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" WENT_TO",
" STUDIED_AT.",
" STUDIED_FOR\n\n Explanation:\n Josh studied Civil Engineering",
" MET",
" WENT_TO\n or\n ATTENDED\n or\n ST",
" GRADUATED_FROM\n\n Explanation:\n Josh went to K",
" HAS_CHILDREN",
" SERVES",
" SELF_REFERENCE",
" SERVES.\n\n Explanation:\n Josh currently serves as the M",
" SELF_REFERENCE",
" ATTENDED_BY.",
" ATTENDED_BY\n\n Entities: \n [{'head':",
" STUDIED_AT.",
" MET_AT.\n This indicates that Josh met MJ at KSU.",
" ATTENDED\n\n Explanation:\n Josh attended KSU from ",
" STUDIED_FOR_DEGREE",
" CHILD_OF",
" ATTENDED_BY\n\n Explanation:\n Josh attended KSU",
" ATTENDED_BY\n\n Explanation:\n Josh attended KSU",
" SERVES.\n\n Explanation:\n Josh currently serves as the M",
" ATTENDED_BY\n\n Explanation:\n Josh attended KSU",
" STUDIED_AT.",
" ATTENDED_BY\n\n Explanation:\n Josh attended KSU",
" STUDIED_AT.",
" MET_AT.",
" ATTENDED\n\n Explanation:\n Josh attended Midwestern Baptist",
" STUDIED_AT.",
" STUDIED_AT",
" SERVES.\n\n Explanation:\n Josh currently serves as the M",
" STUDIED_AT.",
" STUDIED_AT.",
" STUDIED_AT",
" STUDIED_AT.",
" MET_SPouse",
" STUDIED_AT.",
" STUDIED_FOR\n\n Explanation:\n Josh studied Civil Engineering",
" HAS_CHILDREN",
" STUDIED_AT.",
" STUDIED_AT.",
" SERVES.\n\n Explanation:\n Josh currently serves as the M",
" STUDIED_AT.",
" MET",
" GOES_TO",
" MARRIED_DURING",
" MET_IN_ORGANIZATION",
" MET_AT\n\n Explanation:\n The text states that MJ",
" MET_AT\n\n Explanation:\n MJ met Josh at K",
" MARRIED_WITH",
" MET",
" MET",
" SERVES.\n\n Explanation:\n MJ is the head entity",
" MET",
" GO_TO_SCHOOL",
" WENT_TO",
" STUDIED_AT.",
" GOES_TO\n\n Explanation:\n The text states that Josh went",
" MET.",
" GRADUATED_FROM",
" GOES_TO\n\n Explanation:\n The text states that Josh went",
" GO_TO_SCHOOL",
" GO_TO_SCHOOL",
" SERVES.\n\n Explanation:\n Josh currently serves as the M",
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" EDUCATION",
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" STUDIED_FOR_DEGREE",
" MET_AT_LOCATION",
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" EDUCATED_BY",
" CHILDREN_OF",
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" MET_IN_ORGANIZATION",
" HAS_CHILDREN",
" GOES_TO\n\n Explanation:\n Josh went to Midwestern Bapt",
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" CHILDREN_OF",
" SELF_REFERENCE",
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" STUDIED_AT.",
" STUDIED_FOR\n\n Explanation:\n Josh studied Civil Engineering",
" MET",
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" OBTAINED_DEGREE",
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" SELF_REFERENCE",
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" STUDIED_FOR\n\n Or, if no relation exists:\n\n ",
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" GRADUATED_FROM\n\n Explanation:\n Josh is the head",
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" OBTAINED_DEGREE",
" HAS_CHILDREN",
" SELF_REFERENCE",
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] | [
[
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],
[
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],
[
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"2006-2011"
],
[
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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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],
[
"65",
"66",
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"CBC"
],
[
"70",
"71",
"PERSON",
"Josh"
]
] | 701 |
703 | [
"News and other tidbits that Chad Cloman finds interesting enough to share\nThis site has a few jigsaw puzzles where the image is a moving video. It’s fun. You’ll need to have Macromedia Shockwave Player installed in order for it to work.\nPosted in Cool Stuff |\nMail (will not be published)\nHTML: You can use these tags.\nChad ClomanPort Orange, FLUSA\nAll content copyright © 2005-2024 by Chad Cloman"
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['News and other tidbits that Chad Cloman finds interesting enough to share\nThis site has a few jigsaw puzzles where the image is a moving video. It’s fun. You’ll need to have Macromedia Shockwave Player installed in order for it to work.\nPosted in Cool Stuff |\nMail (will not be published)\nHTML: You can use these tags.\nChad ClomanPort Orange, FLUSA\nAll content copyright © 2005-2024 by Chad Cloman']
Entities:
[{'head': ['5', '7', 'PERSON', 'Chad Cloman'], 'tail': ['37', '40', 'PRODUCT', 'Macromedia Shockwave Player']}]
Relation:
| [
[
{
"head": [
"5",
"7",
"PERSON",
"Chad Cloman"
],
"tail": [
"37",
"40",
"PRODUCT",
"Macromedia Shockwave Player"
]
}
],
[
{
"head": [
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],
"tail": [
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]
}
],
[
{
"head": [
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],
"tail": [
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}
],
[
{
"head": [
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],
"tail": [
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}
],
[
{
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],
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}
],
[
{
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],
"tail": [
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}
],
[
{
"head": [
"37",
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],
"tail": [
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]
}
],
[
{
"head": [
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],
"tail": [
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]
}
],
[
{
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],
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}
],
[
{
"head": [
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"74",
"PERSON",
"Chad ClomanPort"
],
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]
}
],
[
{
"head": [
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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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}
],
[
{
"head": [
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"DATE",
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],
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"PERSON",
"Chad Cloman"
]
}
],
[
{
"head": [
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"PERSON",
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],
"tail": [
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"PERSON",
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]
}
],
[
{
"head": [
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],
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}
],
[
{
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"88",
"PERSON",
"Chad Cloman"
],
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]
}
],
[
{
"head": [
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],
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]
}
],
[
{
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"88",
"PERSON",
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],
"tail": [
"82",
"85",
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}
]
] | [
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" SELF_REFERENCE",
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" REQUIRES_TOOL_FOR_USE",
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" LOCATION_OF\n or\n PERSON_FROM_LOCATION\n\n",
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" OWNER",
" OWNER",
" OWNER",
" SELF_REFERENCE",
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[
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],
[
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],
[
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[
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"DATE",
"2005-2024"
],
[
"86",
"88",
"PERSON",
"Chad Cloman"
]
] | 702 |
704 | [
"GOLF DU CHARDONNAY\nThe golf of the Chardonnay is an integral part of the Champagne André JACQUART. It is a unique compact golf of 6 holes where both beginners and professional can train on their small game.\nCome and try the accuracy of your shots under 120m on our synthetic green which technical performances approach those of professional levels.\nDamien Larbre, member of the French golf league for 18 years will share his passion and skills all year long.\nEverything was designed to spend a wonderful golf moment while drinking a glass of Champagne André JACQUART.\nThe Golf du Chardonnay also offer classes for both adults and children"
] | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['GOLF DU CHARDONNAY\nThe golf of the Chardonnay is an integral part of the Champagne André JACQUART. It is a unique compact golf of 6 holes where both beginners and professional can train on their small game.\nCome and try the accuracy of your shots under 120m on our synthetic green which technical performances approach those of professional levels.\nDamien Larbre, member of the French golf league for 18 years will share his passion and skills all year long.\nEverything was designed to spend a wonderful golf moment while drinking a glass of Champagne André JACQUART.\nThe Golf du Chardonnay also offer classes for both adults and children']
Entities:
[{'head': ['0', '3', 'FAC', 'GOLF DU CHARDONNAY'], 'tail': ['66', '68', 'PERSON', 'Damien Larbre']}]
Relation:
| [
[
{
"head": [
"0",
"3",
"FAC",
"GOLF DU CHARDONNAY"
],
"tail": [
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]
}
],
[
{
"head": [
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],
"tail": [
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}
],
[
{
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],
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]
}
],
[
{
"head": [
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],
"tail": [
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]
}
],
[
{
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"27",
"CARDINAL",
"6"
],
"tail": [
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"68",
"PERSON",
"Damien Larbre"
]
}
],
[
{
"head": [
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}
],
[
{
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"PERSON",
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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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[
"108",
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"The Golf du Chardonnay"
]
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705 | [
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] | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Entities:
[{'head': ['9', '11', 'PERSON', 'Pablo Piatti'], 'tail': ['26', '27', 'NORP', 'Argentinian']}]
Relation:
| [
[
{
"head": [
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"tail": [
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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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"141",
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"LOC",
"Europe"
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"117",
"118",
"PRODUCT",
"Cupro"
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}
]
] | [
" ARG1_TYPE: PERSON\n ARG2_TYPE: NORP",
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" GIVES_QUANTITY\n\n Explanation:\n The text states",
" PRODUCES_LENGTH_OF",
" FABRIC_WIDTH_OF",
" CREATED_FROM\n\n Explanation:\n The text states that P",
" PRODUCES_FROM\n\n Explanation:\n Pablo Piatti",
" GIVES_MATERIAL_TO",
" PRODUCED_IN",
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" PRODUCED_IN",
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] | 704 |
706 | [
"Macron trip, hailed as success, still raises questions about EU-China relations - South China Morning Post\nFrans-Paul van der Putten, a researcher and founder of ChinaGeopolitics, a Dutch advisory business, said that given Europe's limited ability to deal with China independently from the US, it was good for Macron to try to make use of his restricted capacity. \"While the EU needs to protect its internal cohesion and maintain close relations with the US, it also has to preserve its economic relations with China. Without these, the EU risks losing its economic strength, which it needs to deal with current and future geopolitical challenges,\" he said.\nKawala Xie published online 9 April 2023, South China Morning Post."
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Macron trip, hailed as success, still raises questions about EU-China relations - South China Morning Post\nFrans-Paul van der Putten, a researcher and founder of ChinaGeopolitics, a Dutch advisory business, said that given Europe\'s limited ability to deal with China independently from the US, it was good for Macron to try to make use of his restricted capacity. "While the EU needs to protect its internal cohesion and maintain close relations with the US, it also has to preserve its economic relations with China. Without these, the EU risks losing its economic strength, which it needs to deal with current and future geopolitical challenges," he said.\nKawala Xie published online 9 April 2023, South China Morning Post.']
Entities:
[{'head': ['0', '1', 'PERSON', 'Macron'], 'tail': ['11', '12', 'ORG', 'EU']}]
Relation:
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{
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['Charlotte Marckx, Violin\nOlivia Marckx, Cello\nCharlotte and Olivia Marckx give a thrilling recital performance in conjunction with Sounding Point Academy, Colburn’s summer program for advanced violinists. These two talented musicians have received national and international acclaim. They were featured on NPR’s From the Top, were grand prize winners of the KING FM Young Artists Awards, and have performed with symphonies around the county.\nView the livestream\nPatrons are encouraged to be fully vaccinated or obtain a negative COVID-19 test, but it is not required. See the full list of safety protocols for more information.']
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" SISTER",
" PLAYS_MUSICAL_INSTRUMENT",
" PLAYS_MUSICAL_INSTRUMENT",
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" PERFORMED_AT",
" PERFORMANCE_IN\n\n Explanation:\n Olivia Marckx",
" SIBLING",
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" HOSTED_BY",
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" PERFORMED_WITH",
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" HOSTED_BY",
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" HOSTED_BY",
" PERFORMS_IN\n\n Explanation:\n Charlotte Marckx performs",
" PERFORMS_WITH",
" PERFORMS_WITH",
" PERFORMS_IN\n\n Explanation:\n Charlotte is performing in the",
" PERFORMS_IN\n\n Explanation:\n The text states that Ol",
" PERFORMS_AT",
" LOCATION.HOST.OF.EVENT",
" FEATURED_IN.",
" SIBLING",
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" PERFORMED_AT",
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" FROM_ORGANIZATION_EVENT",
" FEATURED_IN.",
" FEATURED_IN",
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" PERFORMED_AT",
" FEATURED_IN",
" FEATURED_IN",
" WON_AWARD",
" WON_AWARD",
" WON_AWARD",
" WON_AWARD",
" WON_AWARD",
" PARTICIPANT_IN",
" HOSTED_BY",
" FEATURED_IN"
] | [
[
"0",
"2",
"PERSON",
"Charlotte Marckx"
],
[
"3",
"7",
"PERSON",
"Violin\nOlivia Marckx"
],
[
"8",
"9",
"PERSON",
"Cello"
],
[
"10",
"11",
"PERSON",
"Charlotte"
],
[
"12",
"14",
"PERSON",
"Olivia Marckx"
],
[
"22",
"25",
"ORG",
"Sounding Point Academy"
],
[
"26",
"27",
"GPE",
"Colburn"
],
[
"28",
"29",
"DATE",
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],
[
"35",
"36",
"CARDINAL",
"two"
],
[
"49",
"50",
"ORG",
"NPR"
],
[
"51",
"54",
"WORK_OF_ART",
"From the Top"
],
[
"60",
"66",
"WORK_OF_ART",
"the KING FM Young Artists Awards"
]
] | 707 |
709 | [
"The pasta-bilities are endless with our Pasta Gift Set! Gift includes three different artisanal pasta's and a sauce from Rustichella d'Abruzzo, a 200ml bottle of our award-winning Tuscan Herb Infused Olive Oil, our Italian Seasoning Spice mix, and recipe cards! Set comes wrapped in our large Kraft Gift Box.\n*Con' olio will choose the pastas and the sauce. We may substitute Tuscan Herb Olive Oil depending on availability with a similar herbaceous olive oil."
] | [
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["The pasta-bilities are endless with our Pasta Gift Set! Gift includes three different artisanal pasta's and a sauce from Rustichella d'Abruzzo, a 200ml bottle of our award-winning Tuscan Herb Infused Olive Oil, our Italian Seasoning Spice mix, and recipe cards! Set comes wrapped in our large Kraft Gift Box.\n*Con' olio will choose the pastas and the sauce. We may substitute Tuscan Herb Olive Oil depending on availability with a similar herbaceous olive oil."]
