient",
"subject": "Batchoy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id22"
}
},
{
"309": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Chinese Filipino is an ethnic group in the Philippines, where batchoy is a traditional dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Chinese Filipino are from the Philippines, where batchoy is eaten.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is a food from the Philippines, the country where there is an ethnic group called the Chinese Filipino.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Chinese_Filipino",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Chinese_Filipino",
"property": "ethnicGroup",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id23"
}
},
{
"310": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow originates from Singapore, where Chinese is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Standard chinese speaking Singapore is the origin of beef kway teow.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id24"
}
},
{
"311": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish of Singapore where Tony Tan is a leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Tony Tan is the leader of Singapore, from where Beef kway teow originates.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow originates from Singapore and it's leader is Tony Tan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id25"
}
},
{
"312": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India and Sumitra Mahajan is it's leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from India where the leader is Sumitra Mahajan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India where the leader is Sumitra Mahajan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Sumitra_Mahajan",
"property": "leader",
"subject": "India"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id26"
}
},
{
"313": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji, which uses gram flour, is a dish similar to pakora and other fritters made from wheat or corn flour.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Gram flour is an ingredient of a Bhajji, which is a similar dish to pakora and other fritters made from wheat or corn flour.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Pakora and other fritters made from wheat or corn flour\"",
"property": "similarDish",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Pakora and other fritters made from wheat or corn flour\"@en",
"property": "similarDish",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id27"
}
},
{
"314": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "An ingredient of Binignit is sweet potato, and one of the main ingredients is the banana.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Banana is a main ingredient of Binignit and sweet potato is also an ingredient.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredient of Binignit is banana, and also important is the inclusion of sweet potatoes.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "mainIngredients",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "mainIngredient",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id28"
}
},
{
"315": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sago is the main ingredient in binignit but sweet potatoes are also used in it.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Binignit has the ingredients sweet potato and sago.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sweet potato and sago are main ingredients in Binignit.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Sago",
"property": "mainIngredients",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Sago",
"property": "mainIngredient",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id29"
}
},
{
"316": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sweet potatoes (a kind of flowering plant) are used in binignit recipes.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sweet potato belongs to flowering plants and is an ingredient of Binignit.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Sweet_potato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Flowering_plant",
"property": "division",
"subject": "Sweet_potato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id30"
}
},
{
"317": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sweet potatoes are the main ingredient of binignit and the potatoes are part of the order of Solanales.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sweet potatoes are members of the Solanales order of plants. They are the main ingredient of binignit.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sweet potato is of the order Solanales and are a main ingredient of binignit.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id31"
}
},
{
"318": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert and it requires granola as one of it's ingredients.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert, requiring granola as one of its ingredients.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert that includes the ingredient granola.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id32"
}
},
{
"319": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A popular dessert is Bionico and a variation of this dish can be honey.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Honey can be used as a variaton in Bionico which is a dessert.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Honey can be added to bionico, a dessert dish.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Honey",
"property": "dishVariation",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Honey",
"property": "hasVariant",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id33"
}
},
{
"320": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico and ice cream are both served as a dessert course.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico and ice cream are types of dessert.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico and Ice Cream are served at the dessert course.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ice_cream",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ice_cream",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id34"
}
},
{
"321": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor and Siomay (which has peanut sauce as an ingredient) are variations of the same dish.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Peanut sauce is an ingredient in Siomay and Batagor is a variation of this dish.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor is a variation of the dish Siomay, of which peanut sauce is an ingredient.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Siomay"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id35"
}
},
{
"322": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The inauguration date for the Atatürk Monument (İzmir, Turkey) is 1932-07-27.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Ataturk Monument is in Izmir Turkey and was founded on July 27, 1932.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The inauguration date for the Atatürk Monument (İzmir), which is in Turkey, is 1932-07-27.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"1932-07-27\"",
"property": "inaugurationDate",
"subject": "Atatürk_Monument_(İzmir)"
},
{
"object": "Turkey",
"property": "location",
"subject": "Atatürk_Monument_(İzmir)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"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": "2",
"xml_id": "Id1"
}
},
{
"323": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial has been dedicated to Ottoman Army soldiers killed in the Battle of Baku and is made of red granite and white marble.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial, created in red granite and white marble, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"",
"property": "dedicatedTo",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Red granite and white marble\"",
"property": "material",
"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": "\"Red granite and white marble\"@en",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id2"
}
},
{
"324": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The native name of the Baku Turkish Martyrs' Memorial is Turk Sehitleri Aniti and is made of red granite and white marble.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyr's Memorial is known in Turkish as Turk Sehitleri Aniti and is created in red granite and white marble.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baku Turkish Martyrs' Memorial is made from red granite and white marble and is called Turk Sehitleri Aniti.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Türk Şehitleri Anıtı\"",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Red granite and white marble\"",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Türk Şehitleri Anıtı\"@en",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Red granite and white marble\"@en",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id3"
}
},
{
"325": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Dead Man's Plack, which is made of rock, is dedicated to Æthelwald, Ealdorman of East Anglia.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Æthelwald,_Ealdorman_of_East_Anglia",
"property": "dedicatedTo",
"subject": "Dead_Man's_Plack"
},
{
"object": "Rock_(geology)",
"property": "material",
"subject": "Dead_Man's_Plack"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Æthelwald,_Ealdorman_of_East_Anglia",
"property": "dedicatedTo",
"subject": "Dead_Man's_Plack"
},
{
"object": "Rock_(geology)",
"property": "material",
"subject": "Dead_Man's_Plack"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id4"
}
},
{
"326": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AC Cesena are in the Serie B league, the previous champions of which are Carpi FC 1909.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.C. Cesana play in Serie B, in which Carpi FC 1909 are the champions.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AC Cesena are in the Serie B league, previously won by Carpi F.C. 1909.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Serie_B",
"property": "league",
"subject": "A.C._Cesena"
},
{
"object": "Carpi_F.C._1909",
"property": "champions",
"subject": "Serie_B"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Serie_B",
"property": "league",
"subject": "A.C._Cesena"
},
{
"object": "Carpi_F.C._1909",
"property": "champions",
"subject": "Serie_B"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id1"
}
},
{
"327": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AC Chievo Verona play in the Serie A league and their home ground is Stadio Marc Antonio Bentegodi.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The home ground of A.C. Chievo Verona is Stadio Marc'Antonio Bentegodi and the club is in the league, Serie A.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The home ground of A.C. Chievo Verona is Stadio Marc'Antonio Bentegodi, he played in Serie A.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stadio_Marc'Antonio_Bentegodi",
"property": "ground",
"subject": "A.C._Chievo_Verona"
},
{
"object": "Serie_A",
"property": "league",
"subject": "A.C._Chievo_Verona"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stadio_Marc'Antonio_Bentegodi",
"property": "ground",
"subject": "A.C._Chievo_Verona"
},
{
"object": "Serie_A",
"property": "league",
"subject": "A.C._Chievo_Verona"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id2"
}
},
{
"328": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.C. Lumezzane's ground is in Italy where the leader is Pietro Grasso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.C. Lumezzane play in italy where the leader is Pietro Grasso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.C. Lumezzane play in Italy, where the leader is Pietro Grasso.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "ground",
"subject": "A.C._Lumezzane"
},
{
"object": "Pietro_Grasso",
"property": "leader",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "ground",
"subject": "A.C._Lumezzane"
},
{
"object": "Pietro_Grasso",
"property": "leader",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id3"
}
},
{
"329": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodríguez manages the A.D. Isidro Metapan, he played for the C.D. FAS.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodriguez manages A.D. Isidro Metapan. He is in the C.D. FAS.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Jorge Humberto Rodríguez manages the A.D. Isidro Metapan and plays for C.D. FAS.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jorge_Humberto_Rodríguez",
"property": "manager",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "C.D._FAS",
"property": "club",
"subject": "Jorge_Humberto_Rodríguez"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jorge_Humberto_Rodríguez",
"property": "manager",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "C.D._FAS",
"property": "team",
"subject": "Jorge_Humberto_Rodríguez"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id4"
}
},
{
"330": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.E. Dimitra Efeinoupolis has 1500 members and is in the EPSTH 2nd GROUP league.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "With 1500 members, A.E Dimitra Efxeinoupolis, play in the A EPSTH 2nd GROUP.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1500",
"property": "numberOfMembers",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "A_EPSTH_2nd_GROUP",
"property": "league",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"1500\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "A_EPSTH_2nd_GROUP",
"property": "league",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
],
[
{
"object": "1500",
"property": "capacity",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "A_EPSTH_2nd_GROUP",
"property": "league",
"subject": "A.E_Dimitra_Efxeinoupolis"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id5"
}
},
{
"331": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker (footballer) who plays for Runcorn F.C. Halton.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had Stuart Parker as their manager who was a football player for Runcorn FC Halton.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker (footballer), he played for Runcorn FC Halton.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Runcorn_F.C._Halton",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Runcorn_F.C._Halton",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id6"
}
},
{
"332": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker (footballer), who plays at the Sparta Rotterdam club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker, who plays at the Sparta Rotterdam Club, has also managed AFC Blackpool.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had the manager Stuart Parker who currently plays for Sparta Rotterdam.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Sparta_Rotterdam",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Sparta_Rotterdam",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id7"
}
},
{
"333": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Fylde has the home ground called The Fylde and has 3180 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Fylde has the home ground called The Fylde, which holds 3180 fans.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Fylde, (capacity 3180) is the home ground of AFC Fylde.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "The_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
},
{
"object": "3180",
"property": "numberOfMembers",
"subject": "A.F.C._Fylde"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "The_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
},
{
"object": "\"3180\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.F.C._Fylde"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id8"
}
},
{
"334": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.F.C. Fylde has 3180 members and are based at Bryning Lane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Fylde has 3180 members and are based at Bryning Lane.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.F.C. Fylde are based at Bryning Lane and they have 3180 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "3180",
"property": "numberOfMembers",
"subject": "A.F.C._Fylde"
},
{
"object": "\"Bryning Lane\"",
"property": "ground",
"subject": "A.F.C._Fylde"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"3180\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.F.C._Fylde"
},
{
"object": "\"Bryning Lane\"@en",
"property": "ground",
"subject": "A.F.C._Fylde"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id9"
}
},
{
"335": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio is managed by Christian Panucci who plays for the Italy national football team.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio are managed by Christian Panucci who played for the Italy national football team.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio is managed by Christian Panucci who previously played for the Italian national football team.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Christian_Panucci",
"property": "manager",
"subject": "A.S._Livorno_Calcio"
},
{
"object": "Italy_national_football_team",
"property": "club",
"subject": "Christian_Panucci"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Christian_Panucci",
"property": "manager",
"subject": "A.S._Livorno_Calcio"
},
{
"object": "Italy_national_football_team",
"property": "team",
"subject": "Christian_Panucci"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id10"
}
},
{
"336": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Grounds being in Rome, Italy, A.S. Roma to play in Serie A.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Roma play in the Serie A league and their ground is in Rome, Italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Roma's ground is in Rome, Italy and they play in Serie A.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Rome, Italy\"",
"property": "ground",
"subject": "A.S._Roma"
},
{
"object": "Serie_A",
"property": "league",
"subject": "A.S._Roma"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Rome, Italy\"@en",
"property": "ground",
"subject": "A.S._Roma"
},
{
"object": "Serie_A",
"property": "league",
"subject": "A.S._Roma"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id11"
}
},
{
"337": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "ACF Fiorentina play in Serie A which is based in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "ACF Fiorentina play in Serie A based in Italy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Serie_A",
"property": "league",
"subject": "ACF_Fiorentina"
},
{
"object": "Italy",
"property": "country",
"subject": "Serie_A"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Serie_A",
"property": "league",
"subject": "ACF_Fiorentina"
},
{
"object": "Italy",
"property": "country",
"subject": "Serie_A"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id12"
}
},
{
"338": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of AFC Ajax is Frank de Boer who is in the Ajax Youth Academy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax's manager is Frank de Boer, who played for the Ajax Youth Academy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Frank de Boer is in the Ajax Youth Academy and manages AFC Ajax.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Frank_de_Boer",
"property": "manager",
"subject": "AFC_Ajax"
},
{
"object": "Ajax_Youth_Academy",
"property": "club",
"subject": "Frank_de_Boer"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Frank_de_Boer",
"property": "manager",
"subject": "AFC_Ajax"
},
{
"object": "Ajax_Youth_Academy",
"property": "team",
"subject": "Frank_de_Boer"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id13"
}
},
{
"339": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax has 53502 members and is the owner of AFC Ajax NV.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax has 53502 members and is owned by AFC Ajax NV.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax has 53502 members and is owned by AFC Ajax N.V..",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "AFC_Ajax_N.V.",
"property": "owner",
"subject": "AFC_Ajax"
},
{
"object": "53502",
"property": "numberOfMembers",
"subject": "AFC_Ajax"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"AFC Ajax NV\"@en",
"property": "owner",
"subject": "AFC_Ajax"
},
{
"object": "\"53502\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AFC_Ajax"
}
],
[
{
"object": "AFC_Ajax_N.V.",
"property": "owner",
"subject": "AFC_Ajax"
},
{
"object": "53502",
"property": "capacity",
"subject": "AFC_Ajax"
}
],
[
{
"object": "AFC_Ajax_N.V.",
"property": "owner",
"subject": "AFC_Ajax"
},
{
"object": "\"53502\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AFC_Ajax"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id14"
}
},
{
"340": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax is based in Amsterdam where the leader is Eberhard van der Laan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of AFC Ajax (amateurs) can be found in Amsterdam where Eberhard van der Laan is a leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax is based in Amsterdam where Eberhard van der Laan is a leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "ground",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "Eberhard_van_der_Laan",
"property": "leader",
"subject": "Amsterdam"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Amsterdam",
"property": "ground",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "Eberhard_van_der_Laan",
"property": "leaderName",
"subject": "Amsterdam"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id15"
}
},
{
"341": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs), who have 5000 members, played in the Topklasse in the 2014-2015 season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs) have 5000 members and played in the Topklasse in the 2014-2015 season.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (amateurs) played in the 2014-15 Topklasse season and their home ground has a capacity for 5000 fans.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "2014–15_Topklasse",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "5000",
"property": "numberOfMembers",
"subject": "AFC_Ajax_(amateurs)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"5000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "2014–15_Topklasse",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
}
],
[
{
"object": "2014–15_Topklasse",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "5000",
"property": "capacity",
"subject": "AFC_Ajax_(amateurs)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id16"
}
},
{
"342": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZAL PFK play their home matches at the AZAL Arena in Shuvulan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The AZAL Arena is located in Shuvulan and is the ground of AZAL PFK.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The AZAL Arena in Shuvalan is the ground of AZAL PFK.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Shuvalan",
"property": "location",
"subject": "AZAL_Arena"
},
{
"object": "AZAL_Arena",
"property": "ground",
"subject": "AZAL_PFK"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Shuvalan",
"property": "location",
"subject": "AZAL_Arena"
},
{
"object": "AZAL_Arena",
"property": "ground",
"subject": "AZAL_PFK"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id17"
}
},
{
"343": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZ Alkmaar's fullname is Alkmaar Zaanstreek, it has 17023 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AZ Alkmaar has 17023 member and their full name is \"Alkmaar Zaanstreek\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alkmaar Zaanstreek is the full name of AZ Alkmaar which has 17023 members.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Alkmaar Zaanstreek\"",
"property": "fullname",
"subject": "AZ_Alkmaar"
},
{
"object": "17023",
"property": "numberOfMembers",
"subject": "AZ_Alkmaar"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Alkmaar Zaanstreek\"@en",
"property": "fullname",
"subject": "AZ_Alkmaar"
},
{
"object": "\"17023\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AZ_Alkmaar"
}
],
[
{
"object": "\"Alkmaar Zaanstreek\"@en",
"property": "fullname",
"subject": "AZ_Alkmaar"
},
{
"object": "17023",
"property": "capacity",
"subject": "AZ_Alkmaar"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id18"
}
},
{
"344": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agremiacao Sportiva Arapiraquense's ground is the Estadio Minicipal Coaracy da Mata Fonseca, its located in Brazil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Estádio Municipal Coaracy da Mata Fonseca is located in Brazil and is the ground of Agremiação Sportiva Arapiraquense.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Estádio Municipal Coaracy da Mata Fonseca is the name of the ground of Agremiação Sportiva Arapiraquense which is in Brazil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Estádio_Municipal_Coaracy_da_Mata_Fonseca",
"property": "ground",
"subject": "Agremiação_Sportiva_Arapiraquense"
},
{
"object": "Brazil",
"property": "location",
"subject": "Estádio_Municipal_Coaracy_da_Mata_Fonseca"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Estádio_Municipal_Coaracy_da_Mata_Fonseca",
"property": "ground",
"subject": "Agremiação_Sportiva_Arapiraquense"
},
{
"object": "Brazil",
"property": "location",
"subject": "Estádio_Municipal_Coaracy_da_Mata_Fonseca"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id19"
}
},
{
"345": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense (which is his full name) has a club with 17000 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense's full name is \"Agremiação Sportiva Arapiraquense\" and they have 17000 members.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "17000",
"property": "numberOfMembers",
"subject": "Agremiação_Sportiva_Arapiraquense"
},
{
"object": "\"Agremiação Sportiva Arapiraquense\"",
"property": "fullname",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"17000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "Agremiação_Sportiva_Arapiraquense"
},
{
"object": "\"Agremiação Sportiva Arapiraquense\"@en",
"property": "fullname",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
],
[
{
"object": "17000",
"property": "capacity",
"subject": "Agremiação_Sportiva_Arapiraquense"
},
{
"object": "\"Agremiação Sportiva Arapiraquense\"@en",
"property": "fullname",
"subject": "Agremiação_Sportiva_Arapiraquense"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id20"
}
},
{
"346": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The team Akron Summit Assault are based in Akron, Ohio, USA.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Akron Summit Assault team is based in Akron, Ohio which is in the U.S.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Akron Summit Assault team is based in Akron, Ohio in the U.S.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Akron,_Ohio",
"property": "ground",
"subject": "Akron_Summit_Assault"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Akron,_Ohio",
"property": "ground",
"subject": "Akron_Summit_Assault"
},
{
"object": "United_States",
"property": "country",
"subject": "Akron,_Ohio"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id21"
}
},
{
"347": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago's club is the S.S. Chieti Calcio and he manages A.C. Cesena.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago manages A.C. Cesena and plays for S.S. Chieti Calcio.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "S.S._Chieti_Calcio",
"property": "club",
"subject": "Massimo_Drago"
},
{
"object": "Massimo_Drago",
"property": "manager",
"subject": "A.C._Cesena"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "S.S._Chieti_Calcio",
"property": "team",
"subject": "Massimo_Drago"
},
{
"object": "Massimo_Drago",
"property": "manager",
"subject": "A.C._Cesena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "2",
"xml_id": "Id22"
}
},
{
"348": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090 and the director is Dr. G. P. Prabhukumar.",
"xml_id": "Id1"
},
{
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"subject": "Abilene,_Texas"
}
],
[
{
"object": "Abilene,_Texas",
"property": "city",
"subject": "Abilene_Regional_Airport"
},
{
"object": "Taylor_County,_Texas",
"property": "subdivisionName",
"subject": "Abilene,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Abilene,_Texas"
}
],
[
{
"object": "Abilene,_Texas",
"property": "cityServed",
"subject": "Abilene_Regional_Airport"
},
{
"object": "Taylor_County,_Texas",
"property": "subdivisionName",
"subject": "Abilene,_Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Abilene,_Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1"
}
},
{
"380": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport has a runway length of 1219.0 and serves the cities of Saranac Lake and Lake Placid, New York.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport serves the city of Saranac Lake, New York and the residents of Lake Placid and has a runway that's 1,219 long.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1219.0",
"property": "runwayLength",
"subject": "Adirondack_Regional_Airport"
},
{
"object": "Lake_Placid,_New_York",
"property": "cityServed",
"subject": "Adirondack_Regional_Airport"
},
{
"object": "Saranac_Lake,_New_York",
"property": "cityServed",
"subject": "Adirondack_Regional_Airport"
}
],
"originaltriplesets": {
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[
{
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"property": "runwayLength",
"subject": "Adirondack_Regional_Airport"
},
{
"object": "Lake_Placid,_New_York",
"property": "city",
"subject": "Adirondack_Regional_Airport"
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{
"object": "Saranac_Lake,_New_York",
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],
[
{
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{
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"property": "runwayLength",
"subject": "Adirondack_Regional_Airport"
},
{
"object": "Saranac_Lake,_New_York",
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],
[
{
"object": "\"1218.59\"^^xsd:double",
"property": "runwayLength",
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},
{
"object": "Lake_Placid,_New_York",
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"subject": "Adirondack_Regional_Airport"
},
{
"object": "Saranac_Lake,_New_York",
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"subject": "Adirondack_Regional_Airport"
}
],
[
{
"object": "Lake_Placid,_New_York",
"property": "cityServed",
"subject": "Adirondack_Regional_Airport"
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{
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{
"object": "Saranac_Lake,_New_York",
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]
]
},
"shape": "NA",
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"xml_id": "Id2"
}
},
{
"381": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 14L/32R runway of the Adolfo Suarez Madrid - Barajas Airport in Madrid has a length of 4100.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport is in Madrid; its runway name is 14L/32R and its runway length is 4100.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Adolfo Suarez Madrid-Barajas Airport is in Madrid. Its runway, called 14L/32R has length of 4100.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "4100.0",
"property": "runwayLength",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
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{
"object": "Madrid",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"14L/32R\"",
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"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
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],
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{
"object": "Madrid",
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"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "\"14L/32R\"",
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]
]
},
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"xml_id": "Id3"
}
},
{
"382": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Indian Air Force in India, operates Agra Airport which uses VIAG as its ICAO location identifier.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agra Airport, located in India, is operated by the Indian Air Force, and has an ICAO identifier is VIAG.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "location",
"subject": "Agra_Airport"
},
{
"object": "Indian_Air_Force",
"property": "operatingOrganisation",
"subject": "Agra_Airport"
},
{
"object": "\"VIAG\"",
"property": "ICAO_Location_Identifier",
"subject": "Agra_Airport"
}
],
"originaltriplesets": {
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{
"object": "India",
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{
"object": "Indian_Air_Force",
