Sweatshops

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sweatshops: In the 1990s, Nike received criticism for its use ofsweatshops.Beginning in 1990, many protests occurred in big cities such as Los Angeles,Washington, DC and Boston in order to show public outcry for Nike's use of child labor and sweatshops. Nike has been criticized for contracting with factories (known asNike sweatshops) in countries such as China, Vietnam, Indonesia and Mexico. Vietnam Labor Watch, an activist group, has documented that factories contracted by Nike haveviolatedminimum wageand overtimelaws in Vietnamas late as 1996, although Nike claims that this practice has been stopped. As of July 2011, Nike stated that two-thirds of its factories producing Converse products still do not meet the company's standards for worker treatment. A July 2011Associated Pressarticle stated that employees at the company's plants in Indonesia reported constant abuse from supervisors.

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q24942022 - Sweatshops: album",
    "Q136103272 - Sweatshops at Sea: Merchant Seamen in the World's First Globalized Industry, from 1812 to the Present: book 2011",
    "Q59123332 - Sweatshops: None",
    "Q136033661 - Sweatshops and the Corporatization of the University: journal article from 'Gender, Place & Culture' published in 2002",
    "Q129786056 - SWEATSHOPS AND CONSUMER CHOICES: scholarly article published on 17 May 2018"
]

[
    "Q881 - Vietnam: country in Southeast Asia",
    "Q9199 - Vietnamese: Austroasiatic language originating in Vietnam",
    "Q200180 - Vietnamese Wikipedia: Vietnamese-language edition of Wikipedia",
    "Q8740 - Vietnam War: armed conflict in Vietnam, Laos, and Cambodia between North Vietnam and South Vietnam, from 1955 to 1975",
    "Q430309 - Dai Viet: Vietnamese monarchy (10th–19th century)"
]

[
    "Q40469 - Associated Press: multinational nonprofit news agency headquartered in New York, United States",
    "Q743122 - Associated Press Athlete of the Year: award",
    "Q3625675 - Associated Press College Basketball Coach of the Year: Honor awarded to collegiate basketball coaches",
    "Q131286536 - Associated Press: organisation (1892-)",
    "Q121271034 - Associated Press: production company"
]

[
    "Q252 - Indonesia: island country in Southeast Asia and Oceania",
    "Q9240 - Indonesian: official language of Indonesia",
    "Q155214 - Indonesian Wikipedia: Indonesian-language edition of Wikipedia",
    "Q112136164 - Indonesian Minister of Home Affairs Decree Number 050-145 of 2022: Decree of the Minister of Home Affairs of the Republic of Indonesia Number 050-145 of 2022 concerning the Granting and Updating of Codes, Government Administration Area Data, and Islands in 2021",
    "Q109692162 - Indonesian 2009 DPR Ballot: None"
]

Output: None of the candidates


Sweatshops

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sweatshops: In the 1990s, Nike received criticism for its use ofsweatshops.Beginning in 1990, many protests occurred in big cities such as Los Angeles,Washington, DC and Boston in order to show public outcry for Nike's use of child labor and sweatshops. Nike has been criticized for contracting with factories (known asNike sweatshops) in countries such as China, Vietnam, Indonesia and Mexico. Vietnam Labor Watch, an activist group, has documented that factories contracted by Nike haveviolatedminimum wageand overtimelaws in Vietnamas late as 1996, although Nike claims that this practice has been stopped. As of July 2011, Nike stated that two-thirds of its factories producing Converse products still do not meet the company's standards for worker treatment. A July 2011Associated Pressarticle stated that employees at the company's plants in Indonesia reported constant abuse from supervisors.

Nike,_Inc.

Q483915 - Nike: American athletic equipment company

Q136103272 - Sweatshops at Sea: Merchant Seamen in the World's First Globalized Industry, from 1812 to the Present: book 2011

Q136033661 - Sweatshops and the Corporatization of the University: journal article from 'Gender, Place & Culture' published in 2002

Q129786056 - SWEATSHOPS AND CONSUMER CHOICES: scholarly article published on 17 May 2018

Nike,_Inc.