Entities:
[{'head': ['14', '15', 'CARDINAL', 'three'], 'tail': ['23', '25', 'ORG', "Rustichella d'Abruzzo"]}]
Relation:
| [
[
{
"head": [
"14",
"15",
"CARDINAL",
"three"
],
"tail": [
"23",
"25",
"ORG",
"Rustichella d'Abruzzo"
]
}
],
[
{
"head": [
"14",
"15",
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"three"
],
"tail": [
"56",
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"ORG",
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]
}
],
[
{
"head": [
"14",
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],
"tail": [
"62",
"65",
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"Con' olio"
]
}
],
[
{
"head": [
"23",
"25",
"ORG",
"Rustichella d'Abruzzo"
],
"tail": [
"14",
"15",
"CARDINAL",
"three"
]
}
],
[
{
"head": [
"23",
"25",
"ORG",
"Rustichella d'Abruzzo"
],
"tail": [
"34",
"39",
"PRODUCT",
"Tuscan Herb Infused Olive Oil"
]
}
],
[
{
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"23",
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"ORG",
"Rustichella d'Abruzzo"
],
"tail": [
"41",
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"NORP",
"Italian"
]
}
],
[
{
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"23",
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"ORG",
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],
"tail": [
"56",
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"ORG",
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]
}
],
[
{
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"ORG",
"Rustichella d'Abruzzo"
],
"tail": [
"62",
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"PERSON",
"Con' olio"
]
}
],
[
{
"head": [
"23",
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"ORG",
"Rustichella d'Abruzzo"
],
"tail": [
"76",
"80",
"PRODUCT",
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]
}
],
[
{
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"PRODUCT",
"Tuscan Herb Infused Olive Oil"
],
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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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"41",
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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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"56",
"57",
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],
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"PERSON",
"Con' olio"
]
}
],
[
{
"head": [
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"57",
"ORG",
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],
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"76",
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"PRODUCT",
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]
}
],
[
{
"head": [
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"65",
"PERSON",
"Con' olio"
],
"tail": [
"14",
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"CARDINAL",
"three"
]
}
],
[
{
"head": [
"62",
"65",
"PERSON",
"Con' olio"
],
"tail": [
"23",
"25",
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"Rustichella d'Abruzzo"
]
}
],
[
{
"head": [
"62",
"65",
"PERSON",
"Con' olio"
],
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"34",
"39",
"PRODUCT",
"Tuscan Herb Infused Olive Oil"
]
}
],
[
{
"head": [
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"65",
"PERSON",
"Con' olio"
],
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"41",
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"Italian"
]
}
],
[
{
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"65",
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],
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"56",
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"Kraft"
]
}
],
[
{
"head": [
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"PERSON",
"Con' olio"
],
"tail": [
"76",
"80",
"PRODUCT",
"Tuscan Herb Olive Oil"
]
}
],
[
{
"head": [
"76",
"80",
"PRODUCT",
"Tuscan Herb Olive Oil"
],
"tail": [
"23",
"25",
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"Rustichella d'Abruzzo"
]
}
],
[
{
"head": [
"76",
"80",
"PRODUCT",
"Tuscan Herb Olive Oil"
],
"tail": [
"56",
"57",
"ORG",
"Kraft"
]
}
],
[
{
"head": [
"76",
"80",
"PRODUCT",
"Tuscan Herb Olive Oil"
],
"tail": [
"62",
"65",
"PERSON",
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]
}
]
] | [
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" INCLUDES",
" CHOOSES",
" INCLUDES",
" PRODUCER_OF",
" PRODUCER_OF",
" PROVIDES_WRAPPING_FOR",
" CHOOSES",
" PRODUCER_OF",
" PRODUCED_BY",
" INCLUDES",
" CHOOSES",
" PRODUCES",
" GIFT_BOX_PROVIDER",
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" INCLUDES",
" PROVIDES",
" INCLUDES",
" GIFT_BOX_FROM\n\n Explanation:\n The text states that",
" CHOOSES",
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" CHOOSES\n\n Explanation:\n The text states that \"",
" PROVIDES",
" CHOOSES",
" CHOOSES\n\n Explanation:\n The text states that \"",
" WRAPPING_PROVIDER",
" CHOOSES",
" PRODUCED_BY",
" INCLUDES\n\n Explanation:\n The text states that the gift",
" CHOOSES"
] | [
[
"14",
"15",
"CARDINAL",
"three"
],
[
"23",
"25",
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[
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[
"62",
"65",
"PERSON",
"Con' olio"
],
[
"76",
"80",
"PRODUCT",
"Tuscan Herb Olive Oil"
]
] | 708 |
710 | [
"Dooly man charged with statutory rape\nPublished 3:47 pm Friday, August 12, 2016\nBy BECKY CRISSMAN\nVIENNA — A Dooly County man is behind bars, facing multiple charges for sexual activity with a minor.\nAnthony Martez Collins is being held at the Dooly County Justice Center. He is charged with Statutory Rape, Child Molestation, Enticing a Child for Indecent Purposes and Sexual assault.\nBond for Collins has been set at $40,000 for all charges."
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Dooly man charged with statutory rape\nPublished 3:47 pm Friday, August 12, 2016\nBy BECKY CRISSMAN\nVIENNA — A Dooly County man is behind bars, facing multiple charges for sexual activity with a minor.\nAnthony Martez Collins is being held at the Dooly County Justice Center. He is charged with Statutory Rape, Child Molestation, Enticing a Child for Indecent Purposes and Sexual assault.\nBond for Collins has been set at $40,000 for all charges.']
Entities:
[{'head': ['0', '1', 'GPE', 'Dooly'], 'tail': ['8', '10', 'TIME', '3:47 pm']}]
Relation:
| [
[
{
"head": [
"0",
"1",
"GPE",
"Dooly"
],
"tail": [
"8",
"10",
"TIME",
"3:47 pm"
]
}
],
[
{
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"GPE",
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],
"tail": [
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"16",
"DATE",
"Friday, August 12, 2016"
]
}
],
[
{
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],
"tail": [
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]
}
],
[
{
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"GPE",
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],
"tail": [
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"GPE",
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]
}
],
[
{
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"0",
"1",
"GPE",
"Dooly"
],
"tail": [
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]
}
],
[
{
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],
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}
],
[
{
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],
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}
],
[
{
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],
"tail": [
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]
}
],
[
{
"head": [
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"GPE",
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],
"tail": [
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]
}
],
[
{
"head": [
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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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"TIME",
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],
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}
],
[
{
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}
],
[
{
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}
],
[
{
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"DATE",
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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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711 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['fnlineno – Number the lines of footnotes\nThe package extends the lineno package so that even lines within a \\footnote are numbered and may be referred to using \\linelabel, \\ref, etc.\nThe package is delivered as part of the ‘lineno bundle’\n|The LaTeX Project Public License 1.3c\n|2010 Uwe Lück\nUwe Lück (deceased)\n|TeX Live as lineno\nMiKTeX as lineno\nDownload the contents of this package in one zip archive (926.5k).\nMaybe you are interested in the following packages as well.\n- ntabbing: Simple tabbing extension for automatic line numbering\n- dashrule: Draw dashed rules\n- dashundergaps: Produce gaps that are underlined, dotted or dashed\n- nopageno: No page numbers in LaTeX documents']
Entities:
[{'head': ['57', '59', 'PERSON', 'Uwe Lück'], 'tail': ['60', '62', 'PERSON', 'Uwe Lück']}]
Relation:
| [
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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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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Philippines telco PLDT has announced its CEO Alfredo Panlilio will retire at the end of the year.\nIn a stock filing, PLDT confirmed that chairman Manuel Pangilinan will replace Panlilio.\nPLDT stated that Panlilio is stepping down due to health reasons.\nPanlilio has been CEO of the telco since June 2021, after returning to the company as EVP, chief revenue officer in 2019.\nHe was previously CEO of the business for six years between May 2004 and September 2010.\nHe has also worked at IBM, Meralco, and Smart Communications during his career.\nFounded in 1928, PLDT (Philippine Long Distance Telephone Company) is the country's biggest telecoms company with more than 66 million mobile subscribers."]
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"Movies of Jay Russell and Shonali Bhowmik together\nJay Russell and Shonali Bhowmik have starred in 1 movie together. Their first film was A Very Special Episode in 2009..\nA Very Special Episode\nStarred as Curator\nStarred as Clerk\nUse the timeline above to view all the movies that Jay Russell and Shonali Bhowmik have starred in together. You can use the arrows or the arrow keys on your keyboard to slide from left-to-right along the timeline. If you have a touch device, you can slide left-to-right as well.\nSelect a movie to view who Jay Russell and Shonali Bhowmik starred as in that film.\nLooking for more?\nSee all of Jay Russell's connections.\nSee all of Shonali Bhowmik's connections."
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Movies of Jay Russell and Shonali Bhowmik together\nJay Russell and Shonali Bhowmik have starred in 1 movie together. Their first film was A Very Special Episode in 2009..\nA Very Special Episode\nStarred as Curator\nStarred as Clerk\nUse the timeline above to view all the movies that Jay Russell and Shonali Bhowmik have starred in together. You can use the arrows or the arrow keys on your keyboard to slide from left-to-right along the timeline. If you have a touch device, you can slide left-to-right as well.\nSelect a movie to view who Jay Russell and Shonali Bhowmik starred as in that film.\nLooking for more?\nSee all of Jay Russell's connections.\nSee all of Shonali Bhowmik's connections."]
Entities:
[{'head': ['2', '4', 'PERSON', 'Jay Russell'], 'tail': ['5', '7', 'PERSON', 'Shonali Bhowmik']}]
Relation:
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{
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[
"144",
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] | 713 |
715 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Entities:
[{'head': ['27', '31', 'ORG', 'Kkeswani Dental & Polyclinic'], 'tail': ['57', '59', 'PERSON', 'Pratik Keswani']}]
Relation:
| [
[
{
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}
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[
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] | 714 |
716 | [
"Don't miss out on these incredible offers.\nThis Colombia is silky and sweet with notes of apple, pine nuts, and grapefruit. Super refreshing as a black coffee!\n*Each box comes with 10 Nespresso®-compatible capsules.\n*Any capsules labelled as a \"sale\" item have a remaining shelf life between 2-8 months.\nHis Majesty the Coffee (HMC) is a specialty micro-roastery that was established in 2013 in Monza, Italy. Founded by Paolo Scimone, who is also their head roaster, they are one of the first Italian specialty coffee purveyors. HMC is dedicated to serving high-quality single-origin coffees as well as creating flavorsome blends with some of the finest beans the World has to offer."
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Don\'t miss out on these incredible offers.\nThis Colombia is silky and sweet with notes of apple, pine nuts, and grapefruit. Super refreshing as a black coffee!\n*Each box comes with 10 Nespresso®-compatible capsules.\n*Any capsules labelled as a "sale" item have a remaining shelf life between 2-8 months.\nHis Majesty the Coffee (HMC) is a specialty micro-roastery that was established in 2013 in Monza, Italy. Founded by Paolo Scimone, who is also their head roaster, they are one of the first Italian specialty coffee purveyors. HMC is dedicated to serving high-quality single-origin coffees as well as creating flavorsome blends with some of the finest beans the World has to offer.']
Entities:
[{'head': ['11', '12', 'GPE', 'Colombia'], 'tail': ['40', '41', 'CARDINAL', '10']}]
Relation:
| [
[
{
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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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}
],
[
{
"head": [
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}
],
[
{
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}
],
[
{
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}
],
[
{
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}
],
[
{
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],
"tail": [
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}
],
[
{
"head": [
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],
"tail": [
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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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],
"tail": [
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}
],
[
{
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],
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}
],
[
{
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}
],
[
{
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}
],
[
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"tail": [
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}
]
] | [
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" PROVIDES_COMPATIBLE_CAPSULES",
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" OFFERS_PRODUCTS_FROM\n ORIGINATES_FROM\n ",
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" HMC_is_located_in_Italy",
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" FOUNDER_OF\n Or\n NO_RELATION\n (Answer",
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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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],
[
"115",
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"ORG",
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],
[
"140",
"141",
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"World"
]
] | 715 |
717 | [
"I have been a member of EANJ for 20+ years and have found the association very valuable for making great business relationships and long-lasting friends. EANJ is the best for networking with business professionals. I feel honored to be part of this... read more\nThe Executives’ Association of New Jersey is always looking to grow the membership. Guests are welcome to attend two meetings to see what we’re all about. If you’re interested in being a guest, contact Deb Yohannan at [email protected] for more information."
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['I have been a member of EANJ for 20+ years and have found the association very valuable for making great business relationships and long-lasting friends. EANJ is the best for networking with business professionals. I feel honored to be part of this... read more\nThe Executives’ Association of New Jersey is always looking to grow the membership. Guests are welcome to attend two meetings to see what we’re all about. If you’re interested in being a guest, contact Deb Yohannan at [email protected] for more information.']
Entities:
[{'head': ['6', '7', 'ORG', 'EANJ'], 'tail': ['8', '11', 'DATE', '20+ years']}]
Relation:
| [
[
{
"head": [
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"tail": [
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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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],
"tail": [
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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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}
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{
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}
],
[
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}
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] | [
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" CONTACT_PERSON",
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] | [
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[
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[
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"Deb Yohannan"
]
] | 716 |
718 | [
"F. Cassata's eFootball 2023 Rating Weekly Movement\nAbout F. Cassata\nF. Cassata is a 24-year old Italian professional footballer who plays as a Center Midfielder (CMF) for the Genoa in the Italian Serie A. His contract salary with his current team is amounting to €2,500,000.\nF. Cassata on eFootball 2023\nF. Cassata's eFootball 2023 Rating is 72 with a Max Overall Rating of 85 based on Max Level of 46. His notable skills include:\n- Fighting Spirit\n- Long Ranger\n- Mazing Run\nThe section below shows his eFootball 2023 attributes."
] | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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["F. Cassata's eFootball 2023 Rating Weekly Movement\nAbout F. Cassata\nF. Cassata is a 24-year old Italian professional footballer who plays as a Center Midfielder (CMF) for the Genoa in the Italian Serie A. His contract salary with his current team is amounting to €2,500,000.\nF. Cassata on eFootball 2023\nF. Cassata's eFootball 2023 Rating is 72 with a Max Overall Rating of 85 based on Max Level of 46. His notable skills include:\n- Fighting Spirit\n- Long Ranger\n- Mazing Run\nThe section below shows his eFootball 2023 attributes."]
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719 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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[
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]
] | 718 |
720 | [
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Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Relation:
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] | 720 |
722 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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['1930 Ricardo Zamora DOUBLE-THICK unused weight card from Spain, a proof with a plain back. Issed to be used by weighing machine firms such as Freixinet.\nSee the supporting photo of a Quesada card on this website, from the same series but used. The Quesada is NOT included in this sale but may be bought separately.\nSOLD AS SEEN and what a STUNNER this Zamora card is!']
Entities:
[{'head': ['0', '1', 'DATE', '1930'], 'tail': ['1', '3', 'PERSON', 'Ricardo Zamora']}]
Relation:
| [
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] | 721 |
723 | [
"The famous birds from Walt Disney’s Enchanted Tikki Room, the very first audio-animatronic show which blew audiences away when it opened in 1963. This picture is of the bird tree which is lowered down from the ceiling, and the glee club sings “Let’s all Sing like the Birdies Sing”.\nThis image is available for licensing. Refer to my Pricing Page for a general overview of rates and policy regarding web use. For price quotes specific to a need, contact info@FriedmanArchives.com. All images on this site Copyright (c) 2000-2013 Gary L. Friedman"
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Entities:
[{'head': ['4', '7', 'PERSON', 'Walt Disney’s'], 'tail': ['7', '10', 'WORK_OF_ART', 'Enchanted Tikki Room']}]
Relation:
| [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["When Sylvia Beach opens Shakespeare and Company in 1919, she has no idea that she and her new bookstore will change the course of literature itself. It's where some of the most important literary friendships of the 20th century are forged - none more so than the one between James Joyce and Sylvia herself. When Joyce's controversial novel Ulysses is banned, Beach takes a risk and publishes it under the auspices of Shakespeare and Company. But the future of her store is threatened when Ulysses' success brings other publishers to woo Joyce away. As she faces painful personal and financial crises, Sylvia must decide what Shakespeare and Company truly means to her."]
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725 | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Wooden chest of drawers by Jiri Jiroutek, model U-453, circa 1960\nDESIGNER : Jiri Jiroutek\nMANUFACTURER : Interier Praha\nThis wooden chest of drawers is designed by Jiri Jiroutek for the publisher Interier Praha. It is the model U-453 dating from the 1960s. It has eight drawers with a wooden front and a molded plastic interior. This piece of furniture from the Czech Republic is in good vintage condition. The top is restored.\nLearn more about Jiri Jiroutek's chests."]
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
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733 | [
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] | 732 |
734 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["My shopping cart\nYour cart is currently empty.Continue Shopping\nLove of details, love of high-quality materials, love of craftsmanship - this is what the HUNTER brand has stood for for decades. The LOVE series reflects these values like no other. The cowhide leather used impresses with its stability and strong feel. The eye-catching hearts are individually edged and sewn by hand in the Bielefeld factory. In addition to its beautiful appearance, the LOVE collar is particularly comfortable to wear. It is lined with soft cowhide nappa leather for an extra cuddly feeling on the dog's neck. Every dog will love this collar! Note: Our leathers are manufactured as gently as possible. With dyed leather, there may initially be slight, fading discolouration."]