"property": "operator",
"subject": "Agra_Airport"
},
{
"object": "\"VIAG\"",
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],
[
{
"object": "India",
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},
{
"object": "Indian_Air_Force",
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},
{
"object": "\"(Location of airport in India)\"",
"property": "icaoLocationIdentifier",
"subject": "Agra_Airport"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id4"
}
},
{
"383": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Operated by the United States Air Force, Al Asad Airbase, in Iraq, has a runway length of 3078.48 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The USAF operated the Al Asad Airbase, located in iraq, with a length of 3078.48.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "Iraq",
"property": "location",
"subject": "Al_Asad_Airbase"
},
{
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],
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"object": "Iraq",
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{
"object": "\"3078.48\"^^xsd:double",
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},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id5"
}
},
{
"384": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad airbase is operated by the United States Air Force which was involved in the Invasion of Grenada and the Korean war.",
"xml_id": "Id1"
}
],
"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": "Korean_War",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"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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{
"object": "Korean_War",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id6"
}
},
{
"385": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is operated by the United States Air Force which has the Invasion of Grenada as one of its noted battles as well as The Operation Enduring Freedom.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The USAF operates Al Asad Airbase. They fought in the Invasion of Grenada and The Operation Enduring Freedom.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The US Air Force is the operating organisation for Al Asad Airbase, used in battles such as Invasion of Grenada and the operation enduring freedom.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
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{
"object": "Invasion_of_Grenada",
"property": "battles",
"subject": "United_States_Air_Force"
},
{
"object": "Operation_Enduring_Freedom",
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}
],
"originaltriplesets": {
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{
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"subject": "Al_Asad_Airbase"
},
{
"object": "Invasion_of_Grenada",
"property": "battle",
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},
{
"object": "Operation_Enduring_Freedom",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id7"
}
},
{
"386": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force who is the operating organisation for Al Asad airbase fought battles in the Korean war and also in Operation Enduring Freedom.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The US Air Force, veteran of the Korean war and Operation Enduring Freedom, operated Al Asad Airbase,.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States_Air_Force",
"property": "operatingOrganisation",
"subject": "Al_Asad_Airbase"
},
{
"object": "Korean_War",
"property": "battles",
"subject": "United_States_Air_Force"
},
{
"object": "Operation_Enduring_Freedom",
"property": "battles",
"subject": "United_States_Air_Force"
}
],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "United_States_Air_Force",
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"object": "Korean_War",
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},
{
"object": "Operation_Enduring_Freedom",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id8"
}
},
{
"387": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 1st runway at Alderney Airport is made from Poaceae and has a length of 733.0.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Poaceae",
"property": "1st_runway_SurfaceType",
"subject": "Alderney_Airport"
},
{
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"property": "runwayLength",
"subject": "Alderney_Airport"
},
{
"object": "Alderney",
"property": "cityServed",
"subject": "Alderney_Airport"
}
],
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{
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"property": "runwayLength",
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},
{
"object": "Alderney",
"property": "city",
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}
],
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{
"object": "Poaceae",
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},
{
"object": "Alderney",
"property": "city",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id9"
}
},
{
"388": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Located in Wilson Township, Alpena County, Michigan, Alpena County Regional Airport has an elevation of 210 metres above sea level and a runway length of 1,533 metres.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Aplena County Regional Airport; is elevated 210 metres above sea level, has a runway length of 1,533 metres and is located in Wilson Township, Alpena County, Michigan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport is located in the Wilson Township, Alpena County, Michigan. It is 210 m above sea level and its runway length is 1533 feet.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
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"object": "Wilson_Township,_Alpena_County,_Michigan",
"property": "location",
"subject": "Alpena_County_Regional_Airport"
},
{
"object": "1533.0",
"property": "runwayLength",
"subject": "Alpena_County_Regional_Airport"
},
{
"object": "210",
"property": "elevationAboveTheSeaLevel_(in_metres)",
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],
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],
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{
"object": "\"210.312\"^^xsd:double",
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}
],
[
{
"object": "Wilson_Township,_Alpena_County,_Michigan",
"property": "location",
"subject": "Alpena_County_Regional_Airport"
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"property": "runwayLength",
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},
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],
[
{
"object": "Wilson_Township,_Alpena_County,_Michigan",
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id10"
}
},
{
"389": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Amsterdam airport, Schipol which serves the city of Amsterdam is located at -3.3528 metres above sea level and has a runway length of 3500.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Amsterdam",
"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "3500.0",
"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "-3.3528",
"property": "elevationAboveTheSeaLevel_(in_metres)",
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],
"originaltriplesets": {
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{
"object": "Amsterdam",
"property": "city",
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"property": "runwayLength",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"-3.3528\"^^xsd:double",
"property": "elevation",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id11"
}
},
{
"390": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas (United States), where Houston is the largest city.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is in Texas, United States. Houston is the largest city in Texas.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Andrews County Airport is located in Texas, whose largest city is Houston.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Houston",
"property": "largestCity",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
],
"originaltriplesets": {
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{
"object": "Texas",
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"object": "Houston",
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"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id12"
}
},
{
"391": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International airport is located in Icolo e Bengo Angola, it has a runway length of 4000ft.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located at Ícolo e Bengo, Angola and is 4000 in length.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport, located in Icolo e Bengo, Angola has a runway length of 4000.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "4000.0",
"property": "runwayLength",
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}
],
"originaltriplesets": {
"originaltripleset": [
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{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "\"3999.89\"^^xsd:double",
"property": "runwayLength",
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],
[
{
"object": "Ícolo_e_Bengo",
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},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "\"4000.0\"^^xsd:double",
"property": "runwayLength",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id13"
}
},
{
"392": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located at Ícolo e Bengo in Angola and the runway is named \"south runway\".",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located at Ícolo e Bengo, Angola and the runway is known as \"South Runway\".",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "location",
"subject": "Angola_International_Airport"
},
{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "\"South Runway\"",
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],
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{
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{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "\"South Runway\"",
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},
"shape": "NA",
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"xml_id": "Id14"
}
},
{
"393": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport which has an elevation of 12.0 metres above sea level is owned and operated by the Flemish government.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport which is 12 meters above sea level is owned by Flemish Region and operated by the Flemish government.",
"xml_id": "Id2"
}
],
"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)",
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],
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{
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"subject": "Antwerp_International_Airport"
},
{
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[
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{
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},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id15"
}
},
{
"394": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport; serves the city of Antwerp, is in the Flemish region and is operated by the Flemish department of mobility and public works.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves Antwerp. It is owned by the Flemish Region and operated by the Flemish department of Mobility and Public Works.",
"xml_id": "Id3"
}
],
"links": [],
"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": "Id16"
}
},
{
"395": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Appleton International airport serves the city of Appleton which is part of Kimberly, Wisconsin, as is Little Chute.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Appletone International Airport served the city of Appleton, which is part of a little chute in Kimberly, Wisconsin.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Kimberly,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
},
{
"object": "Little_Chute,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
},
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
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],
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{
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{
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},
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
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],
[
{
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},
{
"object": "Little_Chute,_Wisconsin",
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{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
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],
[
{
"object": "Kimberly,_Wisconsin",
"property": "isPartOf",
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},
{
"object": "Little_Chute,_Wisconsin",
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"subject": "Appleton,_Wisconsin"
},
{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
}
],
[
{
"object": "Kimberly,_Wisconsin",
"property": "isPartOf",
"subject": "Appleton,_Wisconsin"
},
{
"object": "Little_Chute,_Wisconsin",
"property": "subdivisionName",
"subject": "Appleton,_Wisconsin"
},
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id17"
}
},
{
"396": {
"category": "Airport",
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{
"comment": "good",
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"lex": "The Appleton International Airport is in Greenville which is part of Clayton Winnebago County, Wisconsin, in the United States.",
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},
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport is located in Greenville, Wisconsin, USA, which is part of Clayton, Winnebago county.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Appleton International Airport is in Greenville, Clayton, Winnebago County, Wisconsin, United States.",
"xml_id": "Id3"
}
],
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{
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"property": "location",
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},
{
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"subject": "Greenville,_Wisconsin"
},
{
"object": "Clayton,_Winnebago_County,_Wisconsin",
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],
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{
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{
"object": "United_States",
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"subject": "Greenville,_Wisconsin"
},
{
"object": "Clayton,_Winnebago_County,_Wisconsin",
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]
]
},
"shape": "NA",
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"xml_id": "Id18"
}
},
{
"397": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae which is of the order Poales and belongs to the division of flowering plants.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 3rd runway at Ardmore Airport (New Zealand) is made of Poaceae which is part of the Poales order and is in the division of flowering plants.",
"xml_id": "Id2"
}
],
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{
"object": "Flowering_plant",
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{
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{
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},
"shape": "NA",
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"xml_id": "Id19"
}
},
{
"398": {
"category": "Airport",
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{
"comment": "good",
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"lex": "Ashgabat International Airport (located in Ashgabat) is elevated 211 metres above sea level and has a runway length of 3800.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ashgabat is the location of Ashgabat International Airport which has a runway length of 3800.0 and is 211 metres above sea level.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ashgabat International airport is located in Ashgabat at 211 metres above sea level and has a runway length of 3800.0.",
"xml_id": "Id3"
}
],
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{
"object": "Ashgabat",
"property": "location",
"subject": "Ashgabat_International_Airport"
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{
"object": "3800.0",
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{
"object": "211",
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{
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],
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"object": "Ashgabat",
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],
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]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id20"
}
},
{
"399": {
"category": "Airport",
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{
"comment": "good",
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"lex": "The Athens International Airport serves the city of Athens, in Greece where Alexis Tsipras is the leader.",
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},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport serves the city of Athens in Greece where Alexis Tsipras is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Athens International Airport serves the city of Athens in Greece, a country led by Alexis Tsipras.",
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}
],
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{
"object": "Athens",
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{
"object": "Greece",
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{
"object": "Alexis_Tsipras",
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],
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{
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{
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"object": "Greece",
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"object": "Alexis_Tsipras",
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]
]
},
"shape": "NA",
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"xml_id": "Id21"
}
},
{
"400": {
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{
"comment": "good",
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"lex": "Atlantic City International Airport, which has a runway with the name is 4/22, is located in Egg Harbor Township, in New Jersey, United States.",
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},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City International Airport is in Egg Harbor Township, New Jersey, United States and has the runway name 4/22.",
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}
],
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"object": "\"4/22\"",
"property": "runwayName",
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"object": "Egg_Harbor_Township,_New_Jersey",
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{
"object": "United_States",
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],
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},
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},
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"401": {
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"comment": "good",
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},
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"comment": "good",
"lang": "",
"lex": "Antwerp, Belgium, is led by Philippe of Belgium and served by Antwerp International Airport.",
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}
],
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},
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{
"object": "Belgium",
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],
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],
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]
]
},
"shape": "NA",
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"xml_id": "Id23"
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},
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"402": {
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"comment": "good",
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"lex": "Poaceae is in the class Monocotyledon and the order of Commelinids. It is also the surface type of the second runway of Ardmore Airport, New Zealand.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "The 2nd runway at Ardmore Airport (New Zealand) is made of Poaceae which belongs to the order of Commelinids and the class of Monocotyledon.",
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}
],
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},
"shape": "NA",
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},
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"403": {
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"comment": "good",
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},
{
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"lang": "",
"lex": "Alan Bean was a member of the NASA operated Apollo 12 crew commanded by David Scott.",
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},
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"comment": "good",
"lang": "",
"lex": "David Scott was the commander of the NASA operated Apollo 12 flight mission on which the crew included Alan Bean.",
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],
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"modifiedtripleset": [
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]
},
"shape": "NA",
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},
{
"404": {
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"comment": "good",
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"lex": "Dianne Feinstein is a senator in California where Alan Shepard died on July 21 1998.",
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},
{
"comment": "good",
"lang": "",
"lex": "Alan Shephard died on the 21st of July, 1998 in California (whose Senator was Dianne Feinstein).",
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}
],
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"modifiedtripleset": [
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"object": "\"1998-07-21\"",
"property": "deathDate",
"subject": "Alan_Shepard"
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{
"object": "California",
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"object": "Dianne_Feinstein",
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},
"shape": "NA",
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},
{
"405": {
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"comment": "good",
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},
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"object": "Spain",
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"subject": "Ajoblanco"
},
{
"object": "\"Ajo blanco\"",
"property": "alternativeName",
"subject": "Ajoblanco"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "\"Ajo blanco\"@en",
"property": "alias",
"subject": "Ajoblanco"
},
{
"object": "Bread",
"property": "ingredient",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1"
}
},
{
"446": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is from Andalusia, in Spain. Ajoblanco contains water.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Water is an ingredient in Ajoblanco, which is a food found in Andalusia, in Spain.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
"property": "region",
"subject": "Ajoblanco"
},
{
"object": "Water",
"property": "ingredient",
"subject": "Ajoblanco"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
"property": "region",
"subject": "Ajoblanco"
},
{
"object": "Water",
"property": "ingredient",
"subject": "Ajoblanco"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id2"
}
},
{
"447": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem arem originates from the country of Indonesia, where two of the leaders are, Joko Widodo and Jusuf Kalla.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arem arem originates from Indonesia where two of the leaders are Joko Widodo and Jusef Kalla.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Joko_Widodo",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Joko_Widodo",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Joko_Widodo",
"property": "leaderName",
"subject": "Indonesia"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id3"
}
},
{
"448": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arem-arem is a food found nationwide in Indonesia, but is more specific to Java. Jusuf Kalla is the leader of Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Jusuf Kalla is the leader of Indonesia, the country which serves Arem-arem. While Arem-arem is nationwide in Indonesia, it is more specific to Java.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Jusuf Kalla is a leader in Indonesia, where the dish arem-arem is found nationwide, though it is more specific to Java.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Arem-arem"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"",
"property": "region",
"subject": "Arem-arem"
}
],
"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"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id4"
}
},
{
"449": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Italy is Sergio Mattarella, it is also where Italian is spoken and the arrabbiata sauce is from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sergio Mattarella is a leader of Italy where the Italian language is spoken and Arrabbiata sauce can be found.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is from Italy, where the spoken language is Italian and a leader is Sergio Mattarella.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italian_language",
"property": "language",
"subject": "Italy"
},
{
"object": "Sergio_Mattarella",
"property": "leader",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id5"
}
},
{
"450": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce, a traditional dish from Rome in Italy, has chili pepper as one of its important ingredients.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce from Rome in Italy contains chili pepper.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is from the region of Rome in Italy and one of the main ingredients is chili pepper.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id6"
}
},
{
"451": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Garlic is an ingredient in arrabbiata sauce which originates from the region of Rome, in Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce contains garlic and comes from the Rome region of italy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Garlic is an ingredient in arrabbiata sauce, which is a traditional dish from Rome, Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Garlic",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Garlic",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Garlic",
"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": "Id7"
}
},
{
"452": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre contains Squid and comes from the Catalonia region of Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Squid is an ingredient of arros negre which is from the region of Catalonia in Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is from the region of Catalonia, Spain, and one of its ingredients is Squid.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Catalonia",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Squid",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id8"
}
},
{
"453": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient in Arròs negre which comes from the region of the Valencian Community, in Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient of the dish Arròs negre, which is a dish that comes from the Valencian community in Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Cuttlefish is an ingredient in Arròs negre, which is a traditional dish from the Valencian community in Spain.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Cuttlefish",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id9"
}
},
{
"454": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of asam pedas , a food from the Sumatra and Malay Peninsula regions of Malaysia, consist of fish cooked in a sour and hot sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is made from fish cooked in a sour and hot sauce and comes from the Sumatra and Malay Peninsula regions in Malaysia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The dish Asam pedas comes from the region of Sumatra and the Malay Peninsula. Malaysia and the main ingredients are fish cooked in sour and hot sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Sumatra and Malay Peninsula\"",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "mainIngredients",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Sumatra and Malay Peninsula\"@en",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "ingredientName",
"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": "\"Sumatra and Malay Peninsula\"@en",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id10"
}
},
{
"455": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is made from squeezed or squashed chicken served with sambla and is a popular dish in Java, Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet's main ingredients are squeezed or smashed fried chicken served with sambal. It is a popular dish in Malaysia and also found in Java.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet originates from Malaysia and Java. The main ingredients are squeezed or smashed fried chicken served with sambal.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
},
{
"object": "\"Squeezed\" or \"smashed\" fried chicken served with sambal",
"property": "mainIngredients",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
},
{
"object": "\"\\\"Squeezed\\\" or \\\"smashed\\\"fried chickenserved withsambal\"",
"property": "ingredientName",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id11"
}
},
{
"456": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon and sausage are the main ingredients in a Bacon Explosion, which comes from the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion, whose name comes from the United States, has bacon and sausage.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "mainIngredients",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "mainIngredient",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id12"
}
},
{
"457": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "One of the ethnic groups in the United States are native Americans, the country is also the home of the dish bacon explosion and has Wasington D.C. as it's capitol city.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion originated in the United States, where the capital is Washington DC and Native Americans are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States where Washington D.C. is the capital and Native Americans are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"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": "country",
"subject": "Bacon_Explosion"
},
{
"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": "Id13"
}
},
{
"458": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The United States is the country of Bacon Explosion. Barack Obama is the leader and Washington D.C. is the capital.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion comes from the United States where Barack Obama is a leader and the capital city is Washington DC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion originates from the United States where the leader is Barack Obama and the capital is Washington DC.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"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": "Barack_Obama",
"property": "leader",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id14"
}
},
{
"459": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion originated in the United States, where African Americans are an ethnic group and John Roberts is a leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States where African Americans are an ethnic group and John Roberts is a leader.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "John_Roberts",
"property": "leader",
"subject": "United_States"
},
{
"object": "African_Americans",
"property": "ethnicGroup",
"subject": "United_States"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id15"
}
},
{
"460": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwiches are variations of BLT and come from the U.K. Condiments are found in these sandwiches.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The UK dish, the bacon sandwich uses condiments as an ingredient and has the variation BLT.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich, a variation of the BLT, comes from the UK and contains condiments.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "Condiment",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"subject": "Bacon_sandwich"
},
{
"object": "United_Kingdom",