Q319515 - Converse: American shoe company and brand

Q52943 - conversation: interactive communication between two or more people

Q1375963 - converse: reverse of a categorical or hypothetical proposition

Q484156 - Converse County: county in Wyoming, United States

Nike,_Inc.

Q34653 - 1990s: decade of the Gregorian calendar (1990–1999)

Nike,_Inc.

Q8531 - 1996 Summer Olympics: Games of the XXVI Olympiad, in Atlanta, USA

Q431657 - 1996 Taiwanese presidential election: Republic of China presidential election

Q174397 - 1996 Summer Paralympics: 1996 edition of the Summer Paralympics

Nike,_Inc.

Nike,_Inc.

Output: None of the candidates


Child labor

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Child labor: During the 1990s, Nike faced criticism for the use ofchild labor in CambodiaandPakistanin factories it contracted to manufacture soccer balls. Although Nike took action to curb or at least reduce the practice, they continue to contract their production to companies that operate in areas where inadequate regulation and monitoring make it hard to ensure that child labor is not being used. In 2001, a BBC documentary uncovered occurrences of child labor and poor working conditions in a Cambodian factory used by Nike.The documentary focused on six girls, who all worked seven days a week, often 16 hours a day.

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q424 - Cambodia: country in Southeast Asia",
    "Q1057118 - Zhenla: former country",
    "Q9205 - Khmer: Austroasiatic language of Cambodia",
    "Q1028162 - Cambodia: song written and composed by Marty Wilde and Ricky Wilde, originally recorded by Kim Wilde and released in 1981",
    "Q106453161 - Cambodia: vocal track by Kim Wilde; 1981 studio recording"
]

[
    "Q9531 - British Broadcasting Corporation: British public service broadcaster",
    "Q216108 - BBC Two: television channel operated by the BBC",
    "Q795598 - BBC Radio 4: British national radio station",
    "Q1160945 - BBC News: news division of the publicly funded British Broadcasting Corporation",
    "Q687427 - BBC Three: Terrestrial television channel operated by the BBC until 2016"
]

[
    "Q424 - Cambodia: country in Southeast Asia",
    "Q1057118 - Zhenla: former country",
    "Q9205 - Khmer: Austroasiatic language of Cambodia",
    "Q1028162 - Cambodia: song written and composed by Marty Wilde and Ricky Wilde, originally recorded by Kim Wilde and released in 1981",
    "Q106453161 - Cambodia: vocal track by Kim Wilde; 1981 studio recording"
]

[
    "Q1988 - 2001: year",
    "Q841792 - 2001: natural number",
    "Q182280 - 2001: 1999 studio album by Dr. Dre",
    "Q103474 - 2001: A Space Odyssey: 1968 film by Stanley Kubrick",
    "Q10806 - September 11 attacks: 2001 attacks in the United States"
]

[
    "Q843 - Pakistan: sovereign state in South Asia",
    "Q2006542 - Dominion of Pakistan: period of Pakistani history between 1947 to 1956",
    "Q3310017 - Pakistanis: citizens and national of the Republic Pakistan",
    "Q2342822 - West Pakistan: Western Wing of Pakistan between 1947–1970",
    "Q15746372 - Pakistan Journal of Pharmaceutical Sciences: scientific journal"
]

[
    "Q12252328 - association football ball: spherical object used in association football tournament",
    "Q87527056 - ⚽: Unicode character",
    "Q46062111 - Soccer ball-related retinal injuries: a report of 13 cases: scientific article",
    "Q107388581 - Soccer ball lift coefficients via trajectory analysis: scientific article",
    "Q107388599 - Soccer Ball Aerodynamics: scientific article"
]

[
    "Q22967698 - Six Girls and a Room for the Night: 1928 film"
]

[
    "Q192571 - child labor: exploitation of children through any form of work",
    "Q114478473 - Child Labor: episode of Private Secretary (S1 E15)",
    "Q26323193 - Child labor: chapter published in 1992",
    "Q1377073 - child labour in India: child labour",
    "Q5097853 - child labour in Africa: overview about child labour in Africa"
]

[
    "Q22967698 - Six Girls and a Room for the Night: 1928 film"
]