Entities:
[{'head': ['32', '33', 'ORG', 'HUNTER'], 'tail': ['38', '39', 'DATE', 'decades']}]
Relation:
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" SERVES_IN\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AT",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SELF_REFERENCE",
" SERVED_FOR_DURATION",
" SERVED_IN.",
" SERVED_FOR_DURATION",
" SERVED\n\n Entities: \n [{'head': ['8', '",
" SELF_REFERENCE",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" OWNER",
" DIRECTOR_OF\n or\n HEAD_OF\n or\n ",
" LOCATION",
" The three Idaho locations of PURE Medical Spa are in Boise.",
" The three Idaho locations of PURE Medical Spa are in Sun Valley.",
" The three Idaho locations of PURE Medical Spa are in Coeur d’Alene",
" DIRECTOR",
" MILITARY_SERVICE_AT\n or\n SERVED_IN\n ",
" SERVED_IN.",
" DIRECTOR",
" JOINED_BOARD",
" MEMBER_OF",
" JOINED_BOARD",
" LOCATION",
" LOCATION_OPENED",
" LOCATION_OF\n # This relation indicates that Julie Acarregui is the",
" LOCATION_COUNT",
" LOCATION.IN.LOCATION",
" ONE_OF: {\n \"Idaho\": {\n \"Sun Valley\": [\"",
" LOCATION_IN.",
" LOCATION.\n\n Explanation:\n The text states that Julie serves",
" LOCATION_OF\n OR\n WORKED_AT\n OR\n",
" MILITARY_SERVICE_IN.",
" LOCATION_IN_REGION",
" SERVED_IN.",
" LOCATION.IN.ORGANIZATION",
" MEMBER_OF",
" MEMBER_OF",
" TRAVELS_TO",
" LOCATION.\n\n Entities: \n [{'head': ['2",
" ESTABLISHED",
" LOCATION_OF\n\n Entities: \n [{'head': ['",
" LOCATION_OF\n OR\n HEAD_OF\n OR\n ",
" LOCATION_IN.",
" The medical director of PURE Medical Spa in Boise also serves in that capacity for its",
" LOCATION_IN. The entities represent the locations of the medical spa.",
" SERVES_AT.",
" SERVED_FOR_DURATION",
" MILITARY_SERVICE_IN.",
" LOCATION_OF\n OR\n TENURE\n OR\n NO",
" SERVED_IN.",
" SERVES_AT.",
" MEMBER_OF",
" JOINED_BOARD",
" TRAVELS_TO",
" LOCATION.\n\n Explanation:\n The text states that Julie serves",
" OPENED_IN.",
" LOCATION_OF\n Or\n NO_RELATION\n\n Explan",
" LOCATION_OF\n Or\n NO_RELATION (if no relation exists",
" LOCATION_IN.",
" The three Idaho locations of PURE Medical Spa are in Sun Valley and Boise.",
" LOCATION_IN. The entities represent two different locations of PURE Medical Spa.",
" LOCATION_OF\n or\n HEAD_OF\n depending on the",
" SERVED_FOR_DURATION",
" MILITARY_SERVICE_IN.",
" LOCATION_OF\n OR\n TENURE\n OR\n NO",
" SERVED_IN.",
" LOCATION_OF\n # This relation indicates that Julie is located in Sun Valley.",
" JOINED_BOARD",
" JOINED_BOARD",
" TRAVELS_TO",
" LOCATION.OF.ORGANIZATION",
" OPENED_IN",
" DIRECTOR_OF\n or\n LOCATION_OF\n\n [",
" LOCATION_OF\n Or\n NO_RELATION (if no relation exists",
" LOCATION_IN.",
" LOCATION_IN. The head entity is the location of the tail entity.",
" ONE_OF: {\n \"head\": {\"entity\": [\"Coeur d’",
" DIRECTOR_OF\n\n Explanation:\n The text states that Julie",
" SERVED_FOR_DURATION",
" MILITARY_SERVICE_IN.",
" LOCATION_OF\n OR\n STARTED_WORKING_AT\n",
" NO_RELATION",
" DIRECTOR_OF\n or\n HEAD_OF\n\n Explan",
" MEMBER_OF",
" MEMBER_OF",
" TRAVELS_TO",
" FOUNDED_BY",
" ESTABLISHED",
" SELF_REFERENCE",
" OWNER_OF\n ORGANIZATION\n [{'head': ['",
" SERVES_IN\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AT",
" SERVED_FOR_DURATION",
" SERVED_MILITARY",
" SERVED_FOR_DURATION",
" SERVED_IN.",
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" SERVED_IN.",
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" SERVES_LOCATION",
" SERVED_FOR_DURATION",
" SERVED_IN.",
" SERVED_FOR\n Or, if no relation exists:\n NO_RELATION",
" SERVED.",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" FOUNDER",
" ESTABLISHED_DATE",
" SERVED_IN.",
" SERVED_IN. (Note: The SERVED_IN relation is not a standard relation",
" MILITARY_SERVICE_IN\n\n Entities: \n [{'",
" LOCATION_OF\n ORGANIZATION_HEADQUARTERS_IN",
" LOCATION_IN.",
" SERVES_IN (ORG, GPE)",
" SERVED_IN.",
" SERVED_MILITARY_FOR_DURATION",
" MILITARY_SERVICE",
" MILITARY_SERVICE_TOGETHER",
" SERVED_IN.",
" MILITARY_SERVICE_FOR\n OR:\n HONORARY_",
" JOINED_BOARD",
" MILITARY_SERVICE_AT\n or\n WORKED_FOR\n",
" FOUNDER",
" SERVED_FOR\n or\n WORKED_FOR\n or\n ",
" SERVES_IN.",
" SERVED_IN.",
" SERVES_IN.",
" SERVES_LOCATION",
" SERVED_FOR\n or\n WORKED_FOR\n or\n ",
" MILITARY_SERVICE_WITH\n\n Explanation:\n The text",
" SERVED_FOR\n or\n MEMBER_OF\n or\n A",
" SERVED_FOR\n or\n WORKED_FOR\n or\n ",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" MILITARY_AFFILIATION",
" ESTABLISHED_IN_YEAR",
" SERVED\n\n Explanation:\n The text states that \"She served ",
" SERVED_WITH\n or\n HEAD_OF\n or\n ",
" The Army Reserve is located_in Idaho.",
" LOCATION_OF\n ORGANIZATION_HEADQUARTERS_IN",
" LOCATION_IN.",
" LOCATION_IN.",
" SERVED_IN.",
" SERVED_FOR_DURATION",
" SERVED_IN\n\n Explanation:\n The text states that \"She",
" SERVED_FOR\n or\n MEMBER_OF\n or\n A",
" SERVED_IN.",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" FOUNDED_BY",
" ESTABLISHED",
" SAME_AS",
" OWNER_OF\n ORGANIZATION_SIZE\n (Note:",
" SERVES_IN\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AT\n\n Entities: \n [{'head': ['",
" SERVES_AS",
" SERVED_FOR_DURATION",
" SERVED_IN.",
" SERVED_FOR_DURATION",
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" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" MEMBER_OF",
" ESTABLISHED_BY\n\n Entities: \n [{'",
" JOINED_BOARD",
" MEMBER_OF",
" JOINED_BOARD_IN_REGION",
" TRAVELS_TO",
" Galderma is_located_in Sun Valley.",
" LOCATED_IN",
" JOINED_BOARD",
" JOINED_BOARD",
" MILITARY_SERVICE_IN\n\n Explanation:\n The text",
" MEMBER_OF",
" MILITARY_SERVICE_AT\n\n Explanation:\n The text",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" MEMBER_OF",
" ESTABLISHED_IN_YEAR",
" JOINED_BOARD",
" MEMBER_OF",
" Kysse is located_in Idaho.",
" TRAVELS_TO",
" Kysse is_located_in Sun Valley.",
" LOCATION_IN.",
" JOINED_BOARD",
" JOINED_BOARD",
" MILITARY_SERVICE_IN\n\n Explanation:\n The text",
" MEMBER_OF\n or\n ASSOCIATED_WITH\n or\n",
" MILITARY_SERVICE_IN\n\n Explanation:\n The text",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_DATE",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_DATE_ORGANIZATION",
" MILITARY_SERVICE_IN\n\n Explanation:\n The text",
" JOINED_BOARD",
" JOINED",
" JOINED_BOARD",
" JOINED_BOARD",
" JOINED_BOARD"
] | [
[
"2",
"5",
"ORG",
"PURE Medical Spa"
],
[
"6",
"7",
"DATE",
"2008"
],
[
"8",
"10",
"PERSON",
"Julie Acarregui"
],
[
"16",
"17",
"CARDINAL",
"three"
],
[
"17",
"18",
"GPE",
"Idaho"
],
[
"20",
"21",
"GPE",
"Boise"
],
[
"22",
"24",
"GPE",
"Sun Valley"
],
[
"26",
"28",
"GPE",
"Coeur d’Alene"
],
[
"50",
"51",
"PERSON",
"Julie"
],
[
"72",
"74",
"DATE",
"10 years"
],
[
"78",
"81",
"ORG",
"the U.S. Army"
],
[
"82",
"84",
"DATE",
"four years"
],
[
"85",
"88",
"ORG",
"the Army Reserve"
],
[
"95",
"96",
"PERSON",
"Julie"
],
[
"118",
"119",
"ORG",
"Galderma"
],
[
"120",
"121",
"ORG",
"Kysse"
],
[
"125",
"126",
"GPE",
"Stockholm"
]
] | 735 |
737 | [
"Hannah Beecham talks to three funds industry players asking them how safe is it for professional investors to do business with funds based in this jurisdiction.\nWhat are the chances of an Irish bank going bust? Will costs of doing business here being coming down? How can this jurisdiction maintain a sharp competitive edge for increased funds business against a backdrop of economic gloom and doom? And what is the industry doing to support its international partners on a practical basis?"
] | [
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Hannah Beecham talks to three funds industry players asking them how safe is it for professional investors to do business with funds based in this jurisdiction.\nWhat are the chances of an Irish bank going bust? Will costs of doing business here being coming down? How can this jurisdiction maintain a sharp competitive edge for increased funds business against a backdrop of economic gloom and doom? And what is the industry doing to support its international partners on a practical basis?']
Entities:
[{'head': ['0', '2', 'PERSON', 'Hannah Beecham'], 'tail': ['4', '5', 'CARDINAL', 'three']}]
Relation:
| [
[
{
"head": [
"0",
"2",
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"Hannah Beecham"
],
"tail": [
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]
}
],
[
{
"head": [
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],
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}
],
[
{
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"tail": [
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]
}
],
[
{
"head": [
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"Irish"
],
"tail": [
"0",
"2",
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]
}
]
] | [
" TALKS_TO",
" TALKS_TO",
" INTERVIEWS",
" INTERVIEWS"
] | [
[
"0",
"2",
"PERSON",
"Hannah Beecham"
],
[
"4",
"5",
"CARDINAL",
"three"
],
[
"34",
"35",
"NORP",
"Irish"
]
] | 736 |
738 | [
"Letizia Ragazzini, soprano saxophone\nMariella Donnaloia, alto saxophone\nIsabella Fabbri, tenor saxophone\nLaura Rocchegiani, baritone saxophone\nMaria Giulia Cester, piano\nGershwin Quintet is an Italian female ensemble composed by a saxophone quartet (soprano, alto, tenor and baritone saxophone) and a piano. As the name suggests, the ensemble is focused on the repertoire of the American composer George Gershwin. The instrumental composition of the group offers a variety of tone, dynamic and expressive possibilities that let the quintet play the most famous Gershwin's songs, but also his masterpieces for orchestra like \"Rhapsody in Blue\", \"Concert in F\" and \"An American in Paris\".\nMore on: www.gershwinquintet.com"
] | [
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Letizia Ragazzini, soprano saxophone\nMariella Donnaloia, alto saxophone\nIsabella Fabbri, tenor saxophone\nLaura Rocchegiani, baritone saxophone\nMaria Giulia Cester, piano\nGershwin Quintet is an Italian female ensemble composed by a saxophone quartet (soprano, alto, tenor and baritone saxophone) and a piano. As the name suggests, the ensemble is focused on the repertoire of the American composer George Gershwin. The instrumental composition of the group offers a variety of tone, dynamic and expressive possibilities that let the quintet play the most famous Gershwin\'s songs, but also his masterpieces for orchestra like "Rhapsody in Blue", "Concert in F" and "An American in Paris".\nMore on: www.gershwinquintet.com']
Entities:
[{'head': ['0', '2', 'PERSON', 'Letizia Ragazzini'], 'tail': ['6', '8', 'PERSON', 'Mariella Donnaloia']}]
Relation:
| [
[
{
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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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],
"tail": [
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}
],
[
{
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],
[
{
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}
],
[
{
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}
],
[
{
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}
],
[
{
"head": [
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],
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}
],
[
{
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],
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}
],
[
{
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}
],
[
{
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],
"tail": [
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}
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[
{
"head": [
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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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}
],
[
{
"head": [
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],
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]
}
],
[
{
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],
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}
],
[
{
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],
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
" TEACHER_OF",
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATED_AT",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" GUESTING_WITH",
" LOCATION_NEARBY",
" LOCATION_IN.",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n OR LOCATED_AT (both are interchangeable",
" LOCATION_OF\n OR LOCATED_AT (interchangeable with LO",
" LOCATION_OF\n OR LOCATED_AT (both are accepted)",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" LOCATED_AT",
" LOCATED_AT",
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" BASSOON_MASTERCLASS_AT\n # This relation label indicates that the head",
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" TEACHER_OF",
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" MOVEMENT_OF\n\n Explanation:\n The text mentions that",
" COMPOSED_BY",
" MOVE_OF_CONCERTO",
" PERFORMS_WITH",
" MOVEMENT_OF\n\n Explanation:\n The text mentions several",
" GUESTED_AT",
" GUEST_AT\n\n Explanation:\n The text mentions that '",
" GUEST_AT.",
" GUESTED_BY",
" GUESTING_WITH",
" GUESTED_AT",
" GUEST_IN.",
" PERFORMED_BY",
" GUESTED_BY",
" GUESTED_BY",
" GUESTED_BY",
" GUESTED_BY",
" GUESTED_BY\n\n Explanation:\n The text mentions that",
" PERFORMED_WITH",
" GUESTED_BY\n\n Explanation:\n The text mentions that",
" GUESTED_BY",
" GUESTED_BY\n\n Explanation:\n The text mentions that",
" GUESTING_WITH",
" GUESTS_AT\n\n Explanation:\n The text mentions that",
" GUESTED_AT",
" GUESTING_WITH",
" GUESTED_BY",
" PERFORMED_AT",
" PERFORMING_AT",
" PERFORMED_BY",
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" HOSTED_BY",
" HOSTED_BY",
" HOSTED_BY",
" HOSTED_BY",
" BASSOON_MASTER_CLASS_AT\n This relation represents that the first",
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" HOSTED_BY",
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" LOCATED_IN",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n OR LOCATED_IN (depending on the specific",
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" LOCATION_OF\n or\n PERFORMED_AT\n or\n",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" COMPOSED_BY",
" COMPOSED_BY",
" COMPOSED_BY",
" COMPOSITION_OF\n\n Explanation:\n The text mentions that",
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" COMPOSITION_OF\n\n Explanation:\n The text mentions that",
" MOVEMENT_OF",
" COMPOSITION_OF\n\n Explanation:\n The text mentions that",
" MOVEMENT_OF",
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" TEACHER_AT",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" GUESTED_BY",
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" COMPOSED_BY",
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" GUESTING_WITH",
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" LOCATION_IN_CITY",
" MOVEMENT_OF",
" COMPOSED_BY",
" COMPOSED_BY",
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" CONCERTANTE_MOVEMENT_OF",
" MOVEMENT_OF",
" HOSTED_BY",
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
" COMPOSED_BY",
" PERFORMED_WITH",
" PERFORMED_BY",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
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" COMPOSED_BY",
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" LOCATION_OF\n\n Explanation:\n The text mentions that",
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741 | [
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["Anders Petersen is one of the most influential European photographers since the publication of Café Lehmitz in the '70s, with his intimate and direct images. In this volume are collected photos of his three trips to Rome in 1984...\nSign up for exclusive offers, original stories, events and more.\n27, GloucesterStreet, London,\nWC1N 3AX, United Kingdom\nWhen street knowledge\nmeets graphic design. ❤️"]
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743 | [
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744 | [
"1942. On an abandoned squash court, a dazzling scientific experiment takes place that three years later will destroy a city and change the world forever. Two actors, one British, one Japanese, explore the events surrounding the catastrophic ‘mistake’ that launched our nuclear age.\nInterweaving the stories of a young woman in war-time Hiroshima, a brilliant Hungarian scientist, and the American pilot who flew the plane that dropped the bomb, THE MISTAKE is a powerful drama that confronts the dangers that arise when humans dare to unlock the awesome power of nature.\nPlease contact the school for more information 01977 233600\nor see Michael Mear’s website:More information"
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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['1942. On an abandoned squash court, a dazzling scientific experiment takes place that three years later will destroy a city and change the world forever. Two actors, one British, one Japanese, explore the events surrounding the catastrophic ‘mistake’ that launched our nuclear age.\nInterweaving the stories of a young woman in war-time Hiroshima, a brilliant Hungarian scientist, and the American pilot who flew the plane that dropped the bomb, THE MISTAKE is a powerful drama that confronts the dangers that arise when humans dare to unlock the awesome power of nature.\nPlease contact the school for more information 01977 233600\nor see Michael Mear’s website:More information']