"property": "country",
"subject": "Bacon_sandwich"
},
{
"object": "Condiment",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id16"
}
},
{
"461": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Meringue is an ingredient of a Baked Alaska, which is from the New York region and France.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Meringue is an ingredient of a Baked Alaska, which is reputed to come from both France and New York.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient of baked Alaska is meringue which originates in France and the New York regions.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id17"
}
},
{
"462": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is one of the ingredients in Baked Alaska, a dish from the New York region and found in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sponge cake is an ingredient of Baked Alaska, which comes from the region of New York and can be found in the U.S.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "An ingredient of baked Alaska is sponge cake, the dish originates from New York, USA.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "New_York",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"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": "Id18"
}
},
{
"463": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Christmas pudding is an ingredient in the dessert Baked Alaska. Cookie is also a dessert.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A cookie can be a dessert, as can baked alaska which has christmas pudding as an ingredient.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Cookies are a dessert item, as is Baked Alaska that has Christmas pudding as one of its ingredients.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id19"
}
},
{
"464": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sandesh (confectionery) is a dish that can be served as a dessert. Another dish that is a dessert is Baked Alaska which has Christmas pudding as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sandesh and Baked Alaska are desserts. Baked Alaska uses Christmas pudding as an ingredient.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Christmas pudding is an ingredient in Baked Alaska, which is a dessert, same as sandesh.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Sandesh_(confectionery)",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Dessert",
"property": "course",
"subject": "Baked_Alaska"
},
{
"object": "Sandesh_(confectionery)",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Christmas_pudding",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id20"
}
},
{
"465": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Part of Columbian cuisine, Bandeja paisa (which has kidney beans as one of the ingredients), comes from Antioquia Department area.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa, which contains kidney beans, is a traditional dish found in the Antioquia Department of Colombia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Kidney beans are an ingredient of Bandeja paisa which is a Colombian dish from the Antioquia department.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Kidney_bean",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Kidney_bean",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Antioquia_Department",
"property": "region",
"subject": "Bandeja_paisa"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id21"
}
},
{
"466": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes come from France where Gerard Larcher and Claude Bartolone are leaders.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes are found in France where Gerard Larcher and Claude Bartolone are leaders.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes are found in France where both Gerard Larcher and Claude Bartolone are leaders.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "Claude_Bartolone",
"property": "leader",
"subject": "France"
}
],
[
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leader",
"subject": "France"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id22"
}
},
{
"467": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny cakes can be made with apple and have 18 grams of carbs and 1.8 g of protein.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes can be made with apple and contain 1.8g protein and 18g of carbs.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes may be made with apple and have 1.8g of protein and 18g of carbohydrates.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Apple",
"property": "dishVariation",
"subject": "Barny_Cakes"
},
{
"object": "18.0 g",
"property": "carbohydrate",
"subject": "Barny_Cakes"
},
{
"object": "1.8 g",
"property": "protein",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Apple",
"property": "hasVariant",
"subject": "Barny_Cakes"
},
{
"object": "\"18.0\"^^<http://dbpedia.org/datatype/gram>",
"property": "carbohydrate",
"subject": "Barny_Cakes"
},
{
"object": "\"1.8\"^^<http://dbpedia.org/datatype/gram>",
"property": "protein",
"subject": "Barny_Cakes"
}
],
[
{
"object": "Apple",
"property": "hasVariant",
"subject": "Barny_Cakes"
},
{
"object": "\"1.8\"^^xsd:double",
"property": "protein",
"subject": "Barny_Cakes"
},
{
"object": "\"18.0\"^^<http://dbpedia.org/datatype/gram>",
"property": "carbohydrate",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id23"
}
},
{
"468": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish from the country of Singapore, where the leader is Halimah Yacob and the currency is the Singapore dollar.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore, where the Singapore dollar is the currency and the leader is Halimah Yacob.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Singapore_dollar",
"property": "currency",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Singapore_dollar",
"property": "currency",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leader",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id24"
}
},
{
"469": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A dish commonly found in Singapore and Indonesia is beef kway teow which has oyster sauce as a main ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow contains oyster sauce and is found in Indonesia and Singapore.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id25"
}
},
{
"470": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A popular food in Indonesia, Beef kway teow originates from Singapore. The country where Tony Tan is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Tony Tan is a leader in Singapore where the dish Beef kway teow originates from. It is also found in Indonesia.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id26"
}
},
{
"471": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India, it's main ingredients are gram flour and vegetables.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The key ingredients of a Bhajji from India are gram flour and vegetables.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in Bhajji, which originates from Indiam are gram flour and vegetables.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"",
"property": "mainIngredients",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
},
{
"object": "\"Gram flour,vegetables\"",
"property": "ingredientName",
"subject": "Bhajji"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"@en",
"property": "mainIngredient",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id27"
}
},
{
"472": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India, country of Indians where the leader is T. S. Thakur.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India, where the leader is called T S Thakur and the people who live there are called Indians.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originate from India where T S Thakur leads the Indian people.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id28"
}
},
{
"473": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country of India, where two of the leaders are, T. S. Thakur and Narendra Modi.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from India, where two of the leaders are Narendra Modi and T.S. Thakur.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The dish bhajji originates in India where T.S. Thakur and Narendra Modi are leaders.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leader",
"subject": "India"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leaderName",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id29"
}
},
{
"474": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from the Karnataka region and the main ingredients are vegetables and gram flour.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in Bhajji are gram flour and vegetables, this comes from the Karnataka region.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji are found in the region of Karnataka, its main ingredients are gram flour and vegetables.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"",
"property": "mainIngredients",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
},
{
"object": "\"Gram flour, vegetables\"@en",
"property": "mainIngredient",
"subject": "Bhajji"
},
{
"object": "Gram_flour",
"property": "ingredient",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id30"
}
},
{
"475": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The binignit dish can be found in the Philippines. Two of the main ingredients in it are banana and sweet potato.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Binignit are banana and sweet potatoes, and it can be found in the Philippines.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Binignit is a dish from the Philippines made from banana and sweet potato.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "mainIngredients",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Banana",
"property": "mainIngredient",
"subject": "Binignit"
},
{
"object": "Philippines",
"property": "country",
"subject": "Binignit"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id31"
}
},
{
"476": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert containing sour cream from Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Sour cream is an ingredient of Bionico, that is a dessert course found in Mexico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Sour cream is used to serve Bionico, a dessert food found in Mexico.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id32"
}
},
{
"477": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Containing raisins, Bionico is found in the region of Jalisco, in Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Raisins are found in bionico which comes from the region of Jalisco in Mexico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Raisins are an ingredient of Bionico which is from the region Jalisco, Mexico.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Raisin",
"property": "ingredient",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Raisin",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id33"
}
},
{
"478": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "One ingredient in Bakso is celery, which is a member of the genus Apium in the family Apiaceae.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Celery is from the genus apium and the family Apiaceae. It is an ingredient of Bakso.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Celery is a member of the family Apiaceae, it is from the genus apium and is an ingredient of Bakso.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Apium",
"property": "genus",
"subject": "Celery"
},
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Apiaceae",
"property": "family",
"subject": "Celery"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Apium",
"property": "genus",
"subject": "Celery"
},
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
},
{
"object": "Apiaceae",
"property": "family",
"subject": "Celery"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id34"
}
},
{
"479": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert that requires granola as one of its ingredients; it can be eaten as an alternative to cake.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico requires granola as one of its ingredients, it is served at the dessert course, like cake.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cake",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cake",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Granola",
"property": "ingredient",
"subject": "Bionico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id35"
}
},
{
"480": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso is a dish from the country of Indonesia, where the capital is Jakarta and one of the leaders is Jusuf Kalla.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The dish bakso comes from Indonesia where Jusuf Kalla is the leader and Jakarta is the capitol city.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a dish from the country of Indonesia, where the capital is Jakarta and the leader is Jusuf Kalla.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leader",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id36"
}
},
{
"481": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "From the Singapore region, Ayam penyet, is a food found in Java where an ethic group is the Baduy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from the region of Singapore and is also found in Java where the Baduy are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is from Singapore and also Java, where the Baduy are an ethnic group.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id37"
}
},
{
"482": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet originates from Java where the Banyumasan are an ethnic group. It can be found nationwide and also in Singapore and Malaysia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The dish Ayam penyet originates from Java where the Banyumasan people are an ethnic group. The dish can also be found in Malaysia and Singapore.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banyumasan_people",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "\"Nationwide, also can be found in Malaysia and Singapore\"",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Banyumasan_people",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "\"Nationwide, also can be found in Malaysia and Singapore\"@en",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id38"
}
},
{
"483": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Spain is Felipe VI and it is also where Spanish is spoken and the dish arros negre is from.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros Negre is a traditional dish from Spain where they speak Spanish and the leader is Felipe VI.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from Spain where Felipe VI is the leader and the Spanish language is spoken.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spanish_language",
"property": "language",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leaderName",
"subject": "Spain"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Spanish_language",
"property": "language",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leader",
"subject": "Spain"
}
],
[
{
"object": "Spanish_language",
"property": "language",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leaderName",
"subject": "Spain"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id39"
}
},
{
"484": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adams County, Pennsylvania is the location of the 11th Mississippi Infantry monument. Franklin County Pennsylvania is to the west and Frederick County to the south west.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is located in Adams County Pennsylvania, between Franklin County to the west and Frederick County to the southwest.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Franklin_County,_Pennsylvania",
"property": "has to its west",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Frederick_County,_Maryland",
"property": "has to its southwest",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "location",
"subject": "11th_Mississippi_Infantry_Monument"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Franklin_County,_Pennsylvania",
"property": "west",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Frederick_County,_Maryland",
"property": "southwest",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "region",
"subject": "11th_Mississippi_Infantry_Monument"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1"
}
},
{
"485": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyr's memorial which is dedicated to the soldiers of the Ottoman army who were killed in the Battle of Baku is located in Azerbaijan. At present Artur Rasizade is the leader of the country.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Artur_Rasizade",
"property": "leader",
"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"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Artur_Rasizade",
"property": "leader",
"subject": "Azerbaijan"
},
{
"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": "3",
"xml_id": "Id2"
}
},
{
"486": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial in Azerbaijan was designed by Hüseyin Bütüner and Hilmi Güner and is dedicated to Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial in Azerbaijan was designed by Hüseyin Bütüner and Hilmi Güner and is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyr's Memorial in Azerbaijan, designed by Hüseyin Bütüner and Hilmi Güner, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id3"
}
],
"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"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"",
"property": "designer",
"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"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"@en",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id3"
}
},
{
"487": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Azerbaijan, where the leader is Artur Rasizade, is the location of the Baku Turkish Martyrs Memorial. The memorial was designed by Huseyin Butuner and Hilmi Guner.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial, designed by Huseyin Butuner and Hilmi Guner, is located in Azerbaijan, where the leader is Artur Rasizade.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"@en",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id4"
}
},
{
"488": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial was designed by Huseyin Butuner and Hilmi Guner, and is located in Azerbaijan, where it is called Turk Sehitleri Aniti.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Türk Şehitleri Anıtı\"@en",
"property": "nativeName",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"@en",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id5"
}
},
{
"489": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A.C. Cesena, in the Serie B League, has 23900 members and is located is Cesena.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AC Cesena's ground is in Cesena and they have 23900 members. The club plays in Serie B.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.C. Cesena, with 23900 members, is in the Serie B league and has a ground in Cesena.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Cesena",
"property": "ground",
"subject": "A.C._Cesena"
},
{
"object": "Serie_B",
"property": "league",
"subject": "A.C._Cesena"
},
{
"object": "23900",
"property": "numberOfMembers",
"subject": "A.C._Cesena"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Cesena",
"property": "ground",
"subject": "A.C._Cesena"
},
{
"object": "Serie_B",
"property": "league",
"subject": "A.C._Cesena"
},
{
"object": "23900",
"property": "capacity",
"subject": "A.C._Cesena"
}
],
[
{
"object": "Cesena",
"property": "ground",
"subject": "A.C._Cesena"
},
{
"object": "Serie_B",
"property": "league",
"subject": "A.C._Cesena"
},
{
"object": "\"23900\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.C._Cesena"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id1"
}
},
{
"490": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago played for the club SSD Potenza Calcio and his own club was Calcio Catania. He is currently managing AC Cesena.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago manages A.C. Cesena and played for the club SSD Potenza Calcio. He is also in club Calcio Catania.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Massimo Drago played for S.S.D. Potenza Calcio and now plays for Calcio Catania while managing A.C. Cesena.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Massimo_Drago",
"property": "manager",
"subject": "A.C._Cesena"
},
{
"object": "S.S.D._Potenza_Calcio",
"property": "club",
"subject": "Massimo_Drago"
},
{
"object": "Calcio_Catania",
"property": "club",
"subject": "Massimo_Drago"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Massimo_Drago",
"property": "manager",
"subject": "A.C._Cesena"
},
{
"object": "S.S.D._Potenza_Calcio",
"property": "clubs",
"subject": "Massimo_Drago"
},
{
"object": "Calcio_Catania",
"property": "team",
"subject": "Massimo_Drago"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id2"
}
},
{
"491": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AC Lumezzane has 4150 members and is in the Lega Pro/A league where they played in the 2014 season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "I am interested in the 2014 A.C. Lumezzane season. It plays in Lega Pro/A. and has 4150 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.C Lumezzane, with 4150 members, play in the Lega Pro/A, participating in the 2014 season.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Lega Pro/A\"",
"property": "league",
"subject": "A.C._Lumezzane"
},
{
"object": "2014",
"property": "season",
"subject": "A.C._Lumezzane"
},
{
"object": "4150",
"property": "numberOfMembers",
"subject": "A.C._Lumezzane"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Lega Pro/A\"@en",
"property": "league",
"subject": "A.C._Lumezzane"
},
{
"object": "2014",
"property": "season",
"subject": "A.C._Lumezzane"
},
{
"object": "4150",
"property": "capacity",
"subject": "A.C._Lumezzane"
}
],
[
{
"object": "\"Lega Pro/A\"@en",
"property": "league",
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},
{
"object": "2014",
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},
{
"object": "\"4150\"^^xsd:nonNegativeInteger",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id3"
}
},
{
"492": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "toFix",
"lang": "",
"lex": "A.D. Isidro Metapán are at Estadio Jorge Calero Suárez whose full name is Isidro Metapan and has 10000 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.D. Isidro Metapan, (full name, Isidro Metapan), has 10000 members and play their home games at the Estadio Jorge Caler0 Suarez stadium.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Isidro Metapan (A.D. Isidro Metapan) has 10000 members and are at Estadio Jorge Calero Suarez.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Estadio_Jorge_Calero_Suárez",
"property": "ground",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "\"Isidro Metapán\"",
"property": "fullname",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "10000",
"property": "numberOfMembers",
"subject": "A.D._Isidro_Metapán"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Estadio_Jorge_Calero_Suárez",
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"subject": "A.D._Isidro_Metapán"
},
{
"object": "\"Isidro Metapán\"@en",
"property": "fullname",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "10000",
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"subject": "A.D._Isidro_Metapán"
}
],
[
{
"object": "Estadio_Jorge_Calero_Suárez",
"property": "ground",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "\"Isidro Metapán\"@en",
"property": "fullname",
"subject": "A.D._Isidro_Metapán"
},
{
"object": "\"10000\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.D._Isidro_Metapán"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id4"
}
},
{
"493": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The A.E Dimitra Efxeinoupolis club is located in Greece, where there are leaders called Nikos Voutsis and Prokopis Pavlopoulos.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "There are two prominent leaders in Greece: Nikos Voutsis and Prokopis Pavlopoulos as well the A.E. Dimitra Efxeinoupolis is located there.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.E Dimitra Efxeinoupolis is located in Greece, the leader of the country is Nikos Voutsis and/or Prokopis Pavloopoulos.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greece",
"property": "location",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
},
{
"object": "Prokopis_Pavlopoulos",
"property": "leader",
"subject": "Greece"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Greece",
"property": "location",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Nikos_Voutsis",
"property": "leaderName",
"subject": "Greece"
},
{
"object": "Prokopis_Pavlopoulos",
"property": "leader",
"subject": "Greece"
}
],
[
{
"object": "Greece",
"property": "location",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
},
{
"object": "Prokopis_Pavlopoulos",
"property": "leaderName",
"subject": "Greece"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id5"
}
},
{
"494": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker who is with Drogheda United F.C and plays for Runcorn F.C. Halton.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Footballer Stuart Parker, the manager of AFC Blackpool, was at Drogheda United FC and plays for Runcorn F.C. Halton.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker is a footballer with the Drogheda United F.C., also plays for Runcorn F.C. Halton and has managed AFC Blackpool.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Drogheda_United_F.C.",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
},
{
"object": "Runcorn_F.C._Halton",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Drogheda_United_F.C.",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
},
{
"object": "Runcorn_F.C._Halton",
"property": "clubs",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id6"
}
},
{
"495": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The manager of A.F.C. Blackpool is Stuart Parker who was at Drogheda United FC and once played for Stockport County FC.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had Stuart Parker as their manager. He plays for Drogheda United F.C. and Stockport County F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker (football player of Drogheda United F.C.) managed AFC Blackpool. He was a member of Stockport County F.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Drogheda_United_F.C.",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
},
{
"object": "Stockport_County_F.C.",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "Drogheda_United_F.C.",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
},
{
"object": "Stockport_County_F.C.",
"property": "clubs",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id7"
}
},
{
"496": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Stuart Parker is a member of the Iriam Town F.C. as well as the manager of A.F.C. Blackpool and has also represented the KV Mechelen club.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had Stuart Parker as their manager. He has represented the club KV Mechelen and is a member of the Irlam Town F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Blackpool have had Stuart Parker as their manager. He is attached to Irlam Town Football Club and is part of the KV Mechelen club.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Stuart_Parker_(footballer)",
"property": "manager",
"subject": "A.F.C._Blackpool"
},
{
"object": "KV_Mechelen",
"property": "club",
"subject": "Stuart_Parker_(footballer)"
},
{
"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": "KV_Mechelen",
"property": "team",
"subject": "Stuart_Parker_(footballer)"
},
{
"object": "Irlam_Town_F.C.",
"property": "clubs",
"subject": "Stuart_Parker_(footballer)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id8"
}
},
{
"497": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AFC Fylde has the full name \"Association Football Club Fylde,\" they have 3180 members and their ground is located in Warton, Fylde.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Association Football Club Fylde, abbreviated to A.F.C Fylde, has 3180 members and its ground is Warton Fylde.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Warton,_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
},
{
"object": "3180",
"property": "numberOfMembers",
"subject": "A.F.C._Fylde"
},
{
"object": "\"Association Football Club Fylde\"",
"property": "fullname",
"subject": "A.F.C._Fylde"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Warton,_Fylde",
"property": "ground",
"subject": "A.F.C._Fylde"
},
{
"object": "\"3180\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.F.C._Fylde"
},
{
"object": "\"Association Football Club Fylde\"@en",
"property": "fullname",
"subject": "A.F.C._Fylde"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id9"
}
},
{
"498": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Christian Panucci, who has been attached to the Genoa CFC and Real Madrid CF clubs, manages A.S. Livorno Calcio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Livorno Calcio is managed by Christian Panucci. is was attached to the Real Madrid CF club. He is currently attached to the club Genoa CFC.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Christian Panucci was attached to the Real Madrid CF club, he managed A.S. Livorno Calcio and played football for Genoa C.F.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Christian_Panucci",
"property": "manager",
"subject": "A.S._Livorno_Calcio"
},
{
"object": "Genoa_C.F.C.",
"property": "club",
"subject": "Christian_Panucci"
},
{
"object": "Real_Madrid_C.F.",
"property": "club",
"subject": "Christian_Panucci"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Christian_Panucci",
"property": "manager",
"subject": "A.S._Livorno_Calcio"
},
{
"object": "Genoa_C.F.C.",
"property": "team",
"subject": "Christian_Panucci"
},
{
"object": "Real_Madrid_C.F.",
"property": "clubs",
"subject": "Christian_Panucci"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id10"
}
},
{
"499": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The \"Associazione Sportiva Roma S.p.A.\" is the non-abbreviated name of A.S. Roma which has 70634 members and a home ground in Rome, Italy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.S.Roma have 70634 members, play their home games in Rome and their full title is Associazione Roma S.p.A.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A.S. Roma, or Associazione Sportiva Roma S.p.A., has 70634 members and its ground is in Rome, Italy.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Associazione Sportiva Roma S.p.A.\"",
"property": "fullname",
"subject": "A.S._Roma"
},
{
"object": "\"Rome, Italy\"",
"property": "ground",
"subject": "A.S._Roma"
},
{
"object": "70634",
"property": "numberOfMembers",
"subject": "A.S._Roma"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "70634",
"property": "capacity",
"subject": "A.S._Roma"
},
{
"object": "\"Associazione Sportiva Roma S.p.A.\"@en",
"property": "fullname",
"subject": "A.S._Roma"
},
{
"object": "\"Rome, Italy\"@en",
"property": "ground",
"subject": "A.S._Roma"
}
],
[
{
"object": "\"Associazione Sportiva Roma S.p.A.\"@en",
"property": "fullname",
"subject": "A.S._Roma"
},
{
"object": "\"Rome, Italy\"@en",
"property": "ground",
"subject": "A.S._Roma"
},
{
"object": "\"70634\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "A.S._Roma"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id11"
}
},
{
"500": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. ground is in Athens, they have 69618 members and they compete in the Superleague Greece.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C., with 69618 members, play in the Superleague Greece. Their ground is in Athens.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AEK Athens F.C. has 69618 members, their ground is in Athens and they play in the Superleague of Greece.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Superleague_Greece",
"property": "league",
"subject": "AEK_Athens_F.C."
},
{
"object": "Athens",
"property": "ground",
"subject": "AEK_Athens_F.C."
},
{
"object": "69618",
"property": "numberOfMembers",
"subject": "AEK_Athens_F.C."
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Superleague_Greece",
"property": "league",
"subject": "AEK_Athens_F.C."
},
{
"object": "Athens",
"property": "ground",
"subject": "AEK_Athens_F.C."
},
{
"object": "\"69618\"^^xsd:nonNegativeInteger",
"property": "capacity",
"subject": "AEK_Athens_F.C."
}
],
[
{
"object": "69618",
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"subject": "AEK_Athens_F.C."
},
{
"object": "Superleague_Greece",
"property": "league",
"subject": "AEK_Athens_F.C."
},
{
"object": "Athens",
"property": "ground",
"subject": "AEK_Athens_F.C."