[
    "Q34653 - 1990s: decade of the Gregorian calendar (1990–1999)",
    "Q266906 - 1990s: British indie rock band",
    "Q44071843 - 1990s: decade of the Ethiopian calendar",
    "Q2851969 - 1990s BC: decade",
    "Q60996409 - 1990s: 2018 studio album by Ann"
]


Child labor

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Child labor: During the 1990s, Nike faced criticism for the use ofchild labor in CambodiaandPakistanin factories it contracted to manufacture soccer balls. Although Nike took action to curb or at least reduce the practice, they continue to contract their production to companies that operate in areas where inadequate regulation and monitoring make it hard to ensure that child labor is not being used. In 2001, a BBC documentary uncovered occurrences of child labor and poor working conditions in a Cambodian factory used by Nike.The documentary focused on six girls, who all worked seven days a week, often 16 hours a day.

Nike,_Inc


Strike in China factory

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Strike in China factory: In April 2014, one of the biggest strikes in mainland China took place at theYue Yuen Industrial HoldingsDongguanshoe factory, producing amongst others for Nike. Yue Yuen did underpay an employee by 250 yuan (40.82 US Dollars) per month. The average salary at Yue Yuen is 3000 yuan per month. The factory employs 70,000 people. This practice was in place for nearly 20 years.

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q698467 - Yue Yuen Industrial Holdings: company"
]

[
    "Q59218 - Dongguan: prefecture-level city in Guangdong, China",
    "Q11103954 - Dongguan County: former county in China",
    "Q10869484 - Dongguan: subdistrict in Kangping County, Liaoning, China",
    "Q10869478 - Dongguan: township in Dafang County, Guizhou, China",
    "Q151641 - 3476 Dongguan: asteroid"
]

[
    "Q130240771 - 70,000: poem by Petr Bezruč",
    "Q10864465 - 70,000 Character Petition: 1997 essay by Choekyi Gyaltsen, 10th Panchen Lama",
    "Q19363531 - 70,000 Witnesses: 1932 film by Ralph Murphy",
    "Q17666674 - 70,000 General Motors employees go on strike: Wikinews article",
    "Q3271917 - 70000: number"
]

Output: None of the candidates

Output: None of the candidates


Strike in China factory

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Strike in China factory: In April 2014, one of the biggest strikes in mainland China took place at theYue Yuen Industrial HoldingsDongguanshoe factory, producing amongst others for Nike. Yue Yuen did underpay an employee by 250 yuan (40.82 US Dollars) per month. The average salary at Yue Yuen is 3000 yuan per month. The factory employs 70,000 people. This practice was in place for nearly 20 years.

Nike,_Inc

P1056 - product or material produced: material or product, including services, produced or provided by an organization, industry, facility, or process

P1672 - this taxon is source of: links a taxon to natural products it produces. Note that it does not say "this taxon is the source of" or "this taxon is a source of" as this may vary. Some products may be yielded by more than one taxon.

P4914 - produces cohesive end: overhang DNA sequence generated by restriction enzyme, written from 5' to 3'

P2821 - by-product: product of a chemical or industrial process, of secondary economic value

P272 - production company: company that produced this film, audio or performing arts work

Q698467 - Yue Yuen Industrial Holdings: company

Yue_Yuen_Industrial_Holdings

Q1515807 - Yehuda Shoenfeld: Israeli physician

Q152355 - cobbler: person who makes or repairs shoes

Yue_Yuen_Industrial_Holdings

P2139 - total revenue: income gained by an organization during a given time frame. Not to be confused with fiscal revenue

P2770 - source of income: source of income of an organization or person

Yue_Yuen_Industrial_Holdings

Yue_Yuen_Industrial_Holdings

Yue_Yuen_Industrial_Holdings

P1128 - employees: total number of employees of a company at a given "point in time" (P585). Most recent data would generally have preferred rank; data for previous years normal rank (not deprecated rank). Add data for recent years, don't overwrite