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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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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"111",
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"Ankit Hinglajia"
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"tail": [
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"103",
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"2024"
]
}
]
] | [
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" LOCATION.\n\n Explanation:\n The text mentions two locations",
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[
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] | 744 |
746 | [
"top of page\nSelected WIP LATAM SAN SEBASTIÁN 2022\nWON IMPULSO Morelia 2022\nIn Post Production\nMalena had her tubes tied after an abusive relationship and fled to Mexico City seeking a better life, but her ex-husband took custody of her children despite her fierce fight. Today she has a new partner and is finishing the house where she dreams of living with her three children and a new baby. She needs IVF treatment if she is to have a second chance now that she's older and wiser. Malena decides to quit her job as a domestic worker and return home to set up a business and have the family she always dreamt of. Feeling abandoned, her children punish her while her partner threatens to leave if the IVF fails. Can she pull everyone together and make her dreams come true?\nbottom of page"
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["top of page\nSelected WIP LATAM SAN SEBASTIÁN 2022\nWON IMPULSO Morelia 2022\nIn Post Production\nMalena had her tubes tied after an abusive relationship and fled to Mexico City seeking a better life, but her ex-husband took custody of her children despite her fierce fight. Today she has a new partner and is finishing the house where she dreams of living with her three children and a new baby. She needs IVF treatment if she is to have a second chance now that she's older and wiser. Malena decides to quit her job as a domestic worker and return home to set up a business and have the family she always dreamt of. Feeling abandoned, her children punish her while her partner threatens to leave if the IVF fails. Can she pull everyone together and make her dreams come true?\nbottom of page"]
Entities:
[{'head': ['9', '10', 'DATE', '2022'], 'tail': ['12', '14', 'PERSON', 'IMPULSO Morelia']}]
Relation:
| [
[
{
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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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" HOSTED_BY",
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" LOCATION_IN. 'Impulso Morelia' is located 'in' '",
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747 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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['Smiling buddha cast in messing\nIt depicts not buddha but a Chinese Zen-master, called Poe-Tai-Ho-Sang.\nHe lived in China somewhere between 6th and 10th century, happily, most of the time surrounded by children.\nAs he found buddha within, after his death was revered as a popular hero and god of happiness.\nHis likeness is nowadays known over the whole world as the smiling or potbellied buddha, bringer of wealth and happiness.\nThis Buddha holds an ingot in his right hand, symbol of (physical) wealth.\nMeasurements: 7 x 4 cm']
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748 | [
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749 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['PRIME RETAIL SPACE FOR LEASE - CLEVELAND\nIDEAL FOR FOOD OUTLET OR TAKEAWAY BUSINESS\n* Excellent Main Street Frontage/Exposure\n* Split System Air Conditioner\n* 79sqm of Floor Space\n* Located in Cleveland CBD\n* High Pedestrian Foot Traffic\n* Close to Weekend Markets\n* Close to Bus and Train Stations\n* Available 1st of October 2023\nContact Michael Fardouly on 0413 517 222\nDo you own a home?\nPrepare for profit. Download our top tips on how to get the highest and best price when selling.']
Entities:
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Relation:
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750 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
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751 | [
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Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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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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] | 750 |
752 | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["The 22-year old Italian had only lost one set in the tournament before facing Daniil Medvedev in the final.\nAnnual tennis tournament held in Melbourne\nThe Australian Open is a tennis tournament held annually at Melbourne Park in Melbourne, Victoria, Australia. The tournament is the first of the four Grand Slam tennis events held each year, preceding the French Open, Wimbledon and the US Open. The Australian Open starts in the middle of January and continues for two weeks coinciding with the Australia Day holiday. It features men's and women's singles; men's, women's and mixed doubles; junior's championships; and wheelchair, legends and exhibition events. Until 1987, it was played on grass courts, but since then three types of hardcourt surfaces have been used: green-coloured Rebound Ace up to 2007 and blue Plexicushion from 2008 to 2019. Since 2020, it has been played on blue GreenSet."]
Entities:
[{'head': ['1', '5', 'DATE', '22-year old'], 'tail': ['16', '18', 'PERSON', 'Daniil Medvedev']}]
Relation:
| [
[
{
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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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753 | [
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['The latest US back-to-school numbers and insight with NPD’s Leen Nsouli.\nWith 2020’s important back-to-school (BTS) season hit by COVID restrictions and school closures, there are hopes that 2021 will see something of a return to normal. Whether that will now be the case – as the Delta variant spreads in the US – remains to be seen.\nYet, as NPD analyst Leen Nsouli explains in this episode of OPI Talk, the numbers up until the middle of August have been encouraging. She provides details on some of the key category trends, talks about retail versus online and looks at some of the factors that are impacting BTS spending this year.\nClick on the play button below to listen to the podcast.']
Entities:
[{'head': ['2', '3', 'GPE', 'US'], 'tail': ['12', '13', 'ORG', 'NPD']}]
Relation:
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Approximately $1.4 million in cargo was stolen from Miami International Airport on Feb. 10, FreightWatch International reported.\nThe cargo was loaded into a tractor-trailer and left in a warehouse parking lot unattended while the driver entered the building to complete his paperwork, FreightWatch said.\nThis is the fourth similar incident at the Miami airport in a month, all targeting consumer electronics cargo, FreightWatch said. The total estimated value of all the thefts is $3 million, according to Dan Burges, FreightWatch senior director of intelligence.\nSince Miami-Dade Police Department has decided to scale back its Cargo Theft Task Force for budgetary reasons, an increase in organized cargo theft activity is probable, FreightWatch said.']
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"ORG",
"Miami-Dade Police Department"
]
}
],
[
{
"head": [
"94",
"95",
"ORG",
"FreightWatch"
],
"tail": [
"115",
"117",
"ORG",
"Task Force"
]
}
],
[
{
"head": [
"94",
"95",
"ORG",
"FreightWatch"
],
"tail": [
"131",
"132",
"ORG",
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]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"0",
"4",
"MONEY",
"Approximately $1.4 million"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"9",
"12",
"FAC",
"Miami International Airport"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"13",
"15",
"DATE",
"Feb. 10"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"16",
"18",
"ORG",
"FreightWatch International"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"49",
"50",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"56",
"57",
"ORDINAL",
"fourth"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"61",
"62",
"GPE",
"Miami"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"64",
"66",
"DATE",
"a month"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"73",
"74",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"85",
"88",
"MONEY",
"$3 million"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"91",
"93",
"PERSON",
"Dan Burges"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"94",
"95",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"115",
"117",
"ORG",
"Task Force"
]
}
],
[
{
"head": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
"tail": [
"131",
"132",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
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"4",
"MONEY",
"Approximately $1.4 million"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"9",
"12",
"FAC",
"Miami International Airport"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"13",
"15",
"DATE",
"Feb. 10"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"16",
"18",
"ORG",
"FreightWatch International"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"49",
"50",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"56",
"57",
"ORDINAL",
"fourth"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"61",
"62",
"GPE",
"Miami"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"64",
"66",
"DATE",
"a month"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"73",
"74",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"85",
"88",
"MONEY",
"$3 million"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"91",
"93",
"PERSON",
"Dan Burges"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"94",
"95",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
]
}
],
[
{
"head": [
"115",
"117",
"ORG",
"Task Force"
],
"tail": [
"131",
"132",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"0",
"4",
"MONEY",
"Approximately $1.4 million"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"9",
"12",
"FAC",
"Miami International Airport"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"13",
"15",
"DATE",
"Feb. 10"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"16",
"18",
"ORG",
"FreightWatch International"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"49",
"50",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"56",
"57",
"ORDINAL",
"fourth"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"61",
"62",
"GPE",
"Miami"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"64",
"66",
"DATE",
"a month"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"73",
"74",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"85",
"88",
"MONEY",
"$3 million"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"91",
"93",
"PERSON",
"Dan Burges"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"94",
"95",
"ORG",
"FreightWatch"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"102",
"107",
"ORG",
"Miami-Dade Police Department"
]
}
],
[
{
"head": [
"131",
"132",
"ORG",
"FreightWatch"
],
"tail": [
"115",
"117",
"ORG",
"Task Force"
]
}
]
] | [
" REPORTED_BY",
" REPORTED_BY",
" THEFT_AT\n\n Explanation:\n The text states that approximately $",
" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS_BY.",
" REPORTED_BY",
" BUDGET_CONSTRAINT\n\n Explanation:\n The text",
" SCALED_BACK",
" REPORTED_BY",
" REPORTED_BY",
" REPORTED_BY",
" LOCATION_OF\n OR\n LOCATED_AT\n OR\n",
" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS_BY.",
" REPORTED_BY",
" SCALED_BACK",
" SCALED_BACK",
" REPORTED_BY",
" REPORTED_BY",
" REPORTED_BY",
" THEFT_AT\n\n Entities: \n [{'head': ['6",
" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS_BY_PERSON",
" REPORTED_BY",
" DECIDED_TO_SCALE_BACK",
" SCALED_BACK",
" REPORTED_BY",
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" REPORTED_FROM",
" REPORTED_ON",
" SPOKES_FOR",
" REPORTED_BY",
" REPORTED_AT",
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" SPOKES_FOR",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" SPOKES_FOR",
" SPOKE_OF",
" SAID_TO_HAVE\n\n Explanation:\n FreightWatch",
" REPORTED_BY",
" REPORTED_THEFT_VALUE",
" REPORTED_AT.",
" REPORTED_ON",
" IS_INSTANCE_OF",
" FOURTH_OF_A_SEQUENCE\n\n Entities: \n ",
" REPORTED_AT",
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" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" REPORTED_BY",
" SPOKE_OF",
" SAID_TO_HAVE_SCALED_BACK",
" REPORTED_BY",
" FOURTH_OF_SERIES\n\n Explanation:\n The text",
" FOURTH_OF_MONTH_OF_INSTANCES_OF_OR",
" FOURTH_OF_SERIES\n\n Explanation:\n The text",
" FOURTH_OF_MONTH_OF_INSTANCES_OF_OR",
" SPOKE_FOR",
" FOURTH_OF_MONTH_OF_INSTANCES_OF_OR",
" PROBABLE_CAUSE",
" PROBABLE_INCREASE",
" FOURTH_OF_MONTH_OF_INSTANCES_OF_OR",
" THEFT\n\n Explanation:\n The text describes an incident of cargo theft",
" LOCATION_OF\n OR\n LOCATED_AT\n OR\n",
" THEFT_ON_DATE",
" REPORTED_BY",
" REPORTED_BY",
" INCIDENT_FOURTH_OF_SERIES",
" INCIDENT_WITHIN_TIME_PERIOD",
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" SCALED_BACK",
" REPORTED_BY",
" REPORTED_BY",
" REPORTED_BY",
" INCREASED_IN_PROXIMITY_TO\n OR\n LO",
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" REPORTED_BY",
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" SCALED_BACK",
" REPORTED_BY",
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" REPORTED_FROM",
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" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS",
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" VALUE_OF\n OR:\n LOCATION_OF\n OR:",
" ESTIMATED_VALUE_OF_THEFTS",
" ESTIMATED_VALUE_OF_THEFTS",
" ESTIMATED_VALUE_OF_THEFTS",
" ESTIMATED_BUDGET_FOR\n\n Explanation:\n ",
" ESTIMATED_VALUE_OF_THEFTS\n OR\n PROB",
" ESTIMATED_VALUE_OF_THEFTS",
" ESTIMATED_VALUE_OF_THEFTS",
" ESTIMATED_VALUE_OF_THEFTS_FROM\n\n Explan",
" ESTIMATED_VALUE_OF_THEFTS_ON_DATE",
" SPOKE_FOR",
" SPOKE_FOR",
" SPOKE_OF",
" ESTIMATED_VALUE_OF_THEFTS_FROM\n\n Explan",
" ESTIMATED_VALUE_OF_THEFTS_OCCURRED_WITH",
" SPOKE_FOR",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" SAID_THEY_WOULD_PROBABLY_SCALE_",
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" SAME_ORGANIZATION",
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" SPOKE_OF",
" SAID_TO_HAVE_SCALED_BACK",
" REPORTED_BY",
" THEFT_FROM",
" SCALED_BACK_FROM\n\n Explanation:\n The text states",
" DISSOLVED_BY\n\n Explanation:\n The text mentions",
" SCALED_BACK",
" SCALED_BACK_RESOURCE\n\n Explanation:\n The text",
" SCALED_BACK",
" SCALED_BACK",
" SCALED_BACK",
" SPOKE_FOR",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" SPOKE_FOR",
" SCALED_BACK",
" SPOKE_FOR",
" STOLEN_FROM",
" DISSOLVED_BY\n or\n SCALED_BACK_BY\n",
" DISSOLVED_BY\n\n Explanation:\n The text mentions",
" SPOKE_FOR",
" SCALED_BACK",
" SEQ_FOLLOWS",
" DISSOLVED_FROM",
" SCALED_BACK",
" SPOKE_FOR",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" SPOKE_FOR",
" SCALED_BACK",
" SPOKE_FOR",
" REPORTED_THEFT_VALUE",
" REPORTED_AT.",
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" REPORTED_BY",
" ESTIMATED_VALUE_OF_THEFTS",
" SPOKE_FOR",
" REPORTED_BY",
" SPOKE_OF\n\n Explanation:\n FreightWatch reported on",
" SAID_TO_HAVE_SCALED_BACK"
] | [
[
"0",
"4",
"MONEY",
"Approximately $1.4 million"
],
[
"9",
"12",
"FAC",
"Miami International Airport"
],
[
"13",
"15",
"DATE",
"Feb. 10"
],
[
"16",
"18",
"ORG",
"FreightWatch International"
],
[
"49",
"50",
"ORG",
"FreightWatch"
],
[
"56",
"57",
"ORDINAL",
"fourth"
],
[
"61",
"62",
"GPE",
"Miami"
],
[
"64",
"66",
"DATE",
"a month"
],
[
"73",
"74",
"ORG",
"FreightWatch"
],
[
"85",
"88",
"MONEY",
"$3 million"
],
[
"91",
"93",
"PERSON",
"Dan Burges"
],
[
"94",
"95",
"ORG",
"FreightWatch"
],
[
"102",
"107",
"ORG",
"Miami-Dade Police Department"
],
[
"115",
"117",
"ORG",
"Task Force"
],
[
"131",
"132",
"ORG",
"FreightWatch"
]
] | 754 |
756 | [
"Rainbow Cake - Two Tiered\nThe Ultimate Rainbow Smash Cake!\nCelebrate your little one's special day with lots of fun, color and maybe a little mess. It's worth the clean-up, we promise!\nOur ultimate rainbow celebration cake, two tiered cake. Each tier has three layers of your favorite flavor cake between a lighter, airer filling (either chocolate mousse or vanilla mascarpone), finished with an Italian meringue buttercream.\n** Photo Credit: Michelle Rose Photo\nPlease note that the Medium and Large Tiered Cakes are DELIVERY ONLY. Given their size, we do not have the ability to hold them at the stores. Please let us know about a delivery window that works for you."