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id12"
}
},
{
"501": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "toFix",
"lang": "",
"lex": "AFC Ajax (amateurs) has 5000 members play in the Hoofdklasse league and competed in the 2014 season.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax (Amateurs) competed in the 2014 season, they play in the Hoofdklasse league and the main team have 5000 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "AFC Ajax, who played in the 2014 season, have 5000 members and play in the Hoofdklasse league.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "5000",
"property": "numberOfMembers",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "Hoofdklasse",
"property": "league",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "2014",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"5000\"^^xsd:nonNegativeInteger",
"property": "capacity",
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},
{
"object": "Hoofdklasse",
"property": "league",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "2014",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
}
],
[
{
"object": "Hoofdklasse",
"property": "league",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "5000",
"property": "capacity",
"subject": "AFC_Ajax_(amateurs)"
},
{
"object": "2014",
"property": "season",
"subject": "AFC_Ajax_(amateurs)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "3",
"xml_id": "Id13"
}
},
{
"502": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "AZAL PFK play their home matches at the AZAL Arena, and are in the Azerbaijan Premier League, who's championships name is the Qarabag FK.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AZAL Arena is the ground of AZAL PFK and competes in The Azerbaijan Premier League, which champions are Qarabag FK.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Azerbaijan_Premier_League",
"property": "league",
"subject": "AZAL_PFK"
},
{
"object": "Qarabağ_FK",
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"subject": "Azerbaijan_Premier_League"
},
{
"object": "AZAL_Arena",
"property": "ground",
"subject": "AZAL_PFK"
}
],
"originaltriplesets": {
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[
{
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"property": "league",
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},
{
"object": "Qarabağ_FK",
"property": "champions",
"subject": "Azerbaijan_Premier_League"
},
{
"object": "AZAL_Arena",
"property": "ground",
"subject": "AZAL_PFK"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id14"
}
},
{
"503": {
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Alkmaar Zaanstreek is the full name of AZ Alkmaar who played in the 2014 season and has 17023 members.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "\"Alkmaar Zaanstreek\" is the full name of AZ Alkmaar who played in the 2014 season and has 17023 members.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alkmaar Zaanstreek (AZ Alkmaar) has 170723 members and played in the 2014 season.",
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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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},
{
"comment": "good",
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"lex": "John van den Brom, the manager of AZ Alkmaar, is in Vitesse Arnhem and plays for De Graafschap.",
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},
{
"comment": "good",
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"lex": "John van den Brom has been manager of AZ Alkmaar, he is in Vitesse Arnhem and plays for De Graafschap.",
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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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"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Agremiação Sportiva Arapiraquense plays at Estádio Municipal Coaracy da Mata Fonseca. The club play in the Campeonato Brasileiro Série C league and the champions of the league were the Vila Nova Futebol Clube.",
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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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"506": {
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"comment": "good",
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},
{
"comment": "good",
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"lex": "Agremiação Sportiva Arapiraquense play in the Campeonato Brasileiro Série C league in brazil. The Vila Nova Futebol Clube were champions at the Campeonato Brasileiro Série C.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Agremiação Sportiva Arapiraquense play in the Campeonato Brasileiro Série C league, that is based in Brazil. The champions are Vila Nova Futebol Clube.",
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}
],
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{
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{
"object": "Brazil",
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{
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],
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{
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{
"object": "Brazil",
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{
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},
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"xml_id": "Id18"
}
},
{
"507": {
"category": "SportsTeam",
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{
"comment": "good",
"lang": "",
"lex": "St Vincent-St Mary High School is the ground of Akron Summit Assault who play in the Premier Development League, of which K-W United FC have been champions.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault who play in the Premier Development League, won by K-W United FC, have their home ground at St. Vincent-St. Mary High School.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "St Vincent-St Mary High School is the ground of Akron Summit Assault who play in the Premier Development League which K-W United FC have been champions of.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "St._Vincent–St._Mary_High_School",
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{
"object": "Premier_Development_League",
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{
"object": "K-W_United_FC",
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],
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{
"object": "St._Vincent–St._Mary_High_School",
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{
"object": "K-W_United_FC",
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},
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"xml_id": "Id19"
}
},
{
"508": {
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"comment": "good",
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},
{
"comment": "good",
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"lex": "3000 member club Akron Summit Assault play in the Premier Development League alongside former champions K-W United FC.",
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},
{
"comment": "good",
"lang": "",
"lex": "Akron Summit Assault has 3000 members and plays in the Premier Development League of which K-W United FC were champions.",
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}
],
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{
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{
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{
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{
"object": "K-W_United_FC",
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},
"shape": "NA",
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"xml_id": "Id20"
}
},
{
"509": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ground of A.S. Gubbio 1910 is located in Italy where Sergio Mattarella is a leader of the Italian people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of A.S. Gubbio 1910 is located in Italy which is lead by Sergio Mattarella and inhabited by Italians.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The ground of A.S. Gubbio 1910 is located in Italy, country of Italians where the leader is Sergio Mattarella.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Sergio_Mattarella",
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"subject": "Italy"
},
{
"object": "Italy",
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"subject": "A.S._Gubbio_1910"
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],
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"originaltripleset": [
[
{
"object": "Italians",
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{
"object": "Sergio_Mattarella",
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},
{
"object": "Italy",
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]
]
},
"shape": "NA",
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"xml_id": "Id21"
}
},
{
"510": {
"category": "SportsTeam",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The ground for AEK Athens FC is the Olympic Stadium (Athens) in Athens whose mayor is Giorgos Kaminis.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Home ground of AEK Athens FC, the Olympic Stadium is located in Athens, who's mayor is Giorgos Kaminis.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Olympic Stadium in Athens is the home ground of AEK Athens FC. The mayor of Athens is Giorgos Kaminis.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
{
"object": "Athens",
"property": "location",
"subject": "Olympic_Stadium_(Athens)"
},
{
"object": "Giorgos_Kaminis",
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"subject": "Athens"
},
{
"object": "Olympic_Stadium_(Athens)",
"property": "ground",
"subject": "AEK_Athens_F.C."
}
],
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{
"object": "Athens",
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"subject": "Olympic_Stadium_(Athens)"
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},
{
"object": "Olympic_Stadium_(Athens)",
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]
]
},
"shape": "NA",
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"xml_id": "Id22"
}
},
{
"511": {
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{
"comment": "good",
"lang": "",
"lex": "The Accademia di Architettura di Mendrisio is in Switzerland. Its dean is Mario Botta and it has 600 students.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Accademia di Architettura di Mendrisio of Switzerland has 600 students and the dean is called Mario Botta.",
"xml_id": "Id2"
}
],
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"modifiedtripleset": [
{
"object": "Switzerland",
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{
"object": "600",
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],
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{
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{
"object": "\"600\"^^xsd:nonNegativeInteger",
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]
},
"shape": "NA",
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}
},
{
"512": {
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{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology in Bangalore, India has the motto \"Nurturing Excellence\".",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Acharya Institute of Technology is in Bangalore, India and its motto is\"Nurturing Excellence\".",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bangalore",
"property": "city",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"Nurturing Excellence\"",
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},
{
"object": "\"India\"",
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],
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[
{
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},
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},
{
"513": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology is in Bangalore, India. Its president is B.M. Reddy.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology (Bangalore, India) has B. M. Reddy as its president.",
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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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},
{
"514": {
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{
"comment": "good",
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"lex": "The Acharya Insitute of Technology offers tennis and it was established in 2000. The tennis governing body is the International Tennis Federation.",
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},
{
"comment": "good",
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"lex": "The International Tennis Federation is the governing body for the sport which is offered at the Acharya Institute of Technology which was established in 2000.",
"xml_id": "Id2"
}
],
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{
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],
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{
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{
"object": "International_Tennis_Federation",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id4"
}
},
{
"515": {
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{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College is in India. Mumbai is the largest city in India. The river Ganges runs through India.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "AWH Engineering College is located in the country of India, whose capital city is Mumbai. One of the rivers in India is called the Ganges.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mumbai",
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},
{
"object": "India",
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"subject": "AWH_Engineering_College"
},
{
"object": "Ganges",
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],
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"originaltripleset": [
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{
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{
"object": "Ganges",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id5"
}
},
{
"516": {
"category": "University",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of Romania is Prime Minister Klaus Lohannis. The 1 Decembrie 1918 University is in Romania.",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id3"
}
},
{
"553": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Agra airport is in Uttar Pradesh, which is part of Awadh and Bundelkhand, is located 167.94 metres above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Agra Airport is 167.94 metres above sea level and located in Uttar Pradesh, part of Awadh and Bundelkhand.",
"xml_id": "Id2"
}
],
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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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},
{
"554": {
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ram Naik is the leader in Uttar Pradesh where Agra airport is located and is part of Awadh and Bundelkhand.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Agra airport is located in Uttar Pradesh which is part of Awadh and also Bundelkhand, and lead by Ram Naik.",
"xml_id": "Id3"
}
],
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},
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"xml_id": "Id1"
},
{
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"lang": "",
"lex": "Al Asad airbase is operated by the United States air force who were involved in the 1986 bombing of Libya, and deploy the aircraft fighter General Dynamics F-16 Fighting Falcon and have the Lockheed AC130 on their aircraft carriers.",
"xml_id": "Id2"
}
],
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{
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id6"
}
},
{
"556": {
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},
{
"comment": "good",
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"lex": "The United States Air Force is the operating organisation for Al Asad airbase and has the Lockhead AC-130 and General Dynamics F-16 Fighting Falcon as aircraft. One of their battles was Operation Enduring Freedom..",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad Airbase is operated by the United States Air Force which uses Lockheed AC-130 aircraft carriers. The General Dynamics F-16 Fighting Falcon is an aircraft used by the USAF and they were involved in the Operation Enduring Freedom.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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},
{
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},
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]
},
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"xml_id": "Id7"
}
},
{
"557": {
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"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "Operation Enduring Freedom was a battle involving the United States Air Force who operate Al Asad Airbase. They deploy the Lockheed AC-130 on their aircraft carriers and the McDonnell Douglas F-15 Eagle as a fighter aircraft.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Al Asad airbase is operated by the United States Air Force which was involved in Operation Enduring Freedom. The Lockheed AC-130 and McDonnell Douglas F-15 Eagle are two of their aircrafts.",
"xml_id": "Id3"
}
],
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},
{
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},
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"xml_id": "Id8"
}
},
{
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},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force, which is the operating organisation for Al Asad airbase was involved in battles at the Invasion of Grenada. They deploy the Lockheed AC-130 attack aircraft and the General Dynamics F-16 Fighting Falcon.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The United States Air Force, who fought in the Invasion of Grenada operates the Al Asad Airbase. The USAF fly Lockheed AC-130 aircraft from aircraft carriers, in addition to the General Dynamics F-16 Fighting Falcon.",
"xml_id": "Id3"
}
],
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},
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},
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"xml_id": "Id9"
}
},
{
"559": {
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"comment": "good",
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"lex": "The Pakistan Civil Aviation Authority has headquarters at Jinnah International airport and governs the Allama Iqbal International Airport in Punjab, Pakistan which is lead by the Provincial Assembly of the Punjab.",
"xml_id": "Id1"
},
{
"comment": "good",
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"lex": "The Allama Iqbal International Airport is located in Punjab, Pakistan which is led by the Provincial Assembly of the Punjab. The airport is governed by the Pakistan Civil Aviation Authority headquartered in Jinnah International Airport.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Allama Iqbal International Airport is located in Punjab,Pakistan which is led by the Provincial Assembly of Punjab.The airport is operated by the Pakistan Civil Aviation Authority whose headquarters is in the Jinnah International Airport.",
"xml_id": "Id3"
}
],
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},
{
"object": "Punjab,_Pakistan",
"property": "location",
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},
{
"object": "Provincial_Assembly_of_the_Punjab",
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]
]
},
"shape": "NA",
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"xml_id": "Id10"
}
},
{
"560": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport serves Alpena, Michigan and is located at Maple Ridge Township, Alpena County, Michigan. It has a runway length of 1533.0 and is 210 metres above sea level.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena County Regional Airport is located in Maple Ridge Township, Alpena County and serves the city of Alpena, Michigan. It is located 210 metres above sea level and has a runway length of 1533.0.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Alpena, Michigan, is served by the Alpena County Regional Airport in Maple Ridge Township. The runway length is 1533.0 and the elevation is 210 metres above sea level.",
"xml_id": "Id3"
}
],
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"modifiedtripleset": [
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"property": "location",
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},
{
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"property": "runwayLength",
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},
{
"object": "Alpena,_Michigan",
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"subject": "Alpena_County_Regional_Airport"
},
{
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],
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],
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],
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},
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}
},
{
"561": {
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{
"comment": "good",
"lang": "",
"lex": "The city of Amsterdam is served by Amsterdam Airport Schiphol which is -3.3528 metres above sea level. It has a runway length of 2014.0 meters and 06/24, Kaagbaan, is the runway name.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Serving the city of Amsterdam, Amsterdam airport Schipol is -3.3528 above sea level. Amsterdam Airport Schiphol's runway length is 2014.0 and it has the runway name, 06/24 Kaagbaan.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "cityServed",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "\"06/24 'Kaagbaan'\"",
"property": "runwayName",
"subject": "Amsterdam_Airport_Schiphol"
},
{
"object": "2014.0",
"property": "runwayLength",
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},
{
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"property": "elevationAboveTheSeaLevel_(in_metres)",
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],
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],
[
{
"object": "Amsterdam",
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]
]
},
"shape": "NA",
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"xml_id": "Id12"
}
},
{
"562": {
"category": "Airport",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "The demonym for the people of Texas is Tejano and the spoken language is English. The state is the location of Andrews County airport and has the capital city of Austin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County airport is in Texas where the capital city is Austin, the local people are known as Tejano and the spoken language is English.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas whose capital city is Austin.Tejano are the inhabitants of the state and English language is spoken there.",
"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": "Tejano",
"property": "demonym",
"subject": "Texas"
}
],
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{
"object": "Texas",
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{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
},
{
"object": "English_language",
"property": "language",
"subject": "Texas"
},
{
"object": "Tejano",
"property": "demonym",
"subject": "Texas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id13"
}
},
{
"563": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport, Texas is in the United States. The inhabitants of Texas, Texans , speak English.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas within the United States, the people of Texas are referred to as Texans and the local language is English.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Texas is the location of Andrews County airport and is in the United States. The inhabitants are known as Texans and they speak the English language.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "English_language",
"property": "language",
"subject": "Texas"
},
{
"object": "Texan",
"property": "demonym",
"subject": "Texas"
},
{
"object": "United_States",
"property": "country",
"subject": "Texas"
}
],
"originaltriplesets": {
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{
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"property": "location",
"subject": "Andrews_County_Airport"
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{
"object": "English_language",
"property": "language",
"subject": "Texas"
},
{
"object": "Texan",
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"subject": "Texas"
},
{
"object": "United_States",
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"subject": "Texas"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id14"
}
},
{
"564": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County airport is in Texas, United States, where the people are known as Texans and Spanish is spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Texans are the people from Texas in the United States where Spanish is spoken and Andrews County airport is located.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texts,United States. The people of Texas are called Texans and Spanish language is spoken there.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Texas"
},
{
"object": "Texan",
"property": "demonym",
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},
{
"object": "United_States",
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],
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"originaltripleset": [
[
{
"object": "Texas",
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{
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"property": "language",
"subject": "Texas"
},
{
"object": "Texan",
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{
"object": "United_States",
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]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id15"
}
},
{
"565": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Andrews County Airport is located in Texas in the United States. In Texas, the capital city is Austin and the largest city is Houston.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County airport in the US is located in Texas where the capital is Austin and the largest city is Houston.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Andrews County airport is located in Texas, United States, where Houston is the largest city and Austin is the capital.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Texas",
"property": "location",
"subject": "Andrews_County_Airport"
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{
"object": "Houston",
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"subject": "Texas"
},
{
"object": "Austin,_Texas",
"property": "capital",
"subject": "Texas"
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{
"object": "United_States",
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],
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{
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{
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{
"object": "Austin,_Texas",
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{
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]
]
},
"shape": "NA",
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"xml_id": "Id16"
}
},
{
"566": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "At 159 meters above sea level, Angola International Airport is located at Ícolo e Bengo, part of Luanda Province, in Angola.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International airport, situated 159 metres above sea level, is located at Ícolo e Bengo, part of Luanda Province in Angola.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Angola International Airport is located in Ícolo e Bengo in Luanda Province, Angola. It is 159 metres above sea level..",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ícolo_e_Bengo",
"property": "location",
"subject": "Angola_International_Airport"
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{
"object": "Angola",
"property": "country",
"subject": "Ícolo_e_Bengo"
},
{
"object": "Luanda_Province",
"property": "isPartOf",
"subject": "Ícolo_e_Bengo"
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{
"object": "159",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Angola_International_Airport"
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],
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{
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{
"object": "Angola",
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{
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{
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[
{
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{
"object": "Angola",
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{
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{
"object": "\"159.106\"^^xsd:double",
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"shape": "NA",
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"xml_id": "Id17"
}
},
{
"567": {
"category": "Airport",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Flemish Government runs the flemish region in Belgium where Antwerp is served by Antwerp International airport.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves the city of Antwerp.The Flemish region is in the country of Belgium and is under the jurisdiction of the Flemish government.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Flemish region, which is led by the Flemish government, is part of Belgium. Antwerp International airport serves Antwerp.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Antwerp",
"property": "cityServed",
"subject": "Antwerp_International_Airport"
},
{
"object": "Flemish_Government",
"property": "leaderName",
"subject": "Flemish_Region"
},
{
"object": "Belgium",
"property": "country",
"subject": "Flemish_Region"
},
{
"object": "Flemish_Region",
"property": "jurisdiction",
"subject": "Flemish_Government"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Antwerp",
"property": "city",
"subject": "Antwerp_International_Airport"
},
{
"object": "Flemish_Government",
"property": "leaderName",
"subject": "Flemish_Region"
},
{
"object": "Belgium",
"property": "country",
"subject": "Flemish_Region"
},
{
"object": "Flemish_Region",
"property": "jurisdiction",
"subject": "Flemish_Government"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id18"
}
},
{
"568": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport (in Greenville, Wisconsin) has an elevation of 280 metres above sea level, serves the city of Appleton, and has a runway length of 2,439.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport is located in Greenville, Wisconsin and serves the nearby city of Appleton. The airport is 280 metres above sea level and has a runway which is 2439 metres long.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport can be found in Greenville, Wisconsin it serves Appleton, it's runway length is 2439 units and it is 280 metres above sea level.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greenville,_Wisconsin",
"property": "location",
"subject": "Appleton_International_Airport"
},
{
"object": "2439.0",
"property": "runwayLength",
"subject": "Appleton_International_Airport"
},
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
},
{
"object": "280",
"property": "elevationAboveTheSeaLevel_(in_metres)",
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],
"originaltriplesets": {
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{
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{
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{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
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{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
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{
"object": "\"279.806\"^^xsd:double",
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],
[
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{
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},
{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
},
{
"object": "\"280.0\"^^xsd:double",
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],
[
{
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"subject": "Appleton_International_Airport"
},
{
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{
"object": "Appleton,_Wisconsin",
"property": "city",
"subject": "Appleton_International_Airport"
},
{
"object": "\"279.806\"^^xsd:double",
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]
},
"shape": "NA",
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"xml_id": "Id19"
}
},
{
"569": {
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{