Q261254 - 70000 Tons of Metal: annual heavy metal music festival at sea

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Paradise Papers

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Paradise Papers: On November 5, 2017, theParadise Papers, a set of confidentialelectronic documentsrelating tooffshore investment, revealed that Nike is among the corporations that usedoffshore companiesto avoid taxes. Applebydocuments detail how Nike boosted its after-tax profits by, among other maneuvers, transferring ownership of its Swoosh trademark to a Bermudan subsidiary, Nike International Ltd. This transfer allowed the subsidiary to charge royalties to its European headquarters inHilversum,Netherlands, effectively converting taxable companyprofitsto an account payable intax-freeBermuda.Although the subsidiary was effectively run by executives at Nike's main offices in Beaverton, Oregon—to the point where a duplicate of the Bermudan company's seal was needed—for tax purposes the subsidiary was treated as Bermuda. Its profits were not declared in Europe and came to light only because of a mostly unrelated case in US Tax Court, where papers filed by Nike briefly mention royalties in 2010, 2011 and 2012 totaling $3.86 billion.Under an arrangement with Dutch authorities, the tax break was to expire in 2014, so another reorganization transferred the intellectual property from the Bermudan company to a Dutchcommanditaire vennootschapor limited partnership, Nike Innovate CV. Dutch law treats income earned by a CV as if it had been earned by the principals, who owe no tax in the Netherlands if they do not reside there.

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q42722842 - Paradise Papers: documents leak related to offshore investment",
    "Q42768099 - Paradise Papers: Novas revelações mostram vínculos de empresas, políticos e empresários com paraísos fiscais: Wikinews article",
    "Q48789577 - Paradise Papers en Argentina: None"
]

[
    "Q37788063 - November 5, 2017: Sunday in November 2017",
    "Q131607415 - 2017-11-05 Dynamo Kyiv vs Vorskla Poltava: association football match at NSC Olimpiyskyi during 2017–18 Ukrainian Premier League with score 2:1"
]

[
    "Q23635 - Bermuda: British overseas territory in the North Atlantic Ocean",
    "Q135060183 - Bermuda: video game developed by Short Sight Studios",
    "Q124063477 - Bermuda: 2015 video game",
    "Q303347 - bermuda shorts: knee-length walking shorts for men or women",
    "Q1542520 - Great Bermuda: island of Bermuda"
]

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q9934 - Hilversum: town and municipality in North Holland, the Netherlands",
    "Q5632754 - HMS Helverson: 1655 third-rate frigate",
    "Q49868 - Hilversum railway station: railway station in the Dutch city of Hilversum",
    "Q4358554 - Hilversum ring road: highway in Hilversum, the Netherlands",
    "Q3540474 - Hilversum Trophy: tennis tournament"
]

[
    "Q30 - United States: country located primarily in North America",
    "Q637413 - United States Census Bureau: U.S. agency responsible for the census and related statistics",
    "Q11703 - United States Virgin Islands: unincorporated territory of the United States of America",
    "Q4917 - United States dollar: official currency of the United States",
    "Q214867 - National Gallery of Art: national art museum in Washington, D.C."
]

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q653927 - Swoosh: Nike logo",
    "Q126488015 - Swoosh: 2003 steel sculpture by Carole Eisner on Syracuse University campus",
    "Q29733753 - ens: protein-coding gene in the species Drosophila melanogaster",
    "Q135073395 - SwooshCat: video game developed by PixelForestStudio",
    "Q130956041 - Swoosh, Pop, and Kerplunk: Toys for Tangible Listening in Children: scientific article published on 14 February 2023"
]

[
    "Q8161 - tax: mandatory financial charge or other type of levy imposed on a taxpayer (an individual or legal entity) by a governmental organization to collectively fund government spending, public expenditures, or to regulate and reduce negative externalities",
    "Q1964442 - taxation: levying of tax",
    "Q16102139 - levy: expenditure",
    "Q573607 - revenue service: government agency responsible for the intake of government revenue, tax administration in some jurisdiction",
    "Q10301577 - import tax: None"
]