] | [
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Rainbow Cake - Two Tiered\nThe Ultimate Rainbow Smash Cake!\nCelebrate your little one's special day with lots of fun, color and maybe a little mess. It's worth the clean-up, we promise!\nOur ultimate rainbow celebration cake, two tiered cake. Each tier has three layers of your favorite flavor cake between a lighter, airer filling (either chocolate mousse or vanilla mascarpone), finished with an Italian meringue buttercream.\n** Photo Credit: Michelle Rose Photo\nPlease note that the Medium and Large Tiered Cakes are DELIVERY ONLY. Given their size, we do not have the ability to hold them at the stores. Please let us know about a delivery window that works for you."]
Entities:
[{'head': ['3', '4', 'CARDINAL', 'Two'], 'tail': ['92', '94', 'PERSON', 'Michelle Rose']}]
Relation:
| [
[
{
"head": [
"3",
"4",
"CARDINAL",
"Two"
],
"tail": [
"92",
"94",
"PERSON",
"Michelle Rose"
]
}
],
[
{
"head": [
"50",
"51",
"CARDINAL",
"two"
],
"tail": [
"92",
"94",
"PERSON",
"Michelle Rose"
]
}
],
[
{
"head": [
"57",
"58",
"CARDINAL",
"three"
],
"tail": [
"92",
"94",
"PERSON",
"Michelle Rose"
]
}
],
[
{
"head": [
"82",
"83",
"NORP",
"Italian"
],
"tail": [
"92",
"94",
"PERSON",
"Michelle Rose"
]
}
],
[
{
"head": [
"92",
"94",
"PERSON",
"Michelle Rose"
],
"tail": [
"3",
"4",
"CARDINAL",
"Two"
]
}
],
[
{
"head": [
"92",
"94",
"PERSON",
"Michelle Rose"
],
"tail": [
"50",
"51",
"CARDINAL",
"two"
]
}
],
[
{
"head": [
"92",
"94",
"PERSON",
"Michelle Rose"
],
"tail": [
"57",
"58",
"CARDINAL",
"three"
]
}
],
[
{
"head": [
"92",
"94",
"PERSON",
"Michelle Rose"
],
"tail": [
"82",
"83",
"NORP",
"Italian"
]
}
]
] | [
" PHOTOGRAPHER",
" PHOTOGRAPHER",
" PHOTOGRAPHER",
" PHOTOGRAPHER",
" PHOTO_CREDIT",
" PHOTO_CREDIT",
" PHOTOGRAPHER_OF\n # This model is a fantastic relation extraction",
" PHOTOGRAPHER_OF"
] | [
[
"3",
"4",
"CARDINAL",
"Two"
],
[
"50",
"51",
"CARDINAL",
"two"
],
[
"57",
"58",
"CARDINAL",
"three"
],
[
"82",
"83",
"NORP",
"Italian"
],
[
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] | 755 |
757 | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Franziska Esmek, M. Sc.\nPhoto: Manuel Müller\nFranziska completed her bachelor and master degree in nanoscience at the University of Hamburg. She is part of Irene Fernandez-Cuestas group since 2015 starting with her bachelorthesis. Curerntly she is a phD student working on the detection and analysis of single molecules/particles on chip.']
Entities:
[{'head': ['0', '2', 'PERSON', 'Franziska Esmek'], 'tail': ['9', '11', 'PERSON', 'Manuel Müller']}]
Relation:
| [
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{
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}
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}
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}
],
[
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"tail": [
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}
],
[
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}
],
[
{
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}
],
[
{
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}
],
[
{
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}
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"tail": [
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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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" TOKENIZED_PHOTO_TAKEN_DATE",
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[
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]
] | 756 |
758 | [
"Tune in to Turn It Up to find the best of independent to major label releases, news from the industry, reviews, festival coverage, new artist spotlights, exclusive interviews, music videos, and album preview streams.\nChance The Rapper has emerged as one of the most positive, original and best new artists out right now. Rolling Stone christened his mix-tap the best hip hop release of the year. The 23 year old Chicago native is absolutely killing it on his live TV performances. Earlier this year, he gave a heartfelt tribute"
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Tune in to Turn It Up to find the best of independent to major label releases, news from the industry, reviews, festival coverage, new artist spotlights, exclusive interviews, music videos, and album preview streams.\nChance The Rapper has emerged as one of the most positive, original and best new artists out right now. Rolling Stone christened his mix-tap the best hip hop release of the year. The 23 year old Chicago native is absolutely killing it on his live TV performances. Earlier this year, he gave a heartfelt tribute']
Entities:
[{'head': ['3', '5', 'WORK_OF_ART', 'Turn It'], 'tail': ['43', '46', 'PERSON', 'Chance The Rapper']}]
Relation:
| [
[
{
"head": [
"3",
"5",
"WORK_OF_ART",
"Turn It"
],
"tail": [
"43",
"46",
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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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]
}
],
[
{
"head": [
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}
],
[
{
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"tail": [
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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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] | 757 |
759 | [
"Fatima Saab Bailoun\nArabic and English and French translator and subtitler\nWith over five years of experience, I possess a keen eye for detail and deep understanding of cultural nuances.\nMy goal is to facilitate cross-cultural understanding and enable audiences worldwide to enjoy content in their own language.\nMember of ATA/ Subtle/ NLSC and ProZ.com"
] | [
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]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Fatima Saab Bailoun\nArabic and English and French translator and subtitler\nWith over five years of experience, I possess a keen eye for detail and deep understanding of cultural nuances.\nMy goal is to facilitate cross-cultural understanding and enable audiences worldwide to enjoy content in their own language.\nMember of ATA/ Subtle/ NLSC and ProZ.com']
Entities:
[{'head': ['0', '2', 'PERSON', 'Fatima Saab'], 'tail': ['2', '3', 'LANGUAGE', 'Bailoun']}]
Relation:
| [
[
{
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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764 | [
"OKEECHOBEE – Plans for the long-awaited Bass Pro project at the Okee-Tantie Recreation Area continue to move forward according to information shared at the Jan. 23 meeting of the Okeechobee County Commission.\nBass Pro plans to turn the recreation area into a world class fishing resort.\nCounty Attorney Wade Vose said he recently had a good meeting with Big Cedar, the resort arm of Bass Pro. Vose said the county has resolved the issue of the structure which was funded by a Florida Recreation Development Assistance Program (FRDAP) grant. He said they have successfully dealt with all title issues involving the county.\nVose said Bass Pro is having discussions with the South Florida Water Management District to work through flowage easement issues.\nUPDATE: On Jan. 25, SFWMD officials stated Bass Pro has not scheduled meetings with the district and has not yet applied for permits for work at Okee-Tantie."
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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766 | [
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767 | [
"February 22 – March 9, 2024!\nA man in Stalin’s Russia has a perfect memory. When the State tells him to forget someone, he can’t…and then he disappears.\nA Soviet official is searching for him, and he tries to enlist the help of the psychologist who studied him. But are the official’s reasons political or personal? or both?\nA slippery examination of the connections between memory and identity, MEMOIRS OF A FORGOTTEN MAN also counts the costs we pay when the State decides what the truth is.\nAt the Studio at 12th Avenue Arts\n1620 12th Avenue, 98122"
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
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Relation:
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769 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" TRANSFERRED_KNOWLEDGE",
" EMPLOYED_BY",
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" LOCATION.\n\n Entities: \n [{'head': ['1",
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" LOCATION.\n\n Entities: \n [{'head': ['I",
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" LOCATION_OF\n\n Explanation:\n Ivy Botchway",
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" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
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" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
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" MEMBER_OF",
" MEMBER_OF",
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" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n ['59', '65', 'ORG', '",
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" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" OWSD_has_years_of_experience",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" GIVEN_KNOWLEDGE\n\n Explanation:\n The",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" LOCATION_OF\n OR LOCATED_IN (Both are valid for",
" MEMBER_OF",
" NO_RELATION",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" GIVEN_KNOWLEDGE\n\n Explanation:\n The",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n Ivy Botchway is",
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" WORKED_AT",
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775 | [
"The 21 Day Equity Challenge inspires discussion, action, and change in our community\nJoin the next challenge - June 19\nThe 21 Day Equity Challenge is designed to create dedicated time and space to build more effective social justice habits, particularly those dealing with issues of race, power, privilege, and leadership.\n2020 Challenge Catalogue\nThis challenge was originally developed by Dr. Eddie Moore, Jr., Dr. Marguerite Penick-Parks, and Debby Irving, and was adapted by Food Solutions New England. United Way of the Greater Capital Region has adapted the challenge for New York’s Capital Region in collaboration with the many community partners."
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778 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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[{'head': ['26', '28', 'DATE', 'many years'], 'tail': ['41', '44', 'PERSON', "J.R.R Tolkien's"]}]
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779 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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"Antioch"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"78",
"79",
"GPE",
"California"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"83",
"84",
"GPE",
"Antioch"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"86",
"87",
"ORDINAL",
"first"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"94",
"97",
"DATE",
"the 21st century"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"98",
"106",
"ORG",
"the California Conference of the 5th Episcopal District"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"108",
"109",
"PERSON",
"Barrett"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"115",
"116",
"ORDINAL",
"10th"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"119",
"122",
"ORG",
"Central Texas Conference"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"127",
"128",
"ORDINAL",
"3rd"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"131",
"133",
"GPE",
"West Virginia"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"135",
"137",
"GPE",
"Wheeling District"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"138",
"141",
"ORG",
"the Ohio Conference"
]
}
],
[
{
"head": [
"142",
"144",
"GPE",
"Columbus District"
],
"tail": [
"167",
"168",
"PERSON",
"Barrett"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"0",
"3",
"PERSON",
"Gloria J. Barrett"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"15",
"20",
"DATE",
"the twenty-first century"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"27",
"31",
"DATE",
"the past thirty years"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"44",
"45",
"PERSON",
"Barrett"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"67",
"72",
"ORG",
"Antioch African Methodist Episcopal Church"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"73",
"74",
"DATE",
"2001"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"76",
"77",
"GPE",
"Antioch"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"78",
"79",
"GPE",
"California"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"83",
"84",
"GPE",
"Antioch"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"86",
"87",
"ORDINAL",
"first"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"94",
"97",
"DATE",
"the 21st century"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"98",
"106",
"ORG",
"the California Conference of the 5th Episcopal District"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"108",
"109",
"PERSON",
"Barrett"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"115",
"116",
"ORDINAL",
"10th"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"119",
"122",
"ORG",
"Central Texas Conference"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"127",
"128",
"ORDINAL",
"3rd"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"131",
"133",
"GPE",
"West Virginia"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"135",
"137",
"GPE",
"Wheeling District"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"138",
"141",
"ORG",
"the Ohio Conference"
]
}
],
[
{
"head": [
"167",
"168",
"PERSON",
"Barrett"
],
"tail": [
"142",
"144",
"GPE",
"Columbus District"
]
}
]
] | [
" FOUNDER_IN_CENTURY",
" PAST_EVENT\n\n Explanation:\n The relation between 'Gloria",
" SELF_REFERENCE",
" FOUNDER",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDED_IN",
" FOUNDED_BY",
" FOUNDING_ORGANIZATION_OF\n\n Explanation:\n",
" FOUNDING_YEARS",
" FOUNDED_BY",
" SELF_REFERENCE",
" LEAD_IN_DISTRICT",
" PASTORED_IN.",
" LEAD_IN_DISTRICT",
" 'HAS_MINISTERED_IN'",
" PASTOR_OF",
" PASTORED_BY",
" PASTOR_OF",
" SELF_REFERENCE",
" FOUNDED_BY",
" LEADS_IN_CENTURY",
" FOUNDED_BY",
" FOUNDING_YEARS",
" FOUNDING_PLACE",
" FOUNDING_YEAR",
" FOUNDED_BY",
" LEADS_IN_CENTURY",
" PAST_LEADERSHIP",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEADS_ORGANIZATION_IN_century",
" PAST_EVENT\n\n Explanation:\n The text states that Gloria",
" SPOKE_OF_TIME_PERIOD_FOR_PERSON",
" FOUNDED_BY",
" SPAN_OF_TIME_IN_LOCATION",
" SPAN_IN_GPE",
" SPAN_OF_TIME_IN_LOCATION",
" ESTABLISHED_IN",
" SPOKE_FOR\n\n Explanation:\n The text states that Bar",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" PAST_EVENT\n\n Explanation:\n The text states that Barrett",
" SELF.",
" FOUNDING_YEARS\n\n Explanation:\n The text states",
" SPANNED_PERIOD",
" FOUNDER",
" FOUNDED",
" FOUNDER",
" FOUNDED_IN",
" FOUNDER",
" FOUNDING_ORGANIZATION_OF\n\n Explanation:\n",
" FOUNDING_YEARS\n\n Explanation:\n The text states",
" FOUNDED_BY",
" SELF_REFERENCE",
" LEAD_IN_DISTRICT",
" PASTORED_IN.",
" LEAD_IN_DISTRICT",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" 'HAS_MINISTRY_IN'",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" 'LED'",
" SELF_REFERENCE",
" PASTOR_OF",
" FOUNDED_IN",
" EXISTS_DURING",
" FOUNDER",
" FOUNDED_BY",
" FOUNDER_OF",
" LOCATION.FOUNDED_IN.REGION.STATE",
" FOUNDING_ORGANIZATION_OF",
" FOUNDING_ORGANIZATION",
" FOUNDED_IN",
" FOUNDED_BY",
" FOUNDER",
" LEAD_IN_DISTRICT",
" PAST_LEADERSHIP",
" PAST_LEADER_IN\n\n Explanation:\n The text states",
" Pastored_in",
" PAST_ASSOCIATION",
" LEAD_IN_DISTRICT",
" PAST_LEADER_IN",
" FOUNDER",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDED_IN",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDED_BY",
" PAST_LEADERSHIP",
" PAST_LEADER_IN.",
" PAST_LEADER_IN.",