"comment": "good",
"lang": "",
"lex": "Appleton International airport, which serves Appleton, Wisconsin, is located in Greenville, Wisconsin which is part of both Menasha and Dale.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Appletone International airport serves the city of Appleton in Greenville, Wisconsin. Both Menasha and Dale are parts of Greenville.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton International airport serves Appleton in Greenville, Wisconsin. Both Menasha (town) and Dale are parts of Greenville.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greenville,_Wisconsin",
"property": "location",
"subject": "Appleton_International_Airport"
},
{
"object": "Menasha_(town),_Wisconsin",
"property": "isPartOf",
"subject": "Greenville,_Wisconsin"
},
{
"object": "Dale,_Wisconsin",
"property": "isPartOf",
"subject": "Greenville,_Wisconsin"
},
{
"object": "Appleton,_Wisconsin",
"property": "cityServed",
"subject": "Appleton_International_Airport"
}
],
"originaltriplesets": {
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{
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"property": "location",
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{
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"property": "isPartOf",
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{
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{
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id20"
}
},
{
"570": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Poaceae is of the order poales, the class of Monocotyledon and belongs to the order of Commelinids. It constitutes the surface of the 3rd runway at Ardmore Airport in New Zealand.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport (New Zealand)'s 3rd runway surface type is Poaceae which is a member of the order of Poales, the class is Monocotyledon, and belongs to the order of Commelinids.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport (New Zealand)'s 3rd runway is surfaced with Poaceae.Poaceae belongs to the Poales and Commelinids order and classified as Monocotyledon.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Poaceae",
"property": "3rd_runway_SurfaceType",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poales",
"property": "order",
"subject": "Poaceae"
},
{
"object": "Monocotyledon",
"property": "class",
"subject": "Poaceae"
},
{
"object": "Commelinids",
"property": "order",
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}
],
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{
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},
{
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"subject": "Poaceae"
},
{
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{
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]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id21"
}
},
{
"571": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Ardmore Airport in New Zealand is 34 meters above sea level and has a runway length of 597.0. It has the runway name 03R/21L and the 3rd runway is made of Poaceae.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore airport in New Zealand is 34 metres above sea level and has a runway length of 597.0. The 3rd runway is made of poaceae and one of the runways is called 03R/21L.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore airport in New Zealand has a 3rd runway made of poaceae and is 34 meters above sea level. It has a runway name of 03R/21L with a length of 597.0.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
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"property": "runwayLength",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "Poaceae",
"property": "3rd_runway_SurfaceType",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
"object": "34.0",
"property": "elevationAboveTheSeaLevel_(in_metres)",
"subject": "Ardmore_Airport_(New_Zealand)"
},
{
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},
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"xml_id": "Id22"
}
},
{
"572": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "With a runway length of 1,873, Atlantic City International Airport is located in Egg Harbor Township, New Jerse y(part of Atlantic County) in the United States.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Egg Harbor Township, is a township in Atlantic County, New Jersey, United States. With a runway length of 1873 metres, Atlantic City International Airport is located in Egg Harbor Township.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "1873.0",
"property": "runwayLength",
"subject": "Atlantic_City_International_Airport"
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{
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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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{
"object": "United_States",
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},
{
"object": "Atlantic_County,_New_Jersey",
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],
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{
"object": "\"1872.69\"^^xsd:double",
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{
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"subject": "Atlantic_City_International_Airport"
},
{
"object": "United_States",
"property": "country",
"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "Atlantic_County,_New_Jersey",
"property": "isPartOf",
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]
]
},
"shape": "NA",
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"xml_id": "Id23"
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},
{
"573": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suarez Madrid-Barajas Airport is operated by ENAIRE, which is located in the city of Madrid, Spain. The Adolfo Suarez Madrid-Barajas Airport is located in Madrid, Paracuellos de Jarama, San Sebastian de los Reyes and Alcobendas.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport can be found in Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas and is operated by ENAIRE from the city of Madrid, Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Adolfo Suárez Madrid–Barajas Airport, operated by ENAIRE (Madrid), is found in Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas; Spain.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Madrid",
"property": "locationCity",
"subject": "ENAIRE"
},
{
"object": "\"Madrid, Paracuellos de Jarama, San Sebastián de los Reyes and Alcobendas\"",
"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "Spain",
"property": "country",
"subject": "Madrid"
},
{
"object": "ENAIRE",
"property": "operatingOrganisation",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
}
],
"originaltriplesets": {
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{
"object": "Madrid",
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},
{
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"property": "location",
"subject": "Adolfo_Suárez_Madrid–Barajas_Airport"
},
{
"object": "Spain",
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},
{
"object": "ENAIRE",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id24"
}
},
{
"574": {
"category": "Airport",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Egg Harbor Township is in New Jersey, in the United States and is where Atlantic City International Airport is located. This airport has a runway length of 3048.0.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Egg Harbor Township, New Jersey is part of New Jersey in the United States. It is where Atlantic City International Airport(with a runway length of 3048.0) is located.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Atlantic City Airport, (runway length 3048.0) is located in Egg Harbor Township, which is part of New Jersey in the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "New_Jersey",
"property": "isPartOf",
"subject": "Egg_Harbor_Township,_New_Jersey"
},
{
"object": "3048.0",
"property": "runwayLength",
"subject": "Atlantic_City_International_Airport"
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"lex": "200 Public Square is located in Cleveland, Cuyahoga County, Ohio which is governed by Cleveland City Council.",
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{
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"lex": "Construction of the 12 floor Postmodern style building , 250 Delaware Avenue, which has a floor area of 30843.8 square metres began in January 2014.",
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"comment": "good",
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"lex": "3 Arena is located in Dublin, the Republic of Ireland, where Críona Ní Dhálaigh was Lord Mayor. The owner of 3Arena is Live Nation Entertainment.",
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"comment": "good",
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"lex": "Located in Denmark and with tenant Marriott International, AC Hotel Bella Sky Copenhagen is owned by Bella Center and has 23 floors.",
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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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"object": "Mulatu_Teshome",
"property": "leader",
"subject": "Ethiopia"
},
{
"object": "Addis_Ababa_Stadium",
"property": "isPartOf",
"subject": "Addis_Ababa"
},
{
"object": "Ethiopia",
"property": "country",
"subject": "Addis_Ababa_City_Hall"
},
{
"object": "Addis_Ababa",
"property": "location",
"subject": "Addis_Ababa_City_Hall"
}
],
[
{
"object": "Addis_Ababa_Stadium",
"property": "isPartOf",
"subject": "Addis_Ababa"
},
{
"object": "Ethiopia",
"property": "country",
"subject": "Addis_Ababa_City_Hall"
},
{
"object": "Mulatu_Teshome",
"property": "leaderName",
"subject": "Ethiopia"
},
{
"object": "Addis_Ababa",
"property": "location",
"subject": "Addis_Ababa_City_Hall"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id24"
}
},
{
"611": {
"category": "Building",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The leader of the US is known as the President and the capital city is Washington DC. One of the country's leaders if Paul Ryan and it is the location of 250 Delaware Avenue.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Paul Ryan is president of the United States and both 250 Delaware Avenue and Washington DC (the capital) are located there.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Paul Ryan is the leader, or President of the United States, of which Washington D.C. is the capital. 250 Delaware Avenue is part of the United States.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Paul_Ryan",
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"subject": "United_States"
},
{
"object": "United_States",
"property": "location",
"subject": "250_Delaware_Avenue"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Washington,_D.C.",
"property": "capital",
"subject": "United_States"
},
{
"object": "President_of_the_United_States",
"property": "leaderTitle",
"subject": "United_States"
},
{
"object": "Paul_Ryan",
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"subject": "United_States"
},
{
"object": "United_States",
"property": "location",
"subject": "250_Delaware_Avenue"
}
]
]
},
"shape": "NA",
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"xml_id": "Id25"
}
},
{
"612": {
"category": "ComicsCharacter",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Tim Brooke-Taylor was the star of Bananaman, an STV series first aired on 10/03/1983 and created by Steve Bright.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bananaman was created by Steve Bright and starred Tim Brooke-Taylor. The show was first aired on 3rd October 1983 by STV.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Tim Brooke-Taylor starred in Bananaman that was created by Steve Bright. It was broadcast by STV and first aired on 3 October 1983.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Tim_Brooke-Taylor",
"property": "starring",
"subject": "Bananaman"
},
{
"object": "Steve_Bright",
"property": "creator",
"subject": "Bananaman"
},
{
"object": "\"1983-10-03\"",
"property": "firstAired",
"subject": "Bananaman"
},
{
"object": "\"STV\"",
"property": "broadcastedBy",
"subject": "Bananaman"
}
],
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[
{
"object": "Tim_Brooke-Taylor",
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{
"object": "\"Steve Bright\"@en",
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},
{
"object": "\"1983-10-03\"^^xsd:date",
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{
"object": "\"STV\"@en",
"property": "network",
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}
]
]
},
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}
},
{
"613": {
"category": "ComicsCharacter",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Dan Mishkin and the Puerto Rican national, Ernie Colon created the comic character of Bolt who has the alternative name of Larry Bolatinsky.",
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},
{
"comment": "good",
"lang": "",
"lex": "Dan Mishkin and Ernie Colón (Puerto Rican) created the comic character Bolt, the alternative name of which is Larry Bolatinsky.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The comic book character of Bolt has the alternative name of Larry Bolatinsky. It was created by Dan Mishkin and the Puerto Rican national, Ernie Colon.",
"xml_id": "Id3"
}
],
"links": [],
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{
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{
"object": "Puerto_Ricans",
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"subject": "Ernie_Colón"
},
{
"object": "\"Larry Bolatinsky\"",
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"subject": "Bolt_(comicsCharacter)"
},
{
"object": "Dan_Mishkin",
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"subject": "Bolt_(comicsCharacter)"
}
],
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[
{
"object": "Dan_Mishkin",
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},
{
"object": "Puerto_Ricans",
"property": "nationality",
"subject": "Ernie_Colón"
},
{
"object": "Ernie_Colón",
"property": "creator",
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},
{
"object": "\"Larry Bolatinsky\"@en",
"property": "alterEgo",
"subject": "Bolt_(DC_Comics)"
}
],
[
{
"object": "Ernie_Colón",
"property": "creators",
"subject": "Bolt_(DC_Comics)"
},
{
"object": "Puerto_Ricans",
"property": "nationality",
"subject": "Ernie_Colón"
},
{
"object": "\"Larry Bolatinsky\"@en",
"property": "alterEgo",
"subject": "Bolt_(DC_Comics)"
},
{
"object": "Dan_Mishkin",
"property": "creator",
"subject": "Bolt_(DC_Comics)"
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]
]
},
"shape": "NA",
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}
},
{
"614": {
"category": "ComicsCharacter",
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{
"comment": "good",
"lang": "",
"lex": "Baymax is a character in Big Hero 6 that stars Damon Wayans Jr. The character was created by the American, Duncan Rouleau.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Damon Wayans Jr starred in the film Big Hero 6 which also included the character of Baymax who was created by the American, Duncan Rouleau.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baymax is a character in the film Big Hero 6 which starred Damon Wayans, Jr. The character was created by the American national Duncan Rouleau.",
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}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Duncan_Rouleau",
"property": "creator",
"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
},
{
"object": "Damon_Wayans,_Jr.",
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}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
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},
{
"object": "Duncan_Rouleau",
"property": "creators",
"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
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},
{
"object": "Damon_Wayans,_Jr.",
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]
]
},
"shape": "NA",
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}
},
{
"615": {
"category": "ComicsCharacter",
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{
"comment": "good",
"lang": "",
"lex": "Baymax, a character in Big Hero 6, starring Jamie Chung, was created by Duncan Rouleau, an American.",
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},
{
"comment": "good",
"lang": "",
"lex": "Jamie Chung starred in the film Big Hero 6 in which the character of Baymax appeared. Baymax was created by the American national, Duncan Rouleau.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Duncan Rouleau, who is American, created the character Baymax, which appeared in the movie Big Hero 6 starring Jamie Chung.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Duncan_Rouleau",
"property": "creator",
"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
"property": "series",
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},
{
"object": "Jamie_Chung",
"property": "starring",
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],
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"originaltripleset": [
[
{
"object": "Duncan_Rouleau",
"property": "creator",
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},
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
},
{
"object": "Jamie_Chung",
"property": "starring",
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}
],
[
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Duncan_Rouleau",
"property": "creators",
"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
},
{
"object": "Jamie_Chung",
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]
]
},
"shape": "NA",
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}
},
{
"616": {
"category": "ComicsCharacter",
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{
"comment": "good",
"lang": "",
"lex": "Big Hero 6 starred May Rudolph and the character of Baymax who was created by the American, Duncan Rouleau.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baymax is a character in the film Big Hero 6 which starred Maya Rudolph. The creator of this character was American born Duncan Rouleau.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Maya Rudolph stars in Big Hero 6 in which the character Baymax also appears. Baymax was created by the American, Duncan Rouleau.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Duncan_Rouleau",
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"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
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"subject": "Baymax"
},
{
"object": "Maya_Rudolph",
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}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Americans",
"property": "nationality",
"subject": "Duncan_Rouleau"
},
{
"object": "Duncan_Rouleau",
"property": "creators",
"subject": "Baymax"
},
{
"object": "Big_Hero_6_(film)",
"property": "series",
"subject": "Baymax"
},
{
"object": "Maya_Rudolph",
"property": "starring",
"subject": "Big_Hero_6_(film)"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id5"
}
},
{
"617": {
"category": "Food",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bread, almonds, garlic, water, and olive oil are the main ingredients in Ajoblanco, a food which originates from Andalusia, in Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is from Andalusia Spain and ingredients include bread, almonds, garlic, water, and olive oil.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco, a dish from the Andalusia region of Spain, has the main ingredients bread, almonds, garlic, water and olive oil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "\"Bread, almonds, garlic, water, olive oil\"",
"property": "mainIngredients",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
"property": "region",
"subject": "Ajoblanco"
},
{
"object": "Bread",
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"subject": "Ajoblanco"
}
],
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[
{
"object": "Spain",
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"subject": "Ajoblanco"
},
{
"object": "\"Bread, almonds, garlic, water, olive oil\"@en",
"property": "mainIngredient",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
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},
{
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}
]
]
},
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}
},
{
"618": {
"category": "Food",
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{
"comment": "good",
"lang": "",
"lex": "Water is an ingredient in Ajoblanco, also known as Ajo blanco, a dish that comes from Andalusia Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is a food originates from the Andalusian region of Spain and contains water.An other alternative name of the dish is Ajo blanco.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Andalusia",
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},
{
"object": "\"Ajo blanco\"",
"property": "alternativeName",
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},
{
"object": "Water",
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"subject": "Ajoblanco"
}
],
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[
{
"object": "Spain",
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},
{
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},
{
"object": "\"Ajo blanco\"@en",
"property": "alias",
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},
{
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}
]
]
},
"shape": "NA",
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}
},
{
"619": {
"category": "Food",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Lemper, a dish variation of Arem-Arem contains Banana leaf in which is cooked compressed rice with vegetables or minced meat fillings and is a form of Javanese cuisine.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Arem-arem are compressed rice cooked in banana leaf with vegetables or minced meat fillings. The dish is of Javanese cuisine and has a variation known as Lemper.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lemper",
"property": "dishVariation",
"subject": "Arem-arem"
},
{
"object": "Banana_leaf",
"property": "ingredient",
"subject": "Arem-arem"
},
{
"object": "\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\"",
"property": "mainIngredients",
"subject": "Arem-arem"
},
{
"object": "Javanese_cuisine",
"property": "region",
"subject": "Arem-arem"
}
],
"originaltriplesets": {
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[
{
"object": "Lemper",
"property": "hasVariant",
"subject": "Arem-arem"
},
{
"object": "Banana_leaf",
"property": "ingredient",
"subject": "Arem-arem"
},
{
"object": "\"compressed rice cooked inbanana leafwith vegetables or minced meat fillings\"",
"property": "ingredientName",
"subject": "Arem-arem"
},
{
"object": "Javanese_cuisine",
"property": "region",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id3"
}
},
{
"620": {
"category": "Food",
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{
"comment": "good",
"lang": "",
"lex": "Arem-arem is a food that is found nationwide in Indonesia, but more specific to Java, it can be served as a main course and has a banana leaf in it. It's main ingredients are compressed rice cooked in banana leaf with vegetables or minced meat fillings.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Arem-arem are compressed rice cooked in banana leaf with vegetables or minced meat fillings. It is served as a main course and is found nationwide in Indonesia and more specifically in Java.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banana_leaf",
"property": "ingredient",
"subject": "Arem-arem"
},
{
"object": "\"Main course\"",
"property": "course",
"subject": "Arem-arem"
},
{
"object": "\"compressed rice cooked in banana leaf with vegetables or minced meat fillings\"",
"property": "mainIngredients",
"subject": "Arem-arem"
},
{
"object": "\"Nationwide in Indonesia, but more specific to Java\"",
"property": "region",
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}
],
"originaltriplesets": {
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[
{
"object": "Banana_leaf",
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},
{
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"property": "ingredientName",
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},
{
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"property": "course",
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},
{
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"property": "region",
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],
[
{
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{
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},
{
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"property": "mainIngredient",
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},
{
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"property": "region",
"subject": "Arem-arem"
}
]
]
},
"shape": "NA",
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"xml_id": "Id4"
}
},
{
"621": {
"category": "Food",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce originates from the region of Rome in Italy. It is made with tomatoes, red chili, garlic and olive oil.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is a traditional dish from Rome,Italy.The main ingredients of it are tomatoes,red chili pepper,garlic and olive oil.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arrabbiata sauce is a traditional dish from Rome in Italy. An important ingredient is chili pepper and it also contains tomatoes, red chili, garlic and olive oil.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Tomatoes, red chili, garlic, olive oil\"",
"property": "mainIngredients",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
],
"originaltriplesets": {
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[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
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"property": "mainIngredient",
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},
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Tomatoes,red chili,garlic,olive oil\"",
"property": "ingredientName",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Rome\"@en",
"property": "region",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
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"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Chili_pepper",
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},
{
"object": "\"Tomatoes, red chili, garlic, olive oil\"@en",
"property": "mainIngredient",
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},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
],
[
{
"object": "Chili_pepper",
"property": "ingredient",
"subject": "Arrabbiata_sauce"
},
{
"object": "\"Tomatoes,red chili,garlic,olive oil\"",
"property": "ingredientName",
"subject": "Arrabbiata_sauce"
},
{
"object": "Italy",
"property": "country",
"subject": "Arrabbiata_sauce"
},
{
"object": "Rome",
"property": "region",
"subject": "Arrabbiata_sauce"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id5"
}
},
{
"622": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros Negre is a traditional dish from the Catalonia region of Spain. The main ingredients of Arros negre are white rice, cuttlefish or squid, cephalopod ink, and cubanelle peppers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre is from the region of Catalonia in Spain. The main ingredients of Arros negre are white rice, cuttlefish or squid, cephalopod ink, and cubanelle peppers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre, from the Catalonia region of Spain, includes Cephalopod ink, white rice, cuttlefish/squid and 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": "Cephalopod_ink",
"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": "Cephalopod_ink",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id6"
}
},
{
"623": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Arròs negre, which is from Spain, are white rice, cuttlefish or squid, cephalopod ink, cubanelle and cubanelle peppers. Arròs negre is from the Catalonia region.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in arròs negre are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers. It is a traditional dish from the Catalonia region of Spain.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients in arròs negre are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers. It is a traditional Spanish dish from the Catalonia region.",
"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": "Cubanelle",
"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": "Cubanelle",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id7"
}
},
{
"624": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arros negre is a traditonal dish from the Valencian Community, Spain. It has the main ingredients of white rice, cuttlefish or squid, cephalopod ink and cubnelle peppers.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from the region of the Valencian Community, Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink and cubanelle peppers.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arros negre comes from the region of the Valencian Community in Spain. The main ingredients are white rice, cuttlefish or squid, cephalopod ink, cubanelle peppers.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"",
"property": "mainIngredients",
"subject": "Arròs_negre"
},
{
"object": "Cubanelle",
"property": "ingredient",
"subject": "Arròs_negre"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Valencian_Community",
"property": "region",
"subject": "Arròs_negre"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "\"White rice, cuttlefish or squid, cephalopod ink, cubanelle peppers\"@en",
"property": "mainIngredient",
"subject": "Arròs_negre"
},
{
"object": "Cubanelle",
"property": "ingredient",
"subject": "Arròs_negre"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id8"
}
},
{
"625": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is found in the region of Sumatra and Malay peninsula in Malaysia. It can also be called Asam padeh and it's ingredients are fish cooked in sour and hot sauce.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas (aka asam padeh) is from the Sumatra and Malay Peninsula regions of Malaysia. The main ingredients are fish cooked in sour and hot sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam padeh is also known as asam pedas and is a food found in Malaysia originating from the Sunatra and Malay Peninsula regions. The main ingredients is fish cooked in a sour and hot sauce.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Sumatra and Malay Peninsula\"",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "mainIngredients",
"subject": "Asam_pedas"
},
{
"object": "\"Asam padeh\"",
"property": "alternativeName",
"subject": "Asam_pedas"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "\"Sumatra and Malay Peninsula\"@en",
"property": "region",
"subject": "Asam_pedas"
},
{
"object": "\"Fish cooked in sour and hot sauce\"",
"property": "ingredientName",
"subject": "Asam_pedas"
},
{
"object": "\"Asam padeh\"@en",
"property": "alternateName",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id9"
}
},
{
"626": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia and Sumatra. Malaysian Malay is an ethnic group in Malaysia and Sumatra has an ethnic group called the Minangkabau people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is a food found in Malaysia and Sumatra. Malay is the ethnic group of Malaysia and the Minangkabau people are an ethnic group of Sumatra.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Asam pedas is found in Malaysia, originating from Sumatra. Sumatra is home to the Minangkabau people and Malaysia, the Malay people.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Malaysia",
"property": "country",
"subject": "Asam_pedas"
},
{
"object": "Malaysian_Malay",
"property": "ethnicGroup",
"subject": "Malaysia"
},
{
"object": "Minangkabau_people",
"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": "Minangkabau_people",
"property": "ethnicGroup",
"subject": "Sumatra"
},
{
"object": "Sumatra",
"property": "region",
"subject": "Asam_pedas"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id10"
}
},
{
"627": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion comes from the Kansas city metro area in the U.S. The main ingredient in it is bacon and also includes sausage.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the Kansas City metropolitan area of the United States and includes the main ingredient of sausage and bacon.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion is from the Kansas City metropolitan area of the United States. The main ingredient is bacon along with sausage.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Kansas_City_metropolitan_area",