Output: None of the candidates


Paradise Papers

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Paradise Papers: On November 5, 2017, theParadise Papers, a set of confidentialelectronic documentsrelating tooffshore investment, revealed that Nike is among the corporations that usedoffshore companiesto avoid taxes. Applebydocuments detail how Nike boosted its after-tax profits by, among other maneuvers, transferring ownership of its Swoosh trademark to a Bermudan subsidiary, Nike International Ltd. This transfer allowed the subsidiary to charge royalties to its European headquarters inHilversum,Netherlands, effectively converting taxable companyprofitsto an account payable intax-freeBermuda.Although the subsidiary was effectively run by executives at Nike's main offices in Beaverton, Oregon—to the point where a duplicate of the Bermudan company's seal was needed—for tax purposes the subsidiary was treated as Bermuda. Its profits were not declared in Europe and came to light only because of a mostly unrelated case in US Tax Court, where papers filed by Nike briefly mention royalties in 2010, 2011 and 2012 totaling $3.86 billion.Under an arrangement with Dutch authorities, the tax break was to expire in 2014, so another reorganization transferred the intellectual property from the Bermudan company to a Dutchcommanditaire vennootschapor limited partnership, Nike Innovate CV. Dutch law treats income earned by a CV as if it had been earned by the principals, who owe no tax in the Netherlands if they do not reside there.

Nike,_Inc.

P154 - logo image: graphic mark or emblem commonly used by commercial enterprises, organizations and products

P10120 - Australian Trade Mark Number: identifier for a trade mark registered in Australia

P10482 - U.S. trademark serial number: identifier of a trademark serial number issued by the United States Patent and Trademark Office

Q126488015 - Swoosh: 2003 steel sculpture by Carole Eisner on Syracuse University campus

Q29733753 - ens: protein-coding gene in the species Drosophila melanogaster

Q135073395 - SwooshCat: video game developed by PixelForestStudio

Q130956041 - Swoosh, Pop, and Kerplunk: Toys for Tangible Listening in Children: scientific article published on 14 February 2023

Nike,_Inc.

P6949 - announcement date: time of the first public presentation of a subject by the creator, of information by the media

Q42722842 - Paradise Papers: documents leak related to offshore investment

Q42768099 - Paradise Papers: Novas revelações mostram vínculos de empresas, políticos e empresários com paraísos fiscais: Wikinews article

Q48789577 - Paradise Papers en Argentina: None

Nike,_Inc.

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

P625 - coordinate location: geocoordinates of the subject. For Earth, please note that only the WGS84 geodetic datum is currently supported

P276 - location: location of the object, structure or event; use P131 to indicate the containing administrative entity, P8138 for statistical entities, or P706 for geographic entities; use P7153 for locations associated with the object

P937 - work location: location where persons or organisations were actively participating in employment, business or other work

P3005 - valid in place: place where a statement is valid

P131 - located in the administrative territorial entity: the item is located on the territory of the following administrative entity. Use P276 for specifying locations that are non-administrative places and for items about events. Use P1382 if the item falls only partially into the administrative entity.

Q9934 - Hilversum: town and municipality in North Holland, the Netherlands

Q5632754 - HMS Helverson: 1655 third-rate frigate

Q49868 - Hilversum railway station: railway station in the Dutch city of Hilversum

Q4358554 - Hilversum ring road: highway in Hilversum, the Netherlands

Q3540474 - Hilversum Trophy: tennis tournament

Nike,_Inc.

Nike,_Inc.

P2855 - VAT rate: percentage value-added tax in this country or region. Do not use for sales tax. For specialised rates, use qualifiers

Q23635 - Bermuda: British overseas territory in the North Atlantic Ocean

Q135060183 - Bermuda: video game developed by Short Sight Studios

Q303347 - bermuda shorts: knee-length walking shorts for men or women

Q1542520 - Great Bermuda: island of Bermuda

Nike,_Inc.

Nike,_Inc.

Q621540 - Appleby-in-Westmorland: town and civil parish in Cumbria, England, UK

Q60578251 - Appleby: Constituency of the Parliament of England (to 1707)

Q2545336 - Appleby Magna: village and civil parish in Leicestershire, England, UK

Nike,_Inc.

Q81856 - Beaverton: city in Washington County, Oregon