" FOUNDED_BY",
" PAST_LEADER_IN.",
" FOUNDED_BY",
" FOUNDED_BY",
" FOUNDING_YEAR",
" SPANNED_PERIOD",
" FOUNDER",
" LOCATION",
" FOUNDED_IN",
" LOCATION_IN.",
" FOUNDING_PLACE",
" FOUNDING_YEAR",
" FOUNDED_IN",
" FOUNDING_DISTRICT",
" FOUNDER",
" LEAD_IN_DISTRICT",
" PAST_ASSOCIATION",
" LEAD_IN_DISTRICT",
" PAST_LEADER_IN\n\n Explanation:\n The text states",
" PAST_LEADER_IN\n\n Explanation:\n The text states",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" FOUNDER",
" FOUNDED_IN",
" LOCATION_FOUNDED_IN.",
" SPAN_OF_TIME",
" FOUNDER_OF",
" LOCATION.FOUNDED_IN.ORGANIZATION",
" FOUNDING_YEAR",
" LOCATION_FOUNDING",
" LOCATION_FOUNDING",
" ANTECEDED_BY\n\n Explanation:\n The text states",
" LOCATION_FOUNDED_IN_TIMEPERIOD",
" LOCATION_OF\n or\n ESTABLISHED_IN\n",
" FOUNDER_OF",
" LEAD_IN_DISTRICT",
" PAST_ASSOCIATION",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" DISTRICT_IN.",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" FOUNDER_OF",
" FOUNDED_BY",
" FOUNDING_YEARS",
" SPANNED_PERIOD",
" FOUNDER",
" LOCATION.FOUNDING_PLACE.OF.ORGANIZATION",
" FOUNDED_IN",
" FOUNDING_PLACE",
" LOCATION_IN.",
" FOUNDING_YEAR",
" FOUNDING_YEARS",
" FOUNDING_DISTRICT",
" FOUNDER",
" LEAD_IN_DISTRICT",
" PAST_ASSOCIATION",
" LEAD_IN_DISTRICT",
" PAST_LEADER_IN. This relation indicates that the head entity (Antioch",
" PAST_ASSOCIATION",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" FOUNDER",
" FOUNDER",
" FOUNDER",
" FOUNDER",
" FOUNDING",
" ESTABLISHED_IN",
" FOUNDING",
" ESTABLISHED_IN",
" FOUNDER",
" LEAD_IN_DISTRICT",
" PAST_LEADER_IN.",
" PAST_LEADER_IN.",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" FOUNDER",
" FOUNDER",
" FOUNDER",
" FOUNDED_BY",
" FOUNDING_YEARS\n\n Explanation:\n The text states",
" FOUNDING_PLACE",
" FOUNDING_YEARS",
" FOUNDED_BY",
" FOUNDER",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" FOUNDER",
" FOUNDER",
" FOUNDING_DATE",
" SPAWNED_IN.",
" FOUNDER",
" FOUNDING_ORGANIZATION",
" FOUNDING_DATE",
" FOUNDING_DISTRICT",
" LOCATION_IN.",
" FOUNDING_DISTRICT",
" ANCESTOR_DESCENDANT\n\n Explanation:\n The text",
" ESTABLISHED_IN",
" FOUNDER",
" LEAD_IN_DISTRICT",
" PAST_LEADERSHIP",
" DISTRICT_OF\n\n Explanation:\n The text states that Past",
" DISTRICT_IN\n\n Explanation:\n The text states that Past",
" MEMBER_OF",
" MEMBER_OF\n\n Explanation:\n The text states that Pastor",
" MEMBER_OF",
" FOUNDER",
" SPOUSE\n\n Explanation:\n The text does not provide any information",
" FOUNDED_IN",
" SPANNED_PERIOD",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" FOUNDED_BY",
" FOUNDED_YEAR",
" FOUNDED_BY",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" FOUNDED_BY",
" LEAD_OF\n\n Explanation:\n The text states that \"She",
" FOUNDED_IN",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEAD_IN_DISTRICT",
" PASTORED_IN.",
" LEAD_IN_DISTRICT",
" 'HAS_MINISTERED_IN'",
" 'HAS_MINISTRY_IN'",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" 'LED'",
" LEADS",
" LEADS\n\n Explanation:\n The text states that Gloria J",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEAD_IN_DISTRICT",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEAD_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" PASTORED_BY",
" LEAD_IN_DISTRICT",
" SERVED_IN\n\n Explanation:\n The text states that Pastor",
" PASTORED_BY",
" PAST_LEADERSHIP",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" ESTABLISHED_IN.",
" PREDECESSOR",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" PASTORED_BY",
" LEADS_DISTRICT",
" LEADS_DISTRICT",
" PASTOR_OF",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_DISTRICT",
" LEADS_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" LEADS_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Gl",
" ESTABLISHED_IN",
" SPAN_OF_TIME_IN_LOCATION",
" LEADS_DISTRICT\n\n Explanation:\n The text states",
" FOUNDED_IN",
" ESTABLISHED_IN",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" ESTABLISHED_IN",
" PAST_MEMBERSHIP",
" LEADS_DISTRICT\n\n Explanation:\n The text states",
" LEAD_IN_DISTRICT",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" DISTRICT_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEADS_DISTRICT\n\n Explanation:\n The text states",
" DISTRICT_OF_SERVICE",
" FOUNDING_DATE_OF\n\n Or\n\n [{'head': ['",
" SPANNED_PERIOD",
" DISTRICT_OF_SERVICE",
" DISTRICT_OF\n or\n LOCATED_IN\n or\n",
" FOUNDED_IN",
" DISTRICT_OF\n or\n LOCATED_IN\n or\n",
" DISTRICT_IN_DISTRICT",
" DISTRICT_OF\n or\n LOCATED_IN\n or\n",
" PAST_MEMBER_OF\n\n Explanation:\n The text states",
" FOUNDING_DATE_OF\n\n Or\n\n [{'head': ['",
" SUPERORDINATE_OF",
" DISTRICT_OF_SERVICE",
" PAST_LEADER_IN\n\n Explanation:\n The text states",
" PAST_LEADER_IN.",
" SPOUSE_OF\n # This is incorrect, the text does not provide enough context",
" DISTRICT_OF\n or\n LOCATED_IN\n or\n",
" DISTRICT_OF\n or\n LOCATED_IN\n or\n",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" DISTRICT_OF_SERVICE",
" LEADS_DISTRICT\n\n Explanation:\n The text states",
" FOUNDING_DATE",
" SERVED_IN\n\n Explanation:\n The text states that Pastor",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" FOUNDER",
" FOUNDED_BY",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" FOUNDING_ORGANIZATION_OF",
" FOUNDED_IN.",
" PREDECESSOR",
" LEADS_DISTRICT",
" LEAD_IN_DISTRICT",
" PAST_LEADERSHIP",
" LEAD_IN_DISTRICT",
" PAST_DISTRICT_IN_CONFERENCE",
" DISTRICT_IN\n\n Explanation:\n The text states that Past",
" LOCATION_IN.",
" LEADS\n\n Explanation:\n The text states that Pastor Bar",
" LEADS_DISTRICT",
" FOUNDING_DATE",
" SINCE_STARTED\n\n Explanation:\n The text states that",
" LEADS_DISTRICT",
" PAST_LEADER_OF",
" FOUNDED_IN.",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEAD_IN_DISTRICT",
" LEAD_IN_DISTRICT",
" MEMBER_OF",
" LEADS_DISTRICT",
" PAST_LEADER_IN\n\n Explanation:\n The text states",
" PAST_LEADER_IN.",
" LEAD_IN_DISTRICT",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_IN\n\n Explanation:\n The text states that Past",
" LEADS_DISTRICT\n\n Explanation:\n The text states",
" LEADS_DISTRICT",
" SELF_REFERENCE",
" FOUNDING_YEARS",
" SPANNED_PERIOD",
" SELF_REFERENCE",
" FOUNDER",
" FOUNDED_YEAR",
" FOUNDER",
" FOUNDER_IN.",
" FOUNDER",
" FOUNDING.",
" FOUNDING_YEARS",
" FOUNDED_BY",
" SELF_REFERENCE",
" LEAD_IN_DISTRICT",
" PASTORED_IN.",
" LEAD_IN_DISTRICT",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" PAST_ASSOCIATION",
" PAST_MEMBER\n\n Explanation:\n The text states that Past",
" 'LED'"
] | [
[
"0",
"3",
"PERSON",
"Gloria J. Barrett"
],
[
"15",
"20",
"DATE",
"the twenty-first century"
],
[
"27",
"31",
"DATE",
"the past thirty years"
],
[
"44",
"45",
"PERSON",
"Barrett"
],
[
"67",
"72",
"ORG",
"Antioch African Methodist Episcopal Church"
],
[
"73",
"74",
"DATE",
"2001"
],
[
"76",
"77",
"GPE",
"Antioch"
],
[
"78",
"79",
"GPE",
"California"
],
[
"83",
"84",
"GPE",
"Antioch"
],
[
"86",
"87",
"ORDINAL",
"first"
],
[
"94",
"97",
"DATE",
"the 21st century"
],
[
"98",
"106",
"ORG",
"the California Conference of the 5th Episcopal District"
],
[
"108",
"109",
"PERSON",
"Barrett"
],
[
"115",
"116",
"ORDINAL",
"10th"
],
[
"119",
"122",
"ORG",
"Central Texas Conference"
],
[
"127",
"128",
"ORDINAL",
"3rd"
],
[
"131",
"133",
"GPE",
"West Virginia"
],
[
"135",
"137",
"GPE",
"Wheeling District"
],
[
"138",
"141",
"ORG",
"the Ohio Conference"
],
[
"142",
"144",
"GPE",
"Columbus District"
],
[
"167",
"168",
"PERSON",
"Barrett"
]
] | 778 |
780 | [
"Pop Up Art Exhibition | Saturday, 21 September 2019 @ 10:00\nJoin us on Saturday, the 21st of September 2019 for an awesome Pop Up Art Exhibition. This is a collaboration between community members, creating a platform for exhibiting their artworks in different media and genres.\nAddress: 69 Cliff Street, De Kelders, Gansbaai\nHope to see you there!\nInspired by the visual symphony of clouds, Bredasdorp artist Marina Blight has a passion for the art that she teaches at her Marina Blight Fine Art, workshops and art classes. Using oil paint and mixed media combined with various techniques, Marina guides art lovers of all levels toward unleashing t...\nWhat is Xplorio?\nXplorio is your local connection allowing you to find anything and everything about a town."
] | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Pop Up Art Exhibition | Saturday, 21 September 2019 @ 10:00\nJoin us on Saturday, the 21st of September 2019 for an awesome Pop Up Art Exhibition. This is a collaboration between community members, creating a platform for exhibiting their artworks in different media and genres.\nAddress: 69 Cliff Street, De Kelders, Gansbaai\nHope to see you there!\nInspired by the visual symphony of clouds, Bredasdorp artist Marina Blight has a passion for the art that she teaches at her Marina Blight Fine Art, workshops and art classes. Using oil paint and mixed media combined with various techniques, Marina guides art lovers of all levels toward unleashing t...\nWhat is Xplorio?\nXplorio is your local connection allowing you to find anything and everything about a town.']
Entities:
[{'head': ['5', '10', 'DATE', 'Saturday, 21 September 2019'], 'tail': ['59', '61', 'GPE', 'De Kelders']}]
Relation:
| [
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{
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}
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}
]
] | [
" LOCATION.\n\n Explanation:\n The relation between the entities is",
" LOCATION.IN.EVENT",
" LOCATION_OF\n\n Explanation:\n The text mentions that",
" INSPIRED_BY",
" WILL_TEACH_AT (Marina Blight Fine Art is the head, Saturday",
" INSPIRED_BY",
" LOCATION.\n\n Explanation:\n The relation between the TIME entity",
" LOCATION.IN.EVENT",
" LOCATION_OF\n or\n HOST_OF\n or\n ",
" TEACHER_OF",
" WORKSHOP_AT\n or\n TEACHES_AT\n or",
" TEACHES",
" LOCATION.IN.EVENT",
" LOCATION.IN.EVENT",
" LOCATION_OF\n OR\n HOST_OF\n OR (if",
" ARTIST_OF\n or\n NO_RELATION (if Marina Blight",
" WILL_TEACH\n\n Explanation:\n The text mentions that",
" ARTIST_OF\n\n Explanation:\n The text mentions Marina",
" ADDRESS_OF",
" ADDRESS_OF",
" LOCATION_OF\n or\n LOCATED_IN\n or\n",
" ARTIST_OF\n\n Explanation:\n The text mentions Marina",
" LOCATED_AT",
" ARTIST_OF\n or\n TEACHER_OF\n depending on",
" LOCATION_OF\n OR\n HOSTED_AT\n OR\n",
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" LOCATION_OF\n OR\n HOSTED_ON\n\n [",
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" LOCATION_OF\n or\n VENUE_FOR\n or\n",
" LOCATION_TIME",
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" ADDRESS_OF",
" LOCATION_IN\n\n Explanation:\n The text mentions that",
" NEAR_BY",
" LOCATED_IN",
" LOCATED_IN",
" LOCATION_OF\n or\n ART_INSTITUTION_FOR",
" LOCATION_OF\n or\n HOST_OF\n or\n ",
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" LOCATION_NEARBY",
" LOCATION_NEARBY",
" LOCATED_IN",
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" LOCATED_IN",
" ARTIST_EXHIBITION\n\n Explanation:\n Marina Bl",
" TEACHES_AT\n\n Explanation:\n Marina Blight teaches art",
" CREATED_EVENT\n\n Explanation:\n Marina Blight is the",
" LOCATION_OF\n\n Explanation:\n Marina Blight is the",
" LOCATED_IN",
" LOCATED_IN",
" INSPIRED_BY",
" TEACHER_OF\n or\n ARTIST_OF\n or\n",
" SELF_REFERENCE",
" WORKSHOP_HOSTED_ON\n or\n EVENT_OCCUR",
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" WILL_BE_LOCATED_AT",
" LOCATION_OF\n or\n ADDRESS_OF\n or\n ",
" LOCATED_IN",
" LOCATED_IN",
" LOCATED_IN",
" ART_SCHOOL",
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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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[
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]
] | 781 |
783 | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['|Information for your selected name of : Al\nWe are delighted to say that AncestralName holds on record the unique Coat of Arms details for your family name.\nOur embroiderers, artists and calligraphers can expertly interpret this historical information to create unique and beautifully crafted heraldic products as shown in the listing below, each custom-made to order. We are pleased to summarise your family surname coat of arms as…\nThe Crest sits atop the Shield and is regarded as a significant contributor to a surname’s identity. Occasionally there may be no crest recorded for a particular Coat of Arms, especially those of continental Europe as well as those which are very old.']
Entities:
[{'head': ['7', '8', 'PERSON', 'Al'], 'tail': ['21', '24', 'WORK_OF_ART', 'Coat of Arms']}]
Relation:
| [
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{
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}
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{
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}
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}
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] | 782 |
784 | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Text — Book\nTorres Rubio, Diego de — Lasso, Francisco\nIberian Books aims to produce a foundational listing of all books published in Spain, Portugal and the New World or printed elsewhere in Spanish or Portuguese during the Golden Age, 1472-1700.\nMajor funding to support the activities of Iberian Books has been provided by the Andrew W. Mellon Foundation under their Scholarly Communications and Information Technology Scheme.\n2018 © All Rights Reserved.']
Entities:
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Relation:
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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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Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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789 | [
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] | 790 |
792 | [
"It was like Christmas when the box arrived from Swiftwick. This awesome company is Nikolas's new sock sponsor! Nikolas opened the box and discovered an endless supply of exceptional quality athletic performance socks that will keep Nikolas's feet healthy, happy, and better able to Run the World for Children. :) How cool is that? Thank you, Swiftwick!\nHey! I'm Thea, Nikolas's older sister! I'll be taking care of his blog."
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["It was like Christmas when the box arrived from Swiftwick. This awesome company is Nikolas's new sock sponsor! Nikolas opened the box and discovered an endless supply of exceptional quality athletic performance socks that will keep Nikolas's feet healthy, happy, and better able to Run the World for Children. :) How cool is that? Thank you, Swiftwick!\nHey! I'm Thea, Nikolas's older sister! I'll be taking care of his blog."]
Entities:
[{'head': ['3', '4', 'DATE', 'Christmas'], 'tail': ['9', '10', 'ORG', 'Swiftwick']}]
Relation:
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[
"0",
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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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"111",
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],
[
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],
[
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],
[
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],
[
"126",
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],
[
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],
[
"141",
"142",
"DATE",
"today"
],
[
"146",
"150",
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"the United States’"
]
] | 792 |
794 | [
"Glentoran Belfast United are off to a winning start in the Danske Bank Women's Premiership with a 3-0 away win over Newry City.\nThe goals last night were scored by Kellie Bailie, Ali McMaster and Rebecca McKenna.\nGlens coach Judith Reid told Q radio she was delighted with the victory against her former club.\nElsewhere, Linfield Ladies maintained their 100% record as they won 4 nil at Ballymena United All Stars.\nThe game between Sion Swifts and Crusaders Strikers was called off due to a Waterlogged pitch."