"property": "region",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "mainIngredients",
"subject": "Bacon_Explosion"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Sausage",
"property": "ingredient",
"subject": "Bacon_Explosion"
},
{
"object": "Kansas_City_metropolitan_area",
"property": "region",
"subject": "Bacon_Explosion"
},
{
"object": "Bacon",
"property": "mainIngredient",
"subject": "Bacon_Explosion"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id11"
}
},
{
"628": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States, where one of the leaders is Paul Ryan. It is also where Asian Americans are one of the ethnic groups and Washington, D.C. is the capital.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The United States is the country of the Bacon Explosion and the leader is Paul Ryan. Asian Americans are an ethnic group in the United States and the capital is Washington, D.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Bacon Explosion originates from the United States. The U.S. boasts leader Paul Ryan, is home to Asian Americans and has its capital in Washington D.C.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Paul_Ryan",
"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": "Paul_Ryan",
"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": "Id12"
}
},
{
"629": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwiches are popular in the UK, have a variant called a BLT, include the ingredient bacon and sometimes go by the name of: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The bacon sandwich, of which an ingredient is bacon, has a variation called the BLT and 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"
},
{
"comment": "good",
"lang": "",
"lex": "A bacon sandwich, which is a variation of the BLT, can also be known as a bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm, or bacon muffin. It is a dish containing bacon and comes from the United Kingdom.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"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": "Bacon",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"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": "Bacon",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id13"
}
},
{
"630": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwich is a dish from the United Kingdom and a variation is the BLT. An ingredient used is brown sauce and it's different names include Bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The country that bacon sandwich comes from is 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. Brown sauce can be served with it and a variation is the BLT.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon sandwiches are popular in the UK and can contain the ingredient brown sauce. A variation of the sandwich is a BLT and it 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": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "BLT",
"property": "dishVariation",
"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": "Brown_sauce",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "BLT",
"property": "variations",
"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": "Brown_sauce",
"property": "ingredient",
"subject": "Bacon_sandwich"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id14"
}
},
{
"631": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "From the United Kingdom, the bacon sandwich's main ingredients are bread and bacon and a condiment, often ketchup or brown sauce. It 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 originates from the United Kingdom and its main ingredients are bread,bacon with ketchup or brown sauce.It 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"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of a bacon sandwich, (popular in the UK), are bread, bacon and ketchup or brown sauce as a condiment. The bacon sandwich has several alternative names including: bacon butty, bacon sarnie, rasher sandwich, bacon sanger, piece 'n bacon, bacon cob, bacon barm and bacon muffin.",
"xml_id": "Id3"
}
],
"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": "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": "\"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": "4",
"xml_id": "Id15"
}
},
{
"632": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska has the main ingredients of meringue, ice cream and sponge cake (or Christmas pudding). It is found in France, the US, China, Hong Kong, New York and Paris.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska contains: ice cream, sponge cake (Christmas pudding) and meringue. It is found in Hong Kong, New York and Paris. The origins are either France, Canada or the United States.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"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": "\"Paris, New York or Hong Kong\"",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"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": "\"Paris, New York or Hong Kong\"@en",
"property": "region",
"subject": "Baked_Alaska"
},
{
"object": "Meringue",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id16"
}
},
{
"633": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from either Paris, New York USA or Hong Kong. Meringue, ice cream, sponge cake or Christmas pudding are main ingredients in baked Alaska.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska is found in Hong Kong, New York and Paris. Meringue, ice cream, sponge cake or Christmas pudding are its main ingredients.",
"xml_id": "Id2"
}
],
"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": "Ice_cream",
"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": "Ice_cream",
"property": "ingredient",
"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id17"
}
},
{
"634": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The bakewell tart is popular in the Derbyshire Dales area which is part of Derbyshire. It's a variant of bakewell pudding and uses frangipane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart, a variation of Bakewell pudding, includes the ingredient frangipane and originates from the Derbyshire Dales region of Derbyshire.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variant of bakewell pudding and comes from the Derbyshire Dales. It contains frangipane.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "isPartOf",
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},
{
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"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id18"
}
},
{
"635": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variation of Bakewell pudding that originates from the Derbyshire Dales. It is a dessert that has the main ingredients of ground almond, jam, butter and eggs.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variation of Bakewell pudding which is a dessert that comes from the Derbyshire Dales area and has the main ingredients ground almonds, jam, butter and eggs.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell pudding is a dessert from the Derbyshire Dales region and contains the ingredients ground almonds, jam, butter and eggs. It has a variation called Bakewell tart.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "dishVariation",
"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": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"",
"property": "ingredientName",
"subject": "Bakewell_pudding"
},
{
"object": "\"Dessert\"@en",
"property": "course",
"subject": "Bakewell_pudding"
}
],
[
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "\"Dessert\"@en",
"property": "course",
"subject": "Bakewell_pudding"
},
{
"object": "\"Ground almond, jam, butter, eggs\"@en",
"property": "mainIngredient",
"subject": "Bakewell_pudding"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id19"
}
},
{
"636": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "From the Derbyshire Dales region (in Derbyshire), isBakewell pudding. A variation of which, is Bakewell tart, its main ingredient is shortcrust pastry.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variation of Bakewell pudding which uses shortcut pastry and comes from the Derbyshire Dales in Derbyshire.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakewell tart is a variant of bakewell pudding from the Derbyshire Dales in Derbyshire. The main ingredient is shortcrust pastry.",
"xml_id": "Id3"
}
],
"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": "Shortcrust_pastry",
"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": "Shortcrust_pastry",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id20"
}
},
{
"637": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a Colombian dish from the Paisa region. Its main ingredients include 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 originates from the Paisa region, Columbia. 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": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a typical Colombian dish from the Paisa region and contains the ingredients: 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": "Rice",
"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": "Rice",
"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": "Id21"
}
},
{
"638": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor is an Indonesian food and a variation of the Siomay dish.It contains a fried fish dumpling with tofu and vegetables in peanut suace.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, (a variation of Siomay), is a dish found in Indonesia and its main ingredients are fried fish dumpling with tofu and vegetables in peanut sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Batagor, from Indonesia, is a dish consisting of fried fish dumplings, tofu and vegetables in peanut sauce. Siomay is a variation of batagor.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Batagor"
},
{
"object": "\"fried fish dumpling with tofu and vegetables in peanut sauce\"",
"property": "mainIngredients",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "\"fried fish dumpling with tofu and vegetables in peanut sauce\"@en",
"property": "mainIngredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
}
],
[
{
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"property": "ingredient",
"subject": "Batagor"
},
{
"object": "\"fried fish dumpling with tofu and vegetables in peanut sauce\"@en",
"property": "mainIngredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
},
{
"object": "\"Indonesia\"@en",
"property": "country",
"subject": "Batagor"
}
],
[
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
},
{
"object": "\"fried fish dumpling with tofu and vegetables inpeanut sauce\"",
"property": "ingredientName",
"subject": "Batagor"
},
{
"object": "\"Indonesia\"@en",
"property": "country",
"subject": "Batagor"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Peanut_sauce",
"property": "ingredient",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Batagor"
},
{
"object": "\"fried fish dumpling with tofu and vegetables inpeanut sauce\"",
"property": "ingredientName",
"subject": "Batagor"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id22"
}
},
{
"639": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Batagor is found in the country of Indonesia. It is a variation on Siomay or Shumai, the latter of which includes Shiitake.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Shumai, which has shiitake as a component and is similar to Siomay, is a variation of the Indonesian dish batagor.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "dishVariation",
"subject": "Shumai"
},
{
"object": "Shiitake",
"property": "ingredient",
"subject": "Shumai"
},
{
"object": "Shumai",
"property": "dishVariation",
"subject": "Batagor"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Siomay",
"property": "variations",
"subject": "Shumai"
},
{
"object": "Shiitake",
"property": "ingredient",
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},
{
"object": "Shumai",
"property": "hasVariant",
"subject": "Batagor"
}
],
[
{
"object": "Indonesia",
"property": "country",
"subject": "Batagor"
},
{
"object": "Siomay",
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"subject": "Shumai"
},
{
"object": "Shiitake",
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},
{
"object": "Shumai",
"property": "variations",
"subject": "Batagor"
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id23"
}
},
{
"640": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish from the country of Singapore, where Halimah Yacob is leader and Standard Chinese is spoken. The dish originated from the region of Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow comes from the region of Indonesia and also Singapore. The leader of Singapore, where Standard Chinese is the spoken language, is Halimah Yacob.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Standard_Chinese",
"property": "language",
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},
{
"object": "Halimah_Yacob",
"property": "leaderName",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id24"
}
},
{
"641": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish found 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 main ingredients in beef kway teow.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is served in the region of Indonesia and is also found in Singapore. 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": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beek kway teos includes: kway teow, beef tenderloin, gula Melaka, black beans, garlic, oyster, soya and soy sauce, lengkuas, chili and sesame oil. It is found in Singapore.",
"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": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"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": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id25"
}
},
{
"642": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is known to come from the Singapore region and also Indonesia. It's 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": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow, originating in Indonesia, is a dish found in Singapore. Its 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": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "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 Beef kway teow. The dish originated in Singapore and is found in Indonesia.",
"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": "Oyster_sauce",
"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": "Oyster_sauce",
"property": "ingredient",
"subject": "Beef_kway_teow"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id26"
}
},
{
"643": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow comes from the Singapore region where The English language is spoken. The dish is found in the countries of Indonesia and Singapore. The leader of Singapore is Tony Tan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a dish of Singapore where Tony Tan is the leader and English is spoken. The dish is also popular in Indonesia.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Singapore's leader is Tony Tan and one of the country's spoken language is English.Beef kway teow is made there and Indonesia too.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id27"
}
},
{
"644": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country India, where two of the leaders are T.S. Thakur and Narendra Modi. It is also where the Indian rupee is the currency.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country India, where the currency is the rupee. The leader of India is either T. S. Thakur and/or Narendra Modi.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the country India, where the currency is the rupee and the leader is either T. S. Thakur and/or Narendra Modi.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leaderName",
"subject": "India"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leaderName",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leader",
"subject": "India"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_rupee",
"property": "currency",
"subject": "India"
},
{
"object": "T._S._Thakur",
"property": "leader",
"subject": "India"
},
{
"object": "Narendra_Modi",
"property": "leaderName",
"subject": "India"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id28"
}
},
{
"645": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji originate from India where the Indian people come from. The leaders of the country are T S Thakur and Sumitra Mahajan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originate from India where the Indian people are lead by T S Thakur and Sumitra Mahajan.",
"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": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
}
],
"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"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id29"
}
},
{
"646": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji come from the Karnataka region of India. Narendra Modi is the name of the leader of India, another leader is Sumitra Mahajan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from the Karnataka region of India.The country's leaders are Narendra Modi and Sumitra Mahajan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originate from the Karnataka region in India. The country's leaders are Narendra Modi and Sumitra Mahajan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Narendra_Modi",
"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": "Narendra_Modi",
"property": "leader",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leaderName",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
}
],
[
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Narendra_Modi",
"property": "leaderName",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"property": "leader",
"subject": "India"
},
{
"object": "Karnataka",
"property": "region",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id30"
}
},
{
"647": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from Karnataka, India. One leader in India is T.S. Thakur and another Vajubhai Vala.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji are found in the region of Karnataka, India. The leader of India is called T. S. Thakur and Vajubhai Vala is the leader of Karnataka.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji comes from the Karnataka region or India. Vajubhai Vala is a leader of Karnatka and T.S. Thakur is the leader of India.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"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": "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": "4",
"xml_id": "Id31"
}
},
{
"648": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Cookies and binignit are types of dessert. One of the ingrediens in binignit is sweet potato which belongs to the order Solanales.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Sweet_potato",
"property": "ingredient",
"subject": "Binignit"
},
{
"object": "Dessert",
"property": "course",
"subject": "Binignit"
},
{
"object": "Cookie",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Solanales",
"property": "order",
"subject": "Sweet_potato"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id32"
}
},
{
"649": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut are the main ingredients in Bionico, a food found in Guadalajara, in Mexico.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico has the ingredients chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins and can be found in Guadalajara, Mexico.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The main ingredients of Bionico, a dish from the Guadalaraja region of Mexico, are chopped fruits, sour cream, condensed milk, granola, shredded coconut and raisins.",
"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": "Condensed_milk",
"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": "Condensed_milk",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id33"
}
},
{
"650": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a food found in the Jalisco region of Mexico;its ingredients contain the following: sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico comes from the Jalisco region of Mexico.The main ingredients of the food are:chopped Fruits, sour cream, condensed milk, granola, shredded coconut and raisins.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is found in the region of Jalisco, Mexico and consists of sour cream, chopped fruits, condensed milk. granola, raisins and shredded coconut.",
"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": "Jalisco",
"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": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Sour_cream",
"property": "ingredient",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id34"
}
},
{
"651": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso is a Chinese dish found in Indonesia, the leader of which is Jusuf Kalla. Jakarta is the capital of Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is from the Chinese cuisine and is found in Indonesia where the capital city is Jakarta and Jusuf Kalla is a leader.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Chinese_cuisine",
"property": "country",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Chinese_cuisine",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id35"
}
},
{
"652": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakso comes from Indonesia, and celery is a main ingredient. Jasuf Kalla is a leader in Indonesia.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Celery is an ingredient of Bakso, a dish from the country of Indonesia, where Jusuf Kalla is a leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso ( which contains celery) is a dish found in Indonesia where it originated. One of the country's leaders is Jusuf Kalla.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
},
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
},
{
"object": "Celery",
"property": "ingredient",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id36"
}
},
{
"653": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Two leaders of Italy, where Amatriciana sauce is found, are Matteo Renzi and Sergio Mattarella. Its capital is Rome.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italy is the country Amatriciana sauce comes from. The capital is Rome and political leaders include Matteo Renzi and Sergio Mattarella.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Rome is the capital of Italy, where Sergio Mattarell and Matteo Renzi are leaders and where amatriciana sauce is a traditional dish.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"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": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
],
[
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leader",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id37"
}
},
{
"654": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet comes from the Singaporean region and Halimah Yacob is the country's leader.It can be found in Java as well where Baduy people is an ethnic group.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from the region of Singapore which is lead by Halimah Yacob. It is also found in Java where the Baduy are an ethnic group.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayem penyet is a dish from Java where the Baduy are an ethnic group. It also originates from the region of Singapore where the leader is Halimah Yacob.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id38"
}
},
{
"655": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from Java and Singapore. The leader of the latter is Tony Tan and the Banyumasan people are an ethnic group from Java.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet is a dish from Singapore and Java. Banyumasan people is one of the ethnic groups in Java and Tony Tan is a leader in Singapore.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Banyumasan people are an ethnic group in Java, where the dish ayam penyet is found. The dish originates in Singapore, where Tony Tan is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Banyumasan_people",
"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": "Banyumasan_people",
"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": "Id39"
}
},
{
"656": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The language spoken in the Philippines is Philippine Spanish and batchoy is eaten there. Ethnic groups include the llocano people and the Moro people.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is eaten in the Philippines where Phillippine Spanish is spoken and ethnic groups include the Ilocano and Moro.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Moro and the Ilocano people are ethnic groups within the Philippines where batchoy is eaten and the language used is Philippine Spanish.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ilocano_people",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Philippine_Spanish",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Moro_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ilocano_people",
"property": "ethnicGroups",
"subject": "Philippines"
},
{
"object": "Philippine_Spanish",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Moro_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id40"
}
},
{
"657": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is a traditional dish from Spain whose leader is Felipe VI and Euro is its currency. Spaniards are the ethnic group of Spain.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Euro is the currency in Spain, where the leader is Felipe VI and Spaniards are an ethnic group. Spain is also the home of the traditional dish Arròs negre.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Arròs negre is a dish from Spain where the leader is Felipe VI. Spaniards are an ethnic group of Spain and the currency used is the euro.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Euro",
"property": "currency",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leaderName",
"subject": "Spain"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Euro",
"property": "currency",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Arròs_negre"
},
{
"object": "Spaniards",
"property": "ethnicGroup",
"subject": "Spain"
},
{
"object": "Felipe_VI_of_Spain",
"property": "leader",
"subject": "Spain"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id41"
}
},
{
"658": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Adams County, Pennsylvania has Carrol County, Maryland to its southeast and Pennsylvania's Cumberland County is to the north of the Adams County. The 11th Mississippi Infantry Monument is in Adams County of the municipality of Gettysburg Pennsylvania. .",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is located at Gettysburg, Adams County, Pennsylvania, which has Cumberland County, Pennsylvania to its north and Carrol County, Maryland to its southeast.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The municipality of Gettysburg, Adams County, Pennsylvania is the location of the 11th Mississippi Infantry monument. Adams County is south of Cumberland County and to the northwest of Carroll County, Maryland.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Cumberland_County,_Pennsylvania",
"property": "has to its north",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Gettysburg,_Pennsylvania",
"property": "municipality",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "location",
"subject": "11th_Mississippi_Infantry_Monument"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Carroll_County,_Maryland",
"property": "southeast",
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{
"object": "Cumberland_County,_Pennsylvania",
"property": "north",
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},
{
"object": "Gettysburg,_Pennsylvania",
"property": "municipality",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "region",
"subject": "11th_Mississippi_Infantry_Monument"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1"
}
},
{
"659": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is a Contributing Property located in Adams County, Pennsylvania, with Franklin County to its west and Carrol County, Maryland to its southeast.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Pennsylvania's Franklin County is found to the west of Adams County while Carrol County Maryland is southeast of Adams County Pennsylvania. The location of the 11th Mississippi Infantry Monument is Adams County, Pennsylvania and falls under the category of Contributing property.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Franklin_County,_Pennsylvania",
"property": "has to its west",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Contributing_property",
"property": "category",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "location",
"subject": "11th_Mississippi_Infantry_Monument"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Franklin_County,_Pennsylvania",
"property": "west",
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},
{
"object": "Carroll_County,_Maryland",
"property": "southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Contributing_property",
"property": "category",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "region",
"subject": "11th_Mississippi_Infantry_Monument"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id2"
}
},
{
"660": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baku, the capital of Azerbaijan is the location of the Turkish Martyrs memorial dedicated to the soldiers of the Ottoman army killed in the Battle of Baku. The memorial was designed by Huseyin Butuner and Hilmi Guner.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs Memorial has been dedicated to Ottoman Army soldiers killed in the Battle of Baku and is located in Azerbaijan's capital Baku. The Memorial was designed by Hüseyin Bütüner and Hilmi Güner.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baku is the capital of Azerbaijan and the location of the Turkish Martyrs memorial dedicated to the soldiers of the Ottoman army who died in the battle of Baku. The memorial was designed by Huseyin Butuner and Hilmi Guner.",
"xml_id": "Id3"
}
],
"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": "\"Hüseyin Bütüner and Hilmi Güner\"",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"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"
},
{
"object": "\"Hüseyin Bütüner and Hilmi Güner\"@en",
"property": "designer",
"subject": "Baku_Turkish_Martyrs'_Memorial"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id3"
}
},
{
"661": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Baku is the capital of Azerbaijan and the leader of the legislature (the National Assembly) is Artur Rasizade. Azerbaijan is the location of the Baku Turkish Martyrs memorial.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The National Assembly is the legislative branch of government in Azerbaijan where the leader is Artur Rasizade. The Turkish martyrs memorial is located in the capital, Baku.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial is located in Baku, the capital of Azerbaijan, where leader is Artur Rasizade and the legislature is known as the National Assembly.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Artur_Rasizade",
"property": "leader",
"subject": "Azerbaijan"
},
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "National_Assembly_(Azerbaijan)",
"property": "legislature",
"subject": "Azerbaijan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Artur_Rasizade",
"property": "leader",
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},
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "National_Assembly_(Azerbaijan)",
"property": "legislature",
"subject": "Azerbaijan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id4"
}
},
{
"662": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The red granite and white marble Baku Turkish Martyrs' Memorial is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku, and is located in Baku, Azerbaijan, where the leader is Artur Rasizade.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade was an Azerbaijan leader. Baku Turkish Martyrs' Memorial is made of red granite and white marble, is dedicated to the Ottoman Army soldiers killed in the Battle of Baku and located in Azerbaijan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade is an Azerbaijan leader. The Baku Turkish Martyrs' Memorial located in Baku, Azerbaijan is made with red granite and white marble and has been dedicated to Ottoman Army soldiers killed in the Battle of Baku.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Artur_Rasizade",
"property": "leader",
"subject": "Azerbaijan"