] | [
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] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Glentoran Belfast United are off to a winning start in the Danske Bank Women's Premiership with a 3-0 away win over Newry City.\nThe goals last night were scored by Kellie Bailie, Ali McMaster and Rebecca McKenna.\nGlens coach Judith Reid told Q radio she was delighted with the victory against her former club.\nElsewhere, Linfield Ladies maintained their 100% record as they won 4 nil at Ballymena United All Stars.\nThe game between Sion Swifts and Crusaders Strikers was called off due to a Waterlogged pitch."]
Entities:
[{'head': ['0', '3', 'ORG', 'Glentoran Belfast United'], 'tail': ['10', '16', 'ORG', "the Danske Bank Women's Premiership"]}]
Relation:
| [
[
{
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],
"tail": [
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]
}
],
[
{
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],
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]
}
],
[
{
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],
"tail": [
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]
}
],
[
{
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],
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]
}
],
[
{
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],
"tail": [
"35",
"37",
"PERSON",
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]
}
],
[
{
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],
"tail": [
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]
}
],
[
{
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],
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]
}
],
[
{
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],
"tail": [
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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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],
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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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]
}
],
[
{
"head": [
"88",
"90",
"ORG",
"Sion Swifts"
],
"tail": [
"79",
"83",
"ORG",
"Ballymena United All Stars"
]
}
],
[
{
"head": [
"88",
"90",
"ORG",
"Sion Swifts"
],
"tail": [
"91",
"93",
"ORG",
"Crusaders Strikers"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"0",
"3",
"ORG",
"Glentoran Belfast United"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"10",
"16",
"ORG",
"the Danske Bank Women's Premiership"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"18",
"19",
"CARDINAL",
"3"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"24",
"26",
"GPE",
"Newry City"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"30",
"32",
"TIME",
"last night"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"35",
"37",
"PERSON",
"Kellie Bailie"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"38",
"40",
"PERSON",
"Ali McMaster"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"41",
"43",
"PERSON",
"Rebecca McKenna"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"45",
"46",
"ORG",
"Glens"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"47",
"49",
"PERSON",
"Judith Reid"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"50",
"51",
"ORG",
"Q"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"66",
"68",
"ORG",
"Linfield Ladies"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"70",
"72",
"PERCENT",
"100%"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"76",
"77",
"CARDINAL",
"4"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"79",
"83",
"ORG",
"Ballymena United All Stars"
]
}
],
[
{
"head": [
"91",
"93",
"ORG",
"Crusaders Strikers"
],
"tail": [
"88",
"90",
"ORG",
"Sion Swifts"
]
}
]
] | [
" MEMBER_OF",
" SCORED",
" OPPONENT",
" HOURS_AGO",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" SAME_AS",
" FORMER_TEAM_MEMBER",
" TOLD_RADIO",
" NO_RELATION",
" NO_RELATION",
" WON_AGAINST",
" COMPETES_AGAINST",
" NO_RELATION",
" NO_RELATION",
" PLAYS_IN",
" HOSTED_IN",
" HOME_TEAM",
" MATCH_TIMES",
" PLAYED_IN. This is the relation between the Danske Bank Women's Prem",
" PLAYED_FOR\n\n Explanation:\n The text states that Ali",
" PLAYED_FOR\n\n Explanation:\n Rebecca McKenna is",
" PLAYS_IN",
" COACHED_BY",
" BROADCASTING_OUTLET",
" PARALLEL_COMPETITION",
" The Danske Bank Women's Premiership has a 100% record.",
" LEAGUE_WON_BY_TEAM",
" COMPETES_IN.",
" NO_RELATION",
" CANCELLED",
" PLAYED_AGAINST",
" PLAYS_IN",
" OPPONENT",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" COACHED_BY",
" COACHED_BY",
" SPEAKS_TO",
" WON_BY",
" LOST_TO",
" NO_RELATION",
" NO_RELATION",
" OPPONENT",
" MEMBER_OF",
" OPPONENT",
" HOUR_OF_DAY_EVENT\n\n Explanation:\n The text",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" OPPONENT",
" FORMER_CLUB_MEMBER",
" TOLD_RADIO",
" DIFFERENT_TEAMS",
" NO_RELATION",
" LOST_TO",
" GAME_BETWEEN",
" NO_RELATION",
" NO_RELATION",
" WON_BY\n\n Entities: \n [{'head': ['0",
" PLAYS_IN",
" WON_BY",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" WON_BY\n\n Explanation:\n The text states that \"Gl",
" SPOKE_AT",
" SPOKE_TO",
" PLAYED_AGAINST",
" WON_MATCH",
" NO_RELATION",
" NO_RELATION",
" SCORED_FOR",
" PLAYS_IN",
" SCORED",
" SCORED_AT",
" SCORED_AT",
" SCORED_BY",
" SCORED_GOAL_FOR",
" SCORED_FOR",
" FORMER_TEAM_MEMBER_OF",
" TOLD_TO",
" SCORED_AGAINST",
" NO_RELATION",
" SCORED_GOAL",
" SCORED_AGAINST",
" PLAYED_FOR\n\n Explanation:\n The text mentions that",
" SCORED_AGAINST",
" PLAYER_OF\n\n Explanation:\n Ali McMaster is a",
" PLAYS_IN",
" SCORED",
" SCORED_AT",
" SCORED_DURING",
" SCORED_BY",
" SCORED_GOAL_FOR",
" PLAYER_OF\n\n Explanation:\n The text states that Ali",
" FORMER_TEAM_MEMBER_OF",
" SPOKE_TO",
" PLAYED_FOR\n\n Explanation:\n Ali McMaster is a",
" NO_RELATION",
" SCORED_GOAL",
" PLAYED_AGAINST",
" NO_RELATION",
" PLAYED_AGAINST",
" PLAYED_FOR",
" PLAYS_IN",
" SCORED",
" SCORED_AT",
" SCORED_AT",
" SCORED_BY",
" SCORED_BY",
" PLAYED_FOR",
" COACHED_BY",
" SPOKE_TO",
" SCORED_FOR\n\n Explanation:\n Rebecca McKenna scored",
" NO_RELATION",
" SCORED_GOALS",
" SCORED_AGAINST",
" NO_RELATION",
" SCORED_AGAINST",
" 'ORG_NAME_ALIAS'",
" PLAYS_IN.",
" SCORED_GOALS",
" OPPONENT",
" MENTIONED_IN_CONTEXT",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" COACHED_BY",
" SPEAKS_TO",
" COMPETES_AGAINST",
" LINES_ABOVE_100_PERCENT_RECORD",
" COACHED_BY",
" WON_BY",
" NO_RELATION",
" NO_RELATION",
" COACHED_BY",
" COACHES_LEAGUE",
" COACHED_BY",
" FORMER_CLUB_OF",
" SPEAKS_AT",
" COACHED",
" COACHED_BY",
" COACHED",
" COACHED_BY",
" SPOKE_TO",
" COACHED",
" COACHED_BY",
" COACHED_BY",
" COACHED",
" NO_RELATION",
" COACHED",
" TOLD_RADIO",
" PLAYS_IN",
" SCORED",
" OPPONENT",
" MENTIONED_IN_CONTEXT",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" COACHED_BY",
" COACHED_BY",
" COMPETES_AGAINST",
" NO_RELATION",
" Maintained_record_against",
" WON_AGAINST",
" NO_RELATION",
" NO_RELATION",
" WINNER_OF\n\n Explanation:\n The text states that Linfield",
" The Danske Bank Women's Premiership is the league that Linfield Ladies are",
" WON_BY_SCORE\n\n Explanation:\n The text states",
" WIN_AGAINST",
" WON_AT_TIME",
" SCORED_BY",
" PLAYED_FOR",
" PLAYED_FOR\n\n Explanation:\n The text mentions that",
" COMPETES_AGAINST",
" COACHED_BY",
" TOLD_RADIO",
" MAINTAINED_RECORD",
" WON_BY\n\n Explanation:\n Linfield Ladies won the",
" WON_BY",
" NO_RELATION",
" NO_RELATION",
" HAS_PERFECT_RECORD",
" The Danske Bank Women's Premiership has a PERCENT_100",
" WON_AGAINST",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" MAINTAINED",
" COACHED_BY",
" BROADCASTS",
" '100_PERCENT_RECORD'",
" WON_BY",
" NO_RELATION",
" CANCELLED",
" WON_BY",
" PLAYS_IN",
" OPPONENT",
" SCORED_BY",
" SCORED_BY",
" SCORED_BY",
" COACHED_BY",
" COACHED_BY",
" SPEAKS_TO",
" WON_AGAINST",
" WON_BY",
" NO_RELATION",
" NO_RELATION",
" HOME_TEAM\n\n Explanation:\n The text mentions that",
" HOME_TEAM_IN_LEAGUE",
" LOST_TO",
" HOMETEAM_OPPONENT",
" HOURS_AGO",
" SCORED_AGAINST",
" SCORED_BY",
" OPPONENT",
" WON_BY",
" COACHED_BY",
" BROADCASTING_OUTLET",
" WON_BY",
" WON_AGAINST",
" WIN_GAMES\n\n Explanation:\n The text states that \"",
" NO_RELATION",
" CANCELLED_MATCH",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" CANCELLED_EVENT",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" COACHED_BY",
" NO_RELATION",
" CANCELLED_EVENT\n\n Explanation:\n The text mentions",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" CANCELLED_IN_LEAGUE",
" NO_RELATION",
" CANCELLED",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" COACHED",
" NO_RELATION",
" CANCELLED",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION",
" NO_RELATION"
] | [
[
"0",
"3",
"ORG",
"Glentoran Belfast United"
],
[
"10",
"16",
"ORG",
"the Danske Bank Women's Premiership"
],
[
"18",
"19",
"CARDINAL",
"3"
],
[
"24",
"26",
"GPE",
"Newry City"
],
[
"30",
"32",
"TIME",
"last night"
],
[
"35",
"37",
"PERSON",
"Kellie Bailie"
],
[
"38",
"40",
"PERSON",
"Ali McMaster"
],
[
"41",
"43",
"PERSON",
"Rebecca McKenna"
],
[
"45",
"46",
"ORG",
"Glens"
],
[
"47",
"49",
"PERSON",
"Judith Reid"
],
[
"50",
"51",
"ORG",
"Q"
],
[
"66",
"68",
"ORG",
"Linfield Ladies"
],
[
"70",
"72",
"PERCENT",
"100%"
],
[
"76",
"77",
"CARDINAL",
"4"
],
[
"79",
"83",
"ORG",
"Ballymena United All Stars"
],
[
"88",
"90",
"ORG",
"Sion Swifts"
],
[
"91",
"93",
"ORG",
"Crusaders Strikers"
]
] | 793 |
795 | [
"Enter your email address and we'll send you a link to reset your password.\nJordan White and Amy Blue, two troubled teens, pick up an adolescent drifter, Xavier Red. Together, the threesome embarks on a sex and violence-filled journey through a United States of psychos and quickie marts.\nYou must create an account to continue watching\nQuick Sign Up!\nIt takes less then 1 minute to Sign Up, then you can enjoy Unlimited Movies & TV titles."
] | [
[
"Enter",
"your",
"email",
"address",
"and",
"we",
"'ll",
"send",
"you",
"a",
"link",
"to",
"reset",
"your",
"password",
".",
"\n",
"Jordan",
"White",
"and",
"Amy",
"Blue",
",",
"two",
"troubled",
"teens",
",",
"pick",
"up",
"an",
"adolescent",
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",",
"Xavier",
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"the",
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"embarks",
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"a",
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"of",
"psychos",
"and",
"quickie",
"marts",
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"\n",
"You",
"must",
"create",
"an",
"account",
"to",
"continue",
"watching",
"\n",
"Quick",
"Sign",
"Up",
"!",
"\n",
"It",
"takes",
"less",
"then",
"1",
"minute",
"to",
"Sign",
"Up",
",",
"then",
"you",
"can",
"enjoy",
"Unlimited",
"Movies",
"&",
"TV",
"titles",
"."
]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
["Enter your email address and we'll send you a link to reset your password.\nJordan White and Amy Blue, two troubled teens, pick up an adolescent drifter, Xavier Red. Together, the threesome embarks on a sex and violence-filled journey through a United States of psychos and quickie marts.\nYou must create an account to continue watching\nQuick Sign Up!\nIt takes less then 1 minute to Sign Up, then you can enjoy Unlimited Movies & TV titles."]
Entities:
[{'head': ['17', '19', 'PERSON', 'Jordan White'], 'tail': ['20', '22', 'PERSON', 'Amy Blue']}]
Relation:
| [
[
{
"head": [
"17",
"19",
"PERSON",
"Jordan White"
],
"tail": [
"20",
"22",
"PERSON",
"Amy Blue"
]
}
],
[
{
"head": [
"17",
"19",
"PERSON",
"Jordan White"
],
"tail": [
"23",
"24",
"CARDINAL",
"two"
]
}
],
[
{
"head": [
"17",
"19",
"PERSON",
"Jordan White"
],
"tail": [
"33",
"35",
"PERSON",
"Xavier Red"
]
}
],
[
{
"head": [
"17",
"19",
"PERSON",
"Jordan White"
],
"tail": [
"50",
"53",
"GPE",
"a United States"
]
}
],
[
{
"head": [
"17",
"19",
"PERSON",
"Jordan White"
],
"tail": [
"76",
"80",
"TIME",
"less then 1 minute"
]
}
],
[
{
"head": [
"20",
"22",
"PERSON",
"Amy Blue"
],
"tail": [
"17",
"19",
"PERSON",
"Jordan White"
]
}
],
[
{
"head": [
"20",
"22",
"PERSON",
"Amy Blue"
],
"tail": [
"23",
"24",
"CARDINAL",
"two"
]
}
],
[
{
"head": [
"20",
"22",
"PERSON",
"Amy Blue"
],
"tail": [
"33",
"35",
"PERSON",
"Xavier Red"
]
}
],
[
{
"head": [
"20",
"22",
"PERSON",
"Amy Blue"
],
"tail": [
"50",
"53",
"GPE",
"a United States"
]
}
],
[
{
"head": [
"20",
"22",
"PERSON",
"Amy Blue"
],
"tail": [
"76",
"80",
"TIME",
"less then 1 minute"
]
}
],
[
{
"head": [
"23",
"24",
"CARDINAL",
"two"
],
"tail": [
"17",
"19",
"PERSON",
"Jordan White"
]
}
],
[
{
"head": [
"23",
"24",
"CARDINAL",
"two"
],
"tail": [
"20",
"22",
"PERSON",
"Amy Blue"
]
}
],
[
{
"head": [
"23",
"24",
"CARDINAL",
"two"
],
"tail": [
"33",
"35",
"PERSON",
"Xavier Red"
]
}
],
[
{
"head": [
"23",
"24",
"CARDINAL",
"two"
],
"tail": [
"50",
"53",
"GPE",
"a United States"
]
}
],
[
{
"head": [
"33",
"35",
"PERSON",
"Xavier Red"
],
"tail": [
"17",
"19",
"PERSON",
"Jordan White"
]
}
],
[
{
"head": [
"33",
"35",
"PERSON",
"Xavier Red"
],
"tail": [
"20",
"22",
"PERSON",
"Amy Blue"
]
}
],
[
{
"head": [
"33",
"35",
"PERSON",
"Xavier Red"
],
"tail": [
"23",
"24",
"CARDINAL",
"two"
]
}
],
[
{
"head": [
"33",
"35",
"PERSON",
"Xavier Red"
],
"tail": [
"50",
"53",
"GPE",
"a United States"
]
}
],
[
{
"head": [
"33",
"35",
"PERSON",
"Xavier Red"
],
"tail": [
"76",
"80",
"TIME",
"less then 1 minute"
]
}
],
[
{
"head": [
"50",
"53",
"GPE",
"a United States"
],
"tail": [
"17",
"19",
"PERSON",
"Jordan White"
]
}
],
[
{
"head": [
"50",
"53",
"GPE",
"a United States"
],
"tail": [
"20",
"22",
"PERSON",
"Amy Blue"
]
}
],
[
{
"head": [
"50",
"53",
"GPE",
"a United States"
],
"tail": [
"23",
"24",
"CARDINAL",
"two"
]
}
],
[
{
"head": [
"50",
"53",
"GPE",
"a United States"
],
"tail": [
"33",
"35",
"PERSON",
"Xavier Red"
]
}
],
[
{
"head": [
"50",
"53",
"GPE",
"a United States"
],
"tail": [
"76",
"80",
"TIME",
"less then 1 minute"
]
}
],
[
{
"head": [
"76",
"80",
"TIME",
"less then 1 minute"
],
"tail": [
"17",
"19",
"PERSON",
"Jordan White"
]
}
],
[
{
"head": [
"76",
"80",
"TIME",
"less then 1 minute"
],
"tail": [
"20",
"22",
"PERSON",
"Amy Blue"
]
}
],
[
{
"head": [
"76",
"80",
"TIME",
"less then 1 minute"
],
"tail": [
"33",
"35",
"PERSON",
"Xavier Red"
]
}
],
[
{
"head": [
"76",
"80",
"TIME",
"less then 1 minute"
],
"tail": [
"50",
"53",
"GPE",
"a United States"
]
}
]
] | [
" FRIEND",
" ONE_OR_MORE_PERSONS_HAVE_QUANTITY_X",
" PICK_UP",
" IN_COUNTRY",
" TAKES_TIME",
" SIBLING",
" ONE_OF_MANY\n or\n PART_OF\n depending on the",
" PICKS_UP",
" IN_COUNTRY",
" TAKES_TIME",
" SIBLINGS",
" SIBLING",
" PICK_UP",
" IN_COUNTRY",
" ASSOCIATED_WITH",
" COTRAVELER",
" COMPANION",
" TRAVELS_IN\n\n Explanation:\n The text describes X",
" TAKES_TIME",
" IN_COUNTRY",
" IN_COUNTRY",
" IN_COUNTRY",
" IN_COUNTRY",
" TIME_IN_SET\n\n Explanation:\n The text states that it",
" IT_TAKES_X_MINUTES_FOR_PERSON_TO_",
" NO_RELATION",
" TIME_LESS_THAN\n This text describes a sign-up process for a",
" TEMPORAL_IN_GPE"
] | [
[
"17",
"19",
"PERSON",
"Jordan White"
],
[
"20",
"22",
"PERSON",
"Amy Blue"
],
[
"23",
"24",
"CARDINAL",
"two"
],
[
"33",
"35",
"PERSON",
"Xavier Red"
],
[
"50",
"53",
"GPE",
"a United States"
],
[
"76",
"80",
"TIME",
"less then 1 minute"
]
] | 794 |
796 | [
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"61",
"ORG",
"Revenue"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"63",
"64",
"NORP",
"Irish"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"75",
"78",
"QUANTITY",
"approximately 185.84kg"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"82",
"84",
"FAC",
"Dublin Airport"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"85",
"86",
"DATE",
"Thursday"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"103",
"106",
"PRODUCT",
"Spray Overalls\""
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"117",
"118",
"GPE",
"Canada"
]
}
],
[
{
"head": [
"124",
"125",
"GPE",
"Dublin"
],
"tail": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"0",
"1",
"PERSON",
"Maggie"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"5",
"8",
"MONEY",
"€3.7M"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"12",
"13",
"NORP",
"Irish"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"15",
"18",
"MONEY",
"£3,2 Million"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"23",
"25",
"FAC",
"Dublin Airport"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"29",
"33",
"GPE",
"the Republic of Ireland"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"43",
"44",
"MONEY",
"3,7"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"44",
"45",
"MONEY",
"M"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"47",
"48",
"MONEY",
"3,2"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"48",
"49",
"MONEY",
"M"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"57",
"58",
"PERSON",
"Maggie"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"60",
"61",
"ORG",
"Revenue"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"63",
"64",
"NORP",
"Irish"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"75",
"78",
"QUANTITY",
"approximately 185.84kg"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"82",
"84",
"FAC",
"Dublin Airport"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"85",
"86",
"DATE",
"Thursday"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"103",
"106",
"PRODUCT",
"Spray Overalls\""
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"117",
"118",
"GPE",
"Canada"
]
}
],
[
{
"head": [
"137",
"142",
"GPE",
"the Republic of Ireland's"
],
"tail": [
"124",
"125",
"GPE",
"Dublin"
]
}
]
] | [
" HELPS_FIND",
" HELPS\n\n Explanation:\n Maggie helps Irish Authorities in finding",
" HELPS_FIND",
" HELPS_FIND",
" ASSISTS",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_FIND",
" HELPS",
" HELPS",
" HELPS\n\n Explanation:\n Maggie helps Irish Authorities.",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_ON_THURSDAY",
" HELPS_SEIZURE",
" ORIGIN_COUNTRY",
" HELPS_AT_LOCATION",
" HELPS",
" HELPS_FIND",
" VALUE_OF\n or\n SEIZED_FROM\n or\n ",
" HELPS_FIND",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" VALUE_OF\n This is the correct answer. The head entity is the value,",
" VALUE_OF\n This is the correct answer. The head entity is the value,",
" HELPS",
" COUNTRY_OF_ORIGIN",
" HELPS",
" SEIZED_BY",
" ORIGIN_COUNTRY",
" LOCATION.IN.COUNTRY.OR_REGION",
" LOCATION_OF\n OR:\n BELONGS_TO\n ",
" HELPS_FIND",
" SEIZED_FROM",
" HELPS_FIND",
" SEIZED_BY",
" ORIGIN_COUNTRY",
" VALUE_OF\n This is the correct answer. The relation is VALUE_OF",
" VALUE_OF\n\n Explanation:\n The text states that the Irish",
" HELPS",
" LOCATION_IN.\n Or, if you prefer, you could use the more",
" HELPS",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" LOCATION_NEARBY",
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" HELPS",
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" SEIZED_VALUE",
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" LOCATION_IN.",
" OPERATION_ON_DATE",
" ORIGIN_FROM",
" IMPORTS_FROM",
" LOCATION_IN.",
" OPERATION_OF.",
" HELPS_FIND",
" VALUE_OF\n The value of the seized drugs is 3.7M Euro",
" HELPS_FIND",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" VALUE_OF\n This is the correct answer. The head entity is the value,",
" VALUE_OF\n This is the correct answer. The relation is VALUE_OF",
" HELPS_FIND\n or\n ASSISTS_IN_FIND",
" SEIZED_FROM",
" HELPS_FIND",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" VALUE_OF\n or\n HAS_VALUE\n or\n W",
" VALUE_OF\n This is the correct answer. The head entity is the value (",
" HELPS_FIND",
" SEIZED_FROM",
" HELPS_FIND",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" VALUE_OF\n This text describes the value of the seized drugs in Dublin. The",
" VALUE_OF\n This text describes the value of the seized drugs in both Euros",
" HELPS_FIND",
" SEIZED_FROM",
" HELPS_FIND",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" VALUE_OF\n This is the correct answer. The relation is VALUE_OF",
" SEIZED_FROM",
" SELF_REFERENCE",
" HELPS_FIND",
" HELPS\n\n Explanation:\n Maggie helps Irish Authorities.",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_ORGANIZATION",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_FIND",
" HELPS_FIND",
" HELPS",
" HELPS\n\n Explanation:\n Maggie helps Irish Authorities in finding",
" HELPS_FIND",
" HELPS_FIND",
" HELPED_ON. This is the label for the relation where the head entity helps",
" HELPS_FIND",
" ORIGIN_COUNTRY",
" HELPS_AT_LOCATION",
" HELPS",
" HELPS",
" SEIZED",
" GOVERNMENT_OF",
" SEIZED",
" SEIZED_AT",
" GOVERNMENT_OF",
" SEIZED_VALUE",
" SEIZED\n\n Explanation:\n The text states that \"Reven",
" SEIZED_VALUE",
" SEIZED_VALUE",
" HELPS",
" GOVERNMENT_OF",
" SEIZED",
" SEIZED_FROM",
" SEIZED_ON",
" SEIZED_FROM",
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" SEIZED_AT",
" RESPONSIBLE_FOR",
" HELPS",
" COUNTRY_OF_ORIGIN",
" HELPS",
" SEIZED_BY",
" origin_country",
" LOCATION_IN.",
" OWNER",
" HELPS",
" SEIZED_FROM",
" HELPS",
" SEIZED_BY",
" ORIGIN_COUNTRY",
" LOCATION_OF\n or\n FOUND_AT\n or\n ",
" SEIZED_FROM",
" HELPS",
" LOCATION_IN.\n Or, if you prefer, you could use the more",
" HELPS",
" SEIZED_FROM",
" ORIGIN_COUNTRY",
" LOCATION_NEARBY",
" LOCATION_IN.",
" HELPS",
" SEIZURE_AT_LOCATION",
" HELPS",
" SEIZED_BY",
" ORIGIN_COUNTRY",
" SEIZURE_AT_LOCATION",
" OPERATION_BASED_IN",
" HELPS",
" PRODUCE_FROM",
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" SEIZED_BY",
" ORIGIN_COUNTRY",
" DESTINATION",
" ORIGIN_FROM",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" origin_country",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
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" ORIGIN_COUNTRY",
" ORIGIN_COUNTRY",
" HELPS_FINDING",
" LOCATION_OF\n OR\n SEIZED_FROM\n OR\n",
" LOCATION_OF\n OR:\n COUNTRY_OF\n OR",
" LOCATION_OF\n OR\n SEIZED_FROM\n OR\n",
" LOCATION_NEARBY",
" LOCATION_IN_REGION",
" LOCATION_OF\n OR\n VALUE_OF\n OR\n ",
" VALUE_OF",
" LOCATION_OF\n\n Explanation:\n The text states that the",
" VALUE_OF\n\n Explanation:\n The text states that the her",
" HELPS_FINDING",
" SEIZED_BY",
" LOCATION_OF\n ORIGIN_FROM\n ORGANIZATION",
" LOCATION_OF\n\n Explanation:\n The text states that the",
" LOCATION_NEARBY",
" SEIZURE_ON\n OR\n OCCURRED_ON\n OR",
" DESTINED_FOR",
" ORIGIN_COUNTRY",
" LOCATION_IN_REGION",
" HELPS",
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" SEIZED_VALUE",
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" SEIZED_VALUE",
" SEIZED_VALUE",
" HELPS",
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" COUNTRY_OF_ORIGIN",
" SEIZED_FROM",
" LOCATION_IN.",
" OPERATION_ON_DATE",
" SEIZED_FROM",
" IMPORT_FROM",
" LOCATION_IN."
] | [
[
"0",
"1",
"PERSON",
"Maggie"
],
[
"5",
"8",
"MONEY",
"€3.7M"
],
[
"12",
"13",
"NORP",
"Irish"
],
[
"15",
"18",
"MONEY",
"£3,2 Million"
],
[
"23",
"25",
"FAC",
"Dublin Airport"
],
[
"29",
"33",
"GPE",
"the Republic of Ireland"
],
[
"43",
"44",
"MONEY",
"3,7"
],
[
"44",
"45",
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],
[
"47",
"48",
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],
[
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"49",
"MONEY",
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],
[
"57",
"58",
"PERSON",
"Maggie"
],
[
"60",
"61",
"ORG",
"Revenue"
],
[
"63",
"64",
"NORP",
"Irish"
],
[
"75",
"78",
"QUANTITY",
"approximately 185.84kg"
],
[
"82",
"84",
"FAC",
"Dublin Airport"
],
[
"85",
"86",
"DATE",
"Thursday"
],
[
"103",
"106",
"PRODUCT",
"Spray Overalls\""
],
[
"117",
"118",
"GPE",
"Canada"
],
[
"124",
"125",
"GPE",
"Dublin"
],
[
"137",
"142",
"GPE",
"the Republic of Ireland's"
]
] | 795 |
797 | [
"Discover why Amir Ladan is one of the Best Criminal Defense Attorneys in Orlando. Contact Us For A Free Case Review. Speak to a lawyer today.\n- Residency Florida\n- Company Name The Ladan Law Firm, P.A.\n- Company City Orlando\n- Company State Florida\n- Company Country United States\n- Designation Criminal Defense Attorney\n- Company Email firstname.lastname@example.org\n- Criminal Defense"
] | [
[
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"-",
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"-",
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"\n",
"-",
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"-",
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"-",
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"\n",
"-",
"Company",
"Email",
"firstname.lastname@example.org",
"\n",
"-",
"Criminal",
"Defense"
]
] | mistralai/Mistral-7B-Instruct-v0.2 | You are a fantastic relation extraction model who only outputs valid JSON.
Extract the relation between the given entities using the context in the below text. If no relation exists, use the label NO_RELATION.
ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
Text:
['Discover why Amir Ladan is one of the Best Criminal Defense Attorneys in Orlando. Contact Us For A Free Case Review. Speak to a lawyer today.\n- Residency Florida\n- Company Name The Ladan Law Firm, P.A.\n- Company City Orlando\n- Company State Florida\n- Company Country United States\n- Designation Criminal Defense Attorney\n- Company Email firstname.lastname@example.org\n- Criminal Defense']
Entities:
[{'head': ['2', '4', 'PERSON', 'Amir Ladan'], 'tail': ['13', '14', 'GPE', 'Orlando']}]
Relation:
| [
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"13",
"14",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"27",
"28",
"DATE",
"today"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"32",
"33",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"47",
"48",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"52",
"53",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"2",
"4",
"PERSON",
"Amir Ladan"
],
"tail": [
"57",
"59",
"GPE",
"United States"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"2",
"4",
"PERSON",
"Amir Ladan"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"27",
"28",
"DATE",
"today"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"32",
"33",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"47",
"48",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"52",
"53",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"13",
"14",
"GPE",
"Orlando"
],
"tail": [
"57",
"59",
"GPE",
"United States"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"2",
"4",
"PERSON",
"Amir Ladan"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"13",
"14",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"32",
"33",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"47",
"48",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
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"28",
"DATE",
"today"
],
"tail": [
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"53",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"27",
"28",
"DATE",
"today"
],
"tail": [
"57",
"59",
"GPE",
"United States"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"2",
"4",
"PERSON",
"Amir Ladan"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"13",
"14",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"27",
"28",
"DATE",
"today"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"37",
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"ORG",
"The Ladan Law Firm"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"47",
"48",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"52",
"53",
"GPE",
"Florida"
]
}
],
[
{
"head": [
"32",
"33",
"GPE",
"Florida"
],
"tail": [
"57",
"59",
"GPE",
"United States"
]
}
],
[
{
"head": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
],
"tail": [
"2",
"4",
"PERSON",
"Amir Ladan"
]
}
],
[
{
"head": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
],
"tail": [
"13",
"14",
"GPE",
"Orlando"
]
}
],
[
{
"head": [
"37",
"41",
"ORG",
"The Ladan Law Firm"
],
"tail": [
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798 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
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[{'head': ['25', '26', 'ORG', 'YouTube'], 'tail': ['34', '35', 'DATE', '2024']}]
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799 | [
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ONLY RETURN THE RELATION LABEL. Do not add additional text.
Pay VERY close attention to which entity is the head and tail; this dictates the direction of the relationship.
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Entities:
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[
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] | 799 |