},
{
"object": "\"Red granite and white marble\"",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "\"Ottoman Army soldiers killed in the Battle of Baku\"",
"property": "dedicatedTo",
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},
{
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Artur_Rasizade",
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},
{
"object": "\"Red granite and white marble\"@en",
"property": "material",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"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": "4",
"xml_id": "Id5"
}
},
{
"663": {
"category": "Monument",
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"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial, which is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku, is located in Azerbaijan, where the official leader (Prime Minister of Azerbaijan) is called Artur Rasizade.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyr's Memorial is dedicated to the Ottoman Army soldiers killed in the Battle of Baku, and is located in Azerbaijan, which is led by the Prime Minister of Azerbaijan, Artur Rasizade.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Artur Rasizade is the Prime Minister of Azerbaijan. Azerbaijan is the location of the Baku Turkish Martyrs memorial which is dedicated to the soldiers of the Ottoman army killed in the Battle of Baku.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Prime_Minister_of_Azerbaijan",
"property": "leaderTitle",
"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": "Artur_Rasizade",
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}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Prime_Minister_of_Azerbaijan",
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},
{
"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"
},
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id6"
}
},
{
"664": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The full name of A.C. Chievo Verona is Associazione Calcio ChievoVerona S.r.l. and their home ground in Verona, Italy holds 39371 fans. The club played the 2014-15 season in Serie A.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "A.C. Chievo Verona played the 2014-15 season in Serie A and has 39371 members. Their full name is \"Associazione Calcio ChievoVerona S.r.l.\" and their home ground is in Verona, Italy.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "\"Associazione Calcio ChievoVerona S.r.l.\"",
"property": "fullname",
"subject": "A.C._Chievo_Verona"
},
{
"object": "\"Verona, Italy\"",
"property": "ground",
"subject": "A.C._Chievo_Verona"
},
{
"object": "2014–15_Serie_A",
"property": "season",
"subject": "A.C._Chievo_Verona"
},
{
"object": "39371",
"property": "numberOfMembers",
"subject": "A.C._Chievo_Verona"
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],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "\"Associazione Calcio ChievoVerona S.r.l.\"@en",
"property": "fullname",
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{
"object": "\"Verona, Italy\"@en",
"property": "ground",
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},
{
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{
"object": "39371",
"property": "capacity",
"subject": "A.C._Chievo_Verona"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id1"
}
},
{
"665": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Italian born Rolando Maran manages Associazione Calcio Chievo Verona. He is also in the Unione Triestina 2012 S.S.D. club as well as playing for Carrarese Calcio.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italian born Rolando Maran plays at the Carrarese Calcio and is in the Unione Triestina 2012 S.S.D. club. He is currently the manager of AC Chievo Verona.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Born in Italy, Rolando Maran is the manager of AC Chievo Verona. He also plays for Carrarese Calcio and is in the Unione Triestina 2012 S.S.D. club.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Rolando_Maran",
"property": "manager",
"subject": "A.C._Chievo_Verona"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Rolando_Maran"
},
{
"object": "Unione_Triestina_2012_S.S.D.",
"property": "club",
"subject": "Rolando_Maran"
},
{
"object": "Carrarese_Calcio",
"property": "club",
"subject": "Rolando_Maran"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Rolando_Maran",
"property": "manager",
"subject": "A.C._Chievo_Verona"
},
{
"object": "Italy",
"property": "placeOfBirth",
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},
{
"object": "Unione_Triestina_2012_S.S.D.",
"property": "managerClub",
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},
{
"object": "Carrarese_Calcio",
"property": "team",
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]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id2"
}
},
{
"666": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Michele Marcolini, manager of A.C. Lumezzane, was born in Italy, owns Torino F.C. And has been associated with F.C. Bari 1908.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Italian born Michele Marcolini, previously at FC Bari 1908 and manager of AC Lumezzane, is the owner of Torino F.C.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Italian born Michele Marcolini, previously of FC Bari 1908, is now the manager of A.C. Lumezzane, whist also the owner of Torino F.C.",
"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": "F.C._Bari_1908",
"property": "club",
"subject": "Michele_Marcolini"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Michele_Marcolini",
"property": "manager",
"subject": "A.C._Lumezzane"
},
{
"object": "Italy",
"property": "placeOfBirth",
"subject": "Michele_Marcolini"
},
{
"object": "Torino_F.C.",
"property": "team",
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},
{
"object": "F.C._Bari_1908",
"property": "clubs",
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}
],
[
{
"object": "Michele_Marcolini",
"property": "manager",
"subject": "A.C._Lumezzane"
},
{
"object": "Italy",
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},
{
"object": "Torino_F.C.",
"property": "clubs",
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},
{
"object": "F.C._Bari_1908",
"property": "team",
"subject": "Michele_Marcolini"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "4",
"xml_id": "Id3"
}
},
{
"667": {
"category": "SportsTeam",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Greece, led by Alexis Tsipras and Nikos Voutsis is the location of A.E. Dimitra Efxeinoupolis, where the language is greek.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Greece is the location of the A.E Dimitra Efxeinoupolis club. The language of the country is Greek and two of the leaders are Alexis Tsipras and Nikos Voutsis.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The A.E Dimitra Efxeinoupolis club is located in Greece where Alexis Tsipras heads Greece and Nikos Voutsis is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Greece",
"property": "location",
"subject": "A.E_Dimitra_Efxeinoupolis"
},
{
"object": "Alexis_Tsipras",
"property": "leader",
"subject": "Greece"
},
{
"object": "Nikos_Voutsis",
"property": "leader",
"subject": "Greece"
},
{
"object": "Greek_language",
"property": "language",
"subject": "Greece"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Greece",
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},
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],
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},
{
"674": {
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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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"property": "city",
"subject": "Antwerp_International_Airport"
},
{
"object": "\"12.0\"^^xsd:double",
"property": "elevation",
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},
{
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},
"shape": "NA",
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"xml_id": "Id18"
}
},
{
"730": {
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{
"comment": "good",
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},
{
"comment": "good",
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"lex": "Appleton International Airport can be found in Greenville, Wisconsin and serves the city of Appleton.It is situated 280 meters above sea level and has a 2,439 kilometers long runway called 3/21.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Appleton International Airport, which serves the city of Appleton is located in Greenville, Wisconsin. It is elevated 2801 meters above sea level. The airport's runway, which is named 3/21 measures a lenght of 2439 units.",
"xml_id": "Id3"
}
],
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},
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},
{
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"comment": "good",
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},
{
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"xml_id": "Id20"
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},
{
"732": {
"category": "Airport",
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{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport in New Zealand is operated by the Civil Aviation Authority and is 34 metres above sea level. It has a runway named 03R/21L which is 1,411 long and its third runway has a poaceae surface.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ardmore Airport (New Zealand) is operated by the Civil Aviation Authority of New Zealand. Its 3rd runway is made of Poaceae and its name is 03R/21L. It is 1411 m long and the airport is 34 m above sea level.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "The Civil Aviation Authority of New Zealand is the operating organisation for Ardmore Airport, New Zealand and is situated 34 meters above sea level.The airport's 3rd runway is surfaced with Poaceae and has a 1,411 meter long runway called \"03R/21L\".",
"xml_id": "Id3"
}
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{
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{
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},
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},
{
"733": {
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{
"comment": "good",
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"lex": "Turkmenistan Airlines operates Ashgabat International Airport in Ashgabat. The airport is 211 metres above sea level, has the runway length of 2989.0 and 12R/30L is the runway name.",
"xml_id": "Id1"
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{
"comment": "good",
"lang": "",
"lex": "Ashgabat International Airport is operated by Turkmenistan Airlines. The runway 12R/30L is 2989m long and is 211m a.s.l.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Ashgabat International Airport is located in Ashgabat and is operated byTurkmenistan Airlines. It is 211 metres above sea level and has a runway length of 2989.0. It has a runway named 12R/30L.",
"xml_id": "Id3"
}
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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": "Runway 4/22 at Atlantic City International Airport has an elevation of 23m above sea level and its length is 1873. It is operated by Port Authority of New York and New Jersey under the location Identifier of KACY.",
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"comment": "good",
"lang": "",
"lex": "The Port Authority of New York and New Jersey is the operating organisation of Atlantic City International airport, which lies 23.0 meters above sea level. The airport's ICAO Location Identifier is KACY. The 4/22 is the name of the runway in Atlantic City International Airport and it measures 1873.",
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},
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},
{
"735": {
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"comment": "good",
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},
{
"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves the city of Antwerp in Belgium, the country where the leader is Charles Michel. The Flemish Region can be found in Belgium and it is led by the Flemish Government who has jurisdiction over it.",
"xml_id": "Id2"
},
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"comment": "good",
"lang": "",
"lex": "Antwerp International Airport serves Antwerp. The Flemish region is in the country of Belgium and it is led by the Flemish Government. The jurisdiction of the Flemish Government is in the Flemish Region and the leader of Belgium is Charles Michel.",
"xml_id": "Id3"
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],
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"object": "Belgium",
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{
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"xml_id": "Id24"
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},
{
"736": {
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"comment": "good",
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"lex": "Adirondack Regional Airport serves the city of Lake Placid and Saranac Lake in New York. Saranac Lake is part of Harrietstown and Essex County in New York in the U.S.",
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"comment": "good",
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"lex": "Adirondack Regional Airport serves the cities of Lake Placid and Saranac Lake, New York,United States.Saranac Lake, New York is part of Harrietstown,Essex County,New York.",
"xml_id": "Id2"
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{
"comment": "good",
"lang": "",
"lex": "Adirondack Regional Airport serves the cities of Lake Placid and Saranac Lake in New York state,United States. Saranac Lake, New York is part of Harrietstown in Essex County,New York.",
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],
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},
"shape": "NA",
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},
{
"737": {
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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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"lex": "Baymax was created by American Duncan Rouleau and Steven T. Seagle. Baymax is a character in Big Hero 6 which stars Scott Adsit.",
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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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],
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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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"lex": "Ayam penyet is a hot food dish found in Java, Malaysia and Singapore and consists of fried chicken and sambal sauce.",
"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Ayam penyet is a food made in Java regions of Malaysia and Singapore. It contains fried chicken that is smashed and served hot with sambal.",
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}
],
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{
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},
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},
{
"comment": "good",
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"xml_id": "Id2"
},
{
"comment": "good",
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"lex": "Ayam penyet (origins: Indonesia) contains squeezed/smashed fried chicken with sambel and is popular in Malaysia. Malasysia is home to the Malaysian chinese.",
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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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},
{
"779": {
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{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion is a dish that originates in the United States, where English is the language spoken and the capital is Washington DC. Barack Obama is leader of the US and Native Americans are one of the ethnic groups of the country.",
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},
{
"comment": "good",
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"lex": "Barack Obama is leader of United States, the home of the dish Bacon Explosion. Native Americans are one of the ethnic groups of the country, where Washington DC is the capital and English is the language spoken.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bacon Explosion comes from the United States whose leader is Barack Obama. The capital of the United States is Washington, D.C., the spoken language is English and Native Americans are one of the ethnic groups of the country.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "United_States",
"property": "country",
"subject": "Bacon_Explosion"
},
{
"object": "Barack_Obama",
"property": "leaderName",
"subject": "United_States"
},
{
"object": "Native_Americans_in_the_United_States",
"property": "ethnicGroup",
"subject": "United_States"
},
{
"object": "Washington,_D.C.",
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},
{
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}
],
"originaltriplesets": {
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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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]
},
"shape": "NA",
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"xml_id": "Id10"
}
},
{
"780": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
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"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska comes from France where the spoken language is French,and the country's leader is Gerard Larcher. This dessert is also served in Hong Kong.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Baked Alaska, served in Hong Kong, who's political leader is Carrie Lam, but is actually from France where the national language is French and the leader is Gerard Larcher.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Carrie_Lam_(politician)",
"property": "leaderName",
"subject": "Hong_Kong"
},
{
"object": "Gérard_Larcher",
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},
{
"object": "French_language",
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},
{
"object": "Hong_Kong",
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"subject": "Baked_Alaska"
}
],
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"originaltripleset": [
[
{
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},
{
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},
{
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},
{
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},
{
"object": "Hong_Kong",
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}
],
[
{
"object": "France",
"property": "country",
"subject": "Baked_Alaska"
},
{
"object": "Carrie_Lam_(politician)",
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},
{
"object": "Gérard_Larcher",
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},
{
"object": "French_language",
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"subject": "France"
},
{
"object": "Hong_Kong",
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"subject": "Baked_Alaska"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
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"xml_id": "Id11"
}
},
{
"781": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bakewell Tart (or pudding) is from the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader. The tart is made with shortcrust pastry.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The shortcrust pastry-based dessert Bakewell Tart (or pudding) hails from the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "A variant of bakewell pudding is bakewell tart, which key ingredient is Shortcrust pastry. Bakewell pudding originates from the Derbyshire Dales, which leader is Patrick McLoughlin.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_pudding"
},
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
},
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Shortcrust_pastry",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Derbyshire_Dales",
"property": "region",
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},
{
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},
{
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},
{
"object": "Derbyshire",
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},
{
"object": "Shortcrust_pastry",
"property": "ingredient",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id12"
}
},
{
"782": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a dish from Colombian cuisine and originates from the Paisa region. One of the ingredients in Bandeja paisa is lemon which is a member of the family Rutaceae and is from the genus citrus.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is a traditional dish from the Paisa region, Colombia. Lemon is one of its ingredients and is a member of the family Rutaceae and is from the genus citrus.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Rutaceae",
"property": "family",
"subject": "Lemon"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Citrus",
"property": "genus",
"subject": "Lemon"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Lemon",
"property": "ingredient",
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},
{
"object": "Rutaceae",
"property": "family",
"subject": "Lemon"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
"property": "region",
"subject": "Bandeja_paisa"
},
{
"object": "Citrus",
"property": "genus",
"subject": "Lemon"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id13"
}
},
{
"783": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bandeja paisa is typical Colombian cuisine that originates from the Paisa region and contains lemon. The lemon belongs to the rutaceae family and the order of Rosids.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Lemon",
"property": "ingredient",
"subject": "Bandeja_paisa"
},
{
"object": "Rosids",
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"subject": "Lemon"
},
{
"object": "Rutaceae",
"property": "family",
"subject": "Lemon"
},
{
"object": "Colombian_cuisine",
"property": "country",
"subject": "Bandeja_paisa"
},
{
"object": "Paisa_Region",
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}
],
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{
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{
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},
{
"object": "Rutaceae",
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},
{
"object": "Colombian_cuisine",
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},
{
"object": "Paisa_Region",
"property": "region",
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}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id14"
}
},
{
"784": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Barny Cakes, which are made from sponge cake, are from France, where Gerard Larcher and Claude Bartolone are leaders and French is the language spoken.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes, made with sponge cake, are found in France, where French is spoken and whose leaders are Claude Bartolone and Gerard Larcher.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "Claude_Bartolone",
"property": "leaderName",
"subject": "France"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
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},
{
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},
{
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},
{
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},
{
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}
],
[
{
"object": "France",
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},
{
"object": "French_language",
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"subject": "France"
},
{
"object": "Gérard_Larcher",
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},
{
"object": "Claude_Bartolone",
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"subject": "France"
},
{
"object": "Sponge_cake",
"property": "ingredient",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id15"
}
},
{
"785": {
"category": "Food",
"dbpedialinks": [],
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{
"comment": "good",
"lang": "",
"lex": "Created by Mondelez International (founded in Chicago), Barny Cakes come from France where Gerard Larcher is leader and they speak French.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Barny cakes is created by Mondelez International, which was founded in Chicago. It comes from France, which leader is Gérard Larcher and the language is French.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "France",
"property": "country",
"subject": "Barny_Cakes"
},
{
"object": "Gérard_Larcher",
"property": "leaderName",
"subject": "France"
},
{
"object": "French_language",
"property": "language",
"subject": "France"
},
{
"object": "Chicago",
"property": "foundationPlace",
"subject": "Mondelez_International"
},
{
"object": "Mondelez_International",
"property": "creator",
"subject": "Barny_Cakes"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "France",
"property": "country",
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},
{
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},
{
"object": "French_language",
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},
{
"object": "Chicago",
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},
{
"object": "Mondelez_International",
"property": "creator",
"subject": "Barny_Cakes"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id16"
}
},
{
"786": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "English speaking Singapore, led by Tony Tan and Halimah Yacob, offers the dish Beef kway teow, also found in Indonesia.",
"xml_id": "Id1"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "\"Singapore and Indonesia\"",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leader",
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}
],
[
{
"object": "Singapore",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leader",
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},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "\"Singapore and Indonesia\"@en",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id17"
}
},
{
"787": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bhajji is also known as Bhaji or bajj, originates from the Karnataka region of India. It is usually made up of vegetables and gram flour.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji, also known as Bhaji or bajji, originates from India in the Karnataka region and the main ingredients are gram flour and vegetables.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji (Bhaji), or bajji, contains vegetables and gram flour; it originates from the Karnataka region of India.",
"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": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"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": "Vegetable",
"property": "ingredient",
"subject": "Bhajji"
}
],
[
{
"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"
},
{
"object": "\"Gram flour,vegetables\"",
"property": "ingredientName",
"subject": "Bhajji"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id18"
}
},
{
"788": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Indians is the name given to people from India and the leaders are Sumitra Mahajan and Vajubhai Vala. Bhajji is from the Karnataka region of India.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji is from the Karantake region of India. India is led by Vajubhai Vala and home to the Indian people.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bhajji originates from the Karnataka region (led by Vajubhai Vala) of India. The Indians of India are led by Sumitra Mahajan.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "India",
"property": "country",
"subject": "Bhajji"
},
{
"object": "Indian_people",
"property": "demonym",
"subject": "India"
},
{
"object": "Sumitra_Mahajan",
"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": "Sumitra_Mahajan",
"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": "Id19"
}
},
{
"789": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert from Jalisco, Mexio. The leader in Jalisco is Jesus Casillas Romero. Another dessert is a cake.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert found in the Jalisco region of Mexico. The name of the leader in Jalisco is Jesus Casillas Romero. Another dessert is a cake.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Jesús_Casillas_Romero",
"property": "leaderName",
"subject": "Jalisco"
},
{
"object": "Cake",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Jalisco",
"property": "region",
"subject": "Bionico"
},
{
"object": "Jesús_Casillas_Romero",
"property": "leaderName",
"subject": "Jalisco"
},
{
"object": "Cake",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id20"
}
},
{
"790": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "A type of dessert is a cake. However, bionico is not a cake, and instead a dessert from the Guadalajara region, in Mexico. Mexico is where Enrique Pena Nieto is the leader.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bionico is a dessert from Guadalajara, Mexico where the leader is Enrique Pena Nieto. Another dessert is a cake.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Cake",
"property": "dishVariation",
"subject": "Dessert"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Mexico",
"property": "country",
"subject": "Bionico"
},
{
"object": "Cake",
"property": "hasVariant",
"subject": "Dessert"
},
{
"object": "Enrique_Peña_Nieto",
"property": "leaderName",
"subject": "Mexico"
},
{
"object": "Dessert",
"property": "course",
"subject": "Bionico"
},
{
"object": "Guadalajara",
"property": "region",
"subject": "Bionico"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id21"
}
},
{
"791": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is the leader of Derbyshire Dales which is in the County of Derbyshire. Bakewell tart, a variation of Bakewell pudding, comes from the Derbyshire Dales and contains frangipane.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is a leader in the Derbyshire Dales (in the county of Derbyshire) where the Bakewell tart is a popular dish. This dish contains frangipane and has a variant called Bakewell pudding.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is a leader in Derbyshire Dales in the County of Derbyshire The frangipane Bakewell tart, a variation of Bakewell pudding, is popular in the Derbyshire Dales area.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
},
{
"object": "Bakewell_tart",
"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "administrativeCounty",
"subject": "Derbyshire_Dales"
},
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
},
{
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"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "subdivisionName",
"subject": "Derbyshire_Dales"
},
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id22"
}
},
{
"792": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Patrick McLoughlin is the leader of Derbyshire Dales which is part of Derbyshire. Coming from the Derbyshire Dales region is Bakewell tart (a variation of Bakewell pudding) which has Frangipane as an ingredient.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Derbyshire Dales, whose leader is Patrick McLoughlin, is part of Derbyshire and is where Bakewell tarts come from. Bakewell tart is a variation of Bakewell pudding and one of the ingredients is frangipane.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The Frangipane -based dessert Bakewell Tart (or pudding) is popular in the Derbyshire Dales area of Derbyshire where Patrick McLoughlin is a leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
},
{
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"property": "dishVariation",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Patrick_McLoughlin",
"property": "leaderName",
"subject": "Derbyshire_Dales"
},
{
"object": "Bakewell_tart",
"property": "hasVariant",
"subject": "Bakewell_pudding"
},
{
"object": "Derbyshire_Dales",
"property": "region",
"subject": "Bakewell_tart"
},
{
"object": "Derbyshire",
"property": "isPartOf",
"subject": "Derbyshire_Dales"
},
{
"object": "Frangipane",
"property": "ingredient",
"subject": "Bakewell_tart"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id23"
}
},
{
"793": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Jakarta is the capital of Indonesia where the leader is Jusuf Kalla, they speak Indonesian and eat bakso.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a dish from the country of Indonesia, which leader is Jusuf Kalla, the capital is Jakarta and the language is Indonesian.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Bakso is a food and a dish found in Indonesia, where the capital is Jakarta, the language is Indonesia and Jusuf Kalla is the leader.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "Indonesia",
"property": "country",
"subject": "Bakso"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Jakarta",
"property": "capital",
"subject": "Indonesia"
},
{
"object": "Indonesian_language",
"property": "language",
"subject": "Indonesia"
},
{
"object": "Jusuf_Kalla",
"property": "leaderName",
"subject": "Indonesia"
},
{
"object": "Indonesia",
"property": "region",
"subject": "Bakso"
},
{
"object": "\"Indonesia, derived from Chinese meat ball\"@en",
"property": "country",
"subject": "Bakso"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id24"
}
},
{
"794": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Italy is the country Amatriciana sauce comes from and people there are called Italians. The capital is Rome and the leaders are Matteo Renzi and Sergio Mattarella.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Rome is the capital of Italy where the leaders are Matteo Renzi and Sergio Mattarella. Amatriciana sauce can be found there and people are called Italians.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Amatriciana sauce is from Italy, where Rome is the capital and where Italians live. Leaders of the country include Sergio Mattarella and Matteo Renzi.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leaderName",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leader",
"subject": "Italy"
}
],
[
{
"object": "Italians",
"property": "demonym",
"subject": "Italy"
},
{
"object": "Rome",
"property": "capital",
"subject": "Italy"
},
{
"object": "Matteo_Renzi",
"property": "leader",
"subject": "Italy"
},
{
"object": "Italy",
"property": "country",
"subject": "Amatriciana_sauce"
},
{
"object": "Sergio_Mattarella",
"property": "leaderName",
"subject": "Italy"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id25"
}
},
{
"795": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Halimah Yacob is the leader in Singapore where English is spoken and the dish Ayam penyet is from. It is also from Java, a country where one of the ethnic groups are the Baduy.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baduy is an ethnic group in Java where Ayam penyet is from. This dish is also from Singapore where English is spoken and Halimah Yacob is the leader.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Ayam penyet originates from Java where Baduy people are one of the ethnic groups.The food can also be found in Singapore where English is mainly spoken and Halimah Yacob is the president.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baduy",
"property": "ethnicGroup",
"subject": "Java"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "region",
"subject": "Ayam_penyet"
},
{
"object": "Java",
"property": "country",
"subject": "Ayam_penyet"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id26"
}
},
{
"796": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Philippines is the country the dish Batchoy comes from. The llocano and Igorot peoples are ethnic groups from the Philippines, where languages spoken include Arabic and Phillippine English.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Batchoy is eaten in the Philippines, languages spoken there include English (official language) and also Arabic. Ethnic groups there include the Ilocano and Igorot peoples.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Ilocano_people",
"property": "ethnicGroup",
"subject": "Philippines"
},
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippine_English",
"property": "officialLanguage",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Ilocano_people",
"property": "ethnicGroups",
"subject": "Philippines"
},
{
"object": "Arabic",
"property": "language",
"subject": "Philippines"
},
{
"object": "Philippine_English",
"property": "officialLanguage",
"subject": "Philippines"
},
{
"object": "Philippines",
"property": "country",
"subject": "Batchoy"
},
{
"object": "Igorot_people",
"property": "ethnicGroup",
"subject": "Philippines"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id27"
}
},
{
"797": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Available nationwide in Indonesia and Singapore, Beef kway teow is a dish which comes from Singapore, where English is spoken. It is also where two of the leaders are Halimah Yacob and Tony Tan.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Just like in Indonesia, Beef kway teow is popular nationwide is Singapore (where it comes from). In this country, English is spoken and two of the leaders are Halimah Yacob and Tony Tan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Beef kway teow is a popular dish in Singapore and Indonesia. Tony Tan and Halimah Yacob are leaders in Singapore where English is spoken.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"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"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Singapore",
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"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"@en",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Tony_Tan",
"property": "leaderName",
"subject": "Singapore"
},
{
"object": "Halimah_Yacob",
"property": "leader",
"subject": "Singapore"
}
],
[
{
"object": "Tony_Tan",
"property": "leader",
"subject": "Singapore"
},
{
"object": "English_language",
"property": "language",
"subject": "Singapore"
},
{
"object": "Singapore",
"property": "country",
"subject": "Beef_kway_teow"
},
{
"object": "\"Nationwide in Singapore and Indonesia\"@en",
"property": "region",
"subject": "Beef_kway_teow"
},
{
"object": "Halimah_Yacob",
"property": "leaderName",
"subject": "Singapore"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id28"
}
},
{
"798": {
"category": "Food",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is a dish from Spain, where the currency is the euro, the leader is Felipe VI, the language spoken is Spanish and the people that live there are called Spaniards.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Ajoblanco is a food found in Spanish speaking and Felipe VI led Spain. It is also home to the Spaniards and can be purchased with the Euro.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Felipe_VI_of_Spain",
"property": "leaderName",
"subject": "Spain"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Spain"
},
{
"object": "Euro",
"property": "currency",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Spaniards",
"property": "demonym",
"subject": "Spain"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Felipe_VI_of_Spain",
"property": "leader",
"subject": "Spain"
},
{
"object": "Spanish_language",
"property": "language",
"subject": "Spain"
},
{
"object": "Euro",
"property": "currency",
"subject": "Spain"
},
{
"object": "Spain",
"property": "country",
"subject": "Ajoblanco"
},
{
"object": "Spaniards",
"property": "demonym",
"subject": "Spain"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id29"
}
},
{
"799": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry monument is located in the municipality of Gettysburg, Adams County, Pennsylvania. Adams County is east of Franklin County and south of Cumberland County. Maryland's Carrol county is located to the southeast.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry Monument is placed in the municipality of Gettysburg, Adams County, Pennsylvania. To the west of Adams County lies Franklin County and to the north, Cumberland County, both of these are in Pennsylvania. Carrol County Maryland is southeast of Adams County.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 11th Mississippi Infantry monument is located in Gettysburg, Adams County, Pennsylvania. Adams County is located east of Franklin County and south of Cumberland County. Maryland's Carrol County is located to the southeast.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Franklin_County,_Pennsylvania",
"property": "has to its west",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Carroll_County,_Maryland",
"property": "has to its southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Cumberland_County,_Pennsylvania",
"property": "has to its north",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Gettysburg,_Pennsylvania",
"property": "municipality",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "location",
"subject": "11th_Mississippi_Infantry_Monument"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Franklin_County,_Pennsylvania",
"property": "west",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Carroll_County,_Maryland",
"property": "southeast",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Cumberland_County,_Pennsylvania",
"property": "north",
"subject": "Adams_County,_Pennsylvania"
},
{
"object": "Gettysburg,_Pennsylvania",
"property": "municipality",
"subject": "11th_Mississippi_Infantry_Monument"
},
{
"object": "Adams_County,_Pennsylvania",
"property": "region",
"subject": "11th_Mississippi_Infantry_Monument"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id1"
}
},
{
"800": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The capital of Azerbaijan is Baku and the leader and Prime Minister of Azerbaijan is called Artur Rasizade. The Baku Turkish Martyr's Memorial is located in Azerbaijan and the legislature is known as the National Assembly.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "Prime Minister of Azerbaijan (capital Baku) is the official title of the leader whose name is Artur Rasizade. Baku Turkish Martyrs' Memorial is located in Azerbaijan and the legislature of the country is the National Assembly.",
"xml_id": "Id2"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"object": "Prime_Minister_of_Azerbaijan",
"property": "leaderTitle",
"subject": "Azerbaijan"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
},
{
"object": "National_Assembly_(Azerbaijan)",
"property": "legislature",
"subject": "Azerbaijan"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Baku",
"property": "capital",
"subject": "Azerbaijan"
},
{
"object": "Prime_Minister_of_Azerbaijan",
"property": "leaderTitle",
"subject": "Azerbaijan"
},
{
"object": "Azerbaijan",
"property": "location",
"subject": "Baku_Turkish_Martyrs'_Memorial"
},
{
"object": "Artur_Rasizade",
"property": "leaderName",
"subject": "Azerbaijan"
},
{
"object": "National_Assembly_(Azerbaijan)",
"property": "legislature",
"subject": "Azerbaijan"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "5",
"xml_id": "Id2"
}
},
{
"801": {
"category": "Monument",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial in Azerbaijan is dedicated to the Ottoman Army Soldiers killed in the Battle of Baku and was designed by Huseyin Butuner and Hilmi Guner. The monument is also known by the native name Türk Şehitleri Anıtı. Azerbaijan's leader is Artur Rasizade.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Baku Turkish Martyrs' Memorial ( native name : \"Türk Sehitleri Aniti\") in Azerbaijan has been dedicated to Ottoman Army soldiers killed in the Battle of Baku and it was designed by Hüseyin Bütüner and Hilmi Güner.Artur Rasizade is the leader of Azerbaijan.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
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},
"shape": "NA",
"shape_type": "NA",
"size": "7",
"xml_id": "Id5"
}
},
{
"871": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "1 Decembrie 1918 University is located in the city of Alba Iulia, Romania. The country's capital is Bucharest and the leader (title Prime Minister of Romania) is Klaus Iohannis. An ethnic group of the country is The Germans of Romania and the country's national anthem is Deșteaptă-te, române!",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The national anthem of Romania is \"Desteapta-te, romane!\" and the capital city is Bucharest. The country is lead by Prime Minister Klaus Iohannis and is the location of the 1 Decembrie 1918 University in the city of Alba Iulia. One of the ethnic groups of the country is the Germans of Romania.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "The 1 Decembrie 1918 University is located in Alba Iulia, Romania. Romania's capital is Bucharest; its leader is Prime Minister Klaus Iohannis and its ethnic group is the Germans of Romania. Romania's capital is Bucharest and its anthem is Desteapta-te, Romane!",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Germans_of_Romania",
"property": "ethnicGroup",
"subject": "Romania"
},
{
"object": "Prime_Minister_of_Romania",
"property": "leaderTitle",
"subject": "Romania"
},
{
"object": "Klaus_Iohannis",
"property": "leaderName",
"subject": "Romania"
},
{
"object": "Bucharest",
"property": "capital",
"subject": "Romania"
},
{
"object": "Alba_Iulia",
"property": "city",
"subject": "1_Decembrie_1918_University"
},
{
"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": "Prime_Minister_of_Romania",
"property": "leaderTitle",
"subject": "Romania"
},
{
"object": "Klaus_Iohannis",
"property": "leaderName",
"subject": "Romania"
},
{
"object": "Bucharest",
"property": "capital",
"subject": "Romania"
},
{
"object": "Alba_Iulia",
"property": "city",
"subject": "1_Decembrie_1918_University,_Alba_Iulia"
},
{
"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": "7",
"xml_id": "Id6"
}
},
{
"872": {
"category": "University",
"dbpedialinks": [],
"lexicalisations": [
{
"comment": "good",
"lang": "",
"lex": "In the year 2000 the Acharya Institute of Technology was established in the state of Karnataka, India. The current location is \"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090.\" The Director is Dr G P Prabhukumar and it is affiliated with the Visvesvaraya Technological University in the city of Belgaum.",
"xml_id": "Id1"
},
{
"comment": "good",
"lang": "",
"lex": "The Acharya Institute of Technology's campus is located in Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore, Karnatka, 560090. It was established in 2000 and its director is Dr G P Prabhukumar. It is affiliated to the Visvesvaraya Technological University in Belgaum.",
"xml_id": "Id2"
},
{
"comment": "good",
"lang": "",
"lex": "Acharya Institute of Technology is affiliated with Visvesvaraya Technological University which is in Belgium. The institute itself is in India's Karnataka state and its full address is In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090. It was created in 2000 and its director is Dr. G. P. Prabhukumar.",
"xml_id": "Id3"
}
],
"links": [],
"modifiedtripleset": [
{
"object": "Belgaum",
"property": "city",
"subject": "Visvesvaraya_Technological_University"
},
{
"object": "\"Dr. G. P. Prabhukumar\"",
"property": "director",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "2000",
"property": "established",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Karnataka",
"property": "state",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"India\"",
"property": "country",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"In Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090.\"",
"property": "campus",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Visvesvaraya_Technological_University",
"property": "affiliation",
"subject": "Acharya_Institute_of_Technology"
}
],
"originaltriplesets": {
"originaltripleset": [
[
{
"object": "Belgaum",
"property": "city",
"subject": "Visvesvaraya_Technological_University"
},
{
"object": "\"Dr. G. P. Prabhukumar\"@en",
"property": "director",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "2000",
"property": "established",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Karnataka",
"property": "state",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"India\"@en",
"property": "country",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "\"Urban, ,\\nIn Soldevanahalli, Acharya Dr. Sarvapalli Radhakrishnan Road, Hessarghatta Main Road, Bangalore – 560090.\"@en",
"property": "campus",
"subject": "Acharya_Institute_of_Technology"
},
{
"object": "Visvesvaraya_Technological_University",
"property": "affiliations",
"subject": "Acharya_Institute_of_Technology"
}
]
]
},
"shape": "NA",
"shape_type": "NA",
"size": "7",
"xml_id": "Id7"
}
}
]
}
|
AdaMix/NLG/data/webnlg_challenge_2017/dev.json/0
|
{
"file_path": "AdaMix/NLG/data/webnlg_challenge_2017/dev.json",
"repo_id": "AdaMix",
"token_count": 1810930
}
| 22 |
. ./venv/bin/activate
sudo apt install default-jre -y
seed=110
n_experts=8
vv=lora_adamix
while [[ $# -gt 0 ]]
do
key="$1"
case $key in
--seed)
seed=$2
shift
shift
;;
--n_experts)
n_experts=$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 \
--n_experts $n_experts \
--share_A 0 \
--share_B 1
python src/gpt2_decode.py \
--vocab ./vocab \
--sample_file ./trained_models/GPT2_M/webnlg/$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.sh/0
|
{
"file_path": "AdaMix/NLG/run_eval_webnlg.sh",
"repo_id": "AdaMix",
"token_count": 718
}
| 23 |
# ------------------------------------------------------------------------------------------
# Copyright (c). All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import sys
import io
import json
with open(sys.argv[1], 'r', encoding='utf8') as reader, \
open(sys.argv[2], 'w', encoding='utf8') as writer :
lines_dict = json.load(reader)
full_rela_lst = []
full_src_lst = []
full_tgt_lst = []
full_cate_lst = []
seen = [
'Airport',
'Astronaut',
'Building',
'City',
'ComicsCharacter',
'Food',
'Monument',
'SportsTeam',
'University',
'WrittenWork'
]
cate_dict = {}
for i, example in enumerate(lines_dict['entries']):
sents = example[str(i+1)]['lexicalisations']
triples = example[str(i + 1)]['modifiedtripleset']
cate = example[str(i + 1)]['category']
if not cate in cate_dict:
cate_dict[cate] = 0
cate_dict[cate] += 1
rela_lst = []
temp_triples = ''
for i, tripleset in enumerate(triples):
subj, rela, obj = tripleset['subject'], tripleset['property'], tripleset['object']
rela_lst.append(rela)
if i > 0:
temp_triples += ' | '
temp_triples += '{} : {} : {}'.format(subj, rela, obj)
for sent in sents:
if sent["comment"] == 'good':
full_tgt_lst.append(sent['lex'])
full_src_lst.append(temp_triples)
full_rela_lst.append(rela_lst)
full_cate_lst.append(cate)
for cate in cate_dict:
print('cate', cate, cate_dict[cate])
#edited_sents = []
for src, tgt, cate in zip(full_src_lst, full_tgt_lst, full_cate_lst):
x = {}
x['context'] = src # context #+ '||'
x['completion'] = tgt #completion
x['cate'] = cate in seen
writer.write(json.dumps(x)+'\n')
|
AdaMix/NLG/src/format_converting_webnlg.py/0
|
{
"file_path": "AdaMix/NLG/src/format_converting_webnlg.py",
"repo_id": "AdaMix",
"token_count": 994
}
| 24 |
export num_gpus=1
export CUBLAS_WORKSPACE_CONFIG=":16:8" # https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
export PYTHONHASHSEED=0
task_name=mnli
model=bert-base-uncased
export output_dir="./models/${model}/${task_name}"
python -m torch.distributed.launch --nproc_per_node=$num_gpus \
examples/text-classification/run_glue.py \
--model_name_or_path $model \
--task_name $task_name \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 64 \
--per_device_eval_batch_size 32 \
--learning_rate 4e-4 \
--num_train_epochs 40 \
--output_dir $output_dir/model \
--overwrite_output_dir \
--logging_steps 1000 \
--logging_dir $output_dir/log \
--evaluation_strategy epoch \
--save_strategy epoch \
--warmup_ratio 0.06 \
--apply_expert_soup \
--adapter_size 48 \
--num_experts 4 \
--seed 0 \
--inference_level 3 \
--load_best_model_at_end \
--metric_for_best_model "accuracy" \
--sharing_up 1 \
--sharing_down 0 \
--weight_decay 0.1 \
--use_consistency_loss 1
|
AdaMix/bert_base_mnli_expert_soup.sh/0
|
{
"file_path": "AdaMix/bert_base_mnli_expert_soup.sh",
"repo_id": "AdaMix",
"token_count": 403
}
| 25 |
..
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.
Utilities for Generation
-----------------------------------------------------------------------------------------------------------------------
This page lists all the utility functions used by :meth:`~transformers.PreTrainedModel.generate`,
:meth:`~transformers.PreTrainedModel.greedy_search`, :meth:`~transformers.PreTrainedModel.sample`,
:meth:`~transformers.PreTrainedModel.beam_search`, :meth:`~transformers.PreTrainedModel.beam_sample`, and
:meth:`~transformers.PreTrainedModel.group_beam_search`.
Most of those are only useful if you are studying the code of the generate methods in the library.
Generate Outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The output of :meth:`~transformers.PreTrainedModel.generate` is an instance of a subclass of
:class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
by :meth:`~transformers.PreTrainedModel.generate`, but that can also be used as tuple or dictionary.
Here's an example:
.. code-block::
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
The ``generation_output`` object is a :class:`~transformers.generation_utils.GreedySearchDecoderOnlyOutput`, as we can
see in the documentation of that class below, it means it has the following attributes:
- ``sequences``: the generated sequences of tokens
- ``scores`` (optional): the prediction scores of the language modelling head, for each generation step
- ``hidden_states`` (optional): the hidden states of the model, for each generation step
- ``attentions`` (optional): the attention weights of the model, for each generation step
Here we have the ``scores`` since we passed along ``output_scores=True``, but we don't have ``hidden_states`` and
``attentions`` because we didn't pass ``output_hidden_states=True`` or ``output_attentions=True``.
You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get ``None``. Here for instance ``generation_output.scores`` are all the generated prediction scores of the
language modeling head, and ``generation_output.attentions`` is ``None``.
When using our ``generation_output`` object as a tuple, it only keeps the attributes that don't have ``None`` values.
Here, for instance, it has two elements, ``loss`` then ``logits``, so
.. code-block::
generation_output[:2]
will return the tuple ``(generation_output.sequences, generation_output.scores)`` for instance.
When using our ``generation_output`` object as a dictionary, it only keeps the attributes that don't have ``None``
values. Here, for instance, it has two keys that are ``sequences`` and ``scores``.
We document here all output types.
GreedySearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.GreedySearchDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.GreedySearchEncoderDecoderOutput
:members:
SampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.SampleDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
:members:
BeamSearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.BeamSearchDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.BeamSearchEncoderDecoderOutput
:members:
BeamSampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.generation_utils.BeamSampleDecoderOnlyOutput
:members:
.. autoclass:: transformers.generation_utils.BeamSampleEncoderDecoderOutput
:members:
LogitsProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A :class:`~transformers.LogitsProcessor` can be used to modify the prediction scores of a language model head for
generation.
.. autoclass:: transformers.LogitsProcessor
:members: __call__
.. autoclass:: transformers.LogitsProcessorList
:members: __call__
.. autoclass:: transformers.LogitsWarper
:members: __call__
.. autoclass:: transformers.MinLengthLogitsProcessor
:members: __call__
.. autoclass:: transformers.TemperatureLogitsWarper
:members: __call__
.. autoclass:: transformers.RepetitionPenaltyLogitsProcessor
:members: __call__
.. autoclass:: transformers.TopPLogitsWarper
:members: __call__
.. autoclass:: transformers.TopKLogitsWarper
:members: __call__
.. autoclass:: transformers.NoRepeatNGramLogitsProcessor
:members: __call__
.. autoclass:: transformers.NoBadWordsLogitsProcessor
:members: __call__
.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
:members: __call__
.. autoclass:: transformers.HammingDiversityLogitsProcessor
:members: __call__
StoppingCriteria
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A :class:`~transformers.StoppingCriteria` can be used to change when to stop generation (other than EOS token).
.. autoclass:: transformers.StoppingCriteria
:members: __call__
.. autoclass:: transformers.StoppingCriteriaList
:members: __call__
.. autoclass:: transformers.MaxLengthCriteria
:members: __call__
.. autoclass:: transformers.MaxTimeCriteria
:members: __call__
BeamSearch
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.BeamScorer
:members: process, finalize
.. autoclass:: transformers.BeamSearchScorer
:members: process, finalize
Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: transformers.top_k_top_p_filtering
.. autofunction:: transformers.tf_top_k_top_p_filtering
|
AdaMix/docs/source/internal/generation_utils.rst/0
|
{
"file_path": "AdaMix/docs/source/internal/generation_utils.rst",
"repo_id": "AdaMix",
"token_count": 1987
}
| 26 |
<!---
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.
-->
# Migrating from previous packages
## Migrating from transformers `v3.x` to `v4.x`
A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the
expected changes:
#### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.
The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set.
This introduces two breaking changes:
- The handling of overflowing tokens between the python and rust tokenizers is different.
- The rust tokenizers do not accept integers in the encoding methods.
##### How to obtain the same behavior as v3.x in v4.x
- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
- The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:
In version `v3.x`:
```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
```
to obtain the same in version `v4.x`:
```py
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
```
#### 2. SentencePiece is removed from the required dependencies
The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation.
This includes the **slow** versions of:
- `XLNetTokenizer`
- `AlbertTokenizer`
- `CamembertTokenizer`
- `MBartTokenizer`
- `PegasusTokenizer`
- `T5Tokenizer`
- `ReformerTokenizer`
- `XLMRobertaTokenizer`
##### How to obtain the same behavior as v3.x in v4.x
In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally:
In version `v3.x`:
```bash
pip install transformers
```
to obtain the same in version `v4.x`:
```bash
pip install transformers[sentencepiece]
```
or
```bash
pip install transformers sentencepiece
```
#### 3. The architecture of the repo has been updated so that each model resides in its folder
The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories.
This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path.
##### How to obtain the same behavior as v3.x in v4.x
In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers.
In version `v3.x`:
```bash
from transformers.modeling_bert import BertLayer
```
to obtain the same in version `v4.x`:
```bash
from transformers.models.bert.modeling_bert import BertLayer
```
#### 4. Switching the `return_dict` argument to `True` by default
The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.
##### How to obtain the same behavior as v3.x in v4.x
In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass.
In version `v3.x`:
```bash
model = BertModel.from_pretrained("bert-base-cased")
outputs = model(**inputs)
```
to obtain the same in version `v4.x`:
```bash
model = BertModel.from_pretrained("bert-base-cased")
outputs = model(**inputs, return_dict=False)
```
or
```bash
model = BertModel.from_pretrained("bert-base-cased", return_dict=False)
outputs = model(**inputs)
```
#### 5. Removed some deprecated attributes
Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604).
Here is a list of these attributes/methods/arguments and what their replacements should be:
In several models, the labels become consistent with the other models:
- `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`.
- `lm_labels` becomes `labels` in `BartForConditionalGeneration`.
- `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`.
- `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`.
- `lm_labels` becomes `labels` in `T5ForConditionalGeneration`.
In several models, the caching mechanism becomes consistent with the other models:
- `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `past` becomes `past_key_values` in all CTRL models.
- `past` becomes `past_key_values` in all GPT-2 models.
Regarding the tokenizer classes:
- The tokenizer attribute `max_len` becomes `model_max_length`.
- The tokenizer attribute `return_lengths` becomes `return_length`.
- The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`.
Regarding the `Trainer` class:
- The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`.
- The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` attribute `data_collator` should be a callable.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `Trainer` method `_training_step` is deprecated in favor of `training_step`.
- The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`.
- The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`.
Regarding the `TFTrainer` class:
- The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
- The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
Regarding the `TrainerArgument` class:
- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
Regarding the Transfo-XL model:
- The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.
- The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`.
Regarding pipelines:
- The `FillMaskPipeline` argument `topk` becomes `top_k`.
## Migrating from pytorch-transformers to 🤗 Transformers
Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
## Migrating from pytorch-pretrained-bert
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to 🤗 Transformers
### Models always output `tuples`
The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
Here is a `pytorch-pretrained-bert` to 🤗 Transformers conversion example for a `BertForSequenceClassification` classification model:
```python
# Let's load our model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
# If you used to have this line in pytorch-pretrained-bert:
loss = model(input_ids, labels=labels)
# Now just use this line in 🤗 Transformers to extract the loss from the output tuple:
outputs = model(input_ids, labels=labels)
loss = outputs[0]
# In 🤗 Transformers you can also have access to the logits:
loss, logits = outputs[:2]
# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
outputs = model(input_ids, labels=labels)
loss, logits, attentions = outputs
```
### Serialization
Breaking change in the `from_pretrained()`method:
1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
2. The additional `*inputs` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous `BertForSequenceClassification` examples. More precisely, the positional arguments `*inputs` provided to `from_pretrained()` are directly forwarded the model `__init__()` method while the keyword arguments `**kwargs` (i) which match configuration class attributes are used to update said attributes (ii) which don't match any configuration class attributes are forwarded to the model `__init__()` method.
Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
Here is an example:
```python
### Let's load a model and tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
### Do some stuff to our model and tokenizer
# Ex: add new tokens to the vocabulary and embeddings of our model
tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
model.resize_token_embeddings(len(tokenizer))
# Train our model
train(model)
### Now let's save our model and tokenizer to a directory
model.save_pretrained('./my_saved_model_directory/')
tokenizer.save_pretrained('./my_saved_model_directory/')
### Reload the model and the tokenizer
model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
```
### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences:
- it only implements weights decay correction,
- schedules are now externals (see below),
- gradient clipping is now also external (see below).
The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.
The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule:
```python
# Parameters:
lr = 1e-3
max_grad_norm = 1.0
num_training_steps = 1000
num_warmup_steps = 100
warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
### Previously BertAdam optimizer was instantiated like this:
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
### and used like this:
for batch in train_data:
loss = model(batch)
loss.backward()
optimizer.step()
### In 🤗 Transformers, optimizer and schedules are split and instantiated like this:
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
### and used like this:
for batch in train_data:
loss = model(batch)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
optimizer.step()
scheduler.step()
```
|
AdaMix/docs/source/migration.md/0
|
{
"file_path": "AdaMix/docs/source/migration.md",
"repo_id": "AdaMix",
"token_count": 4313
}
| 27 |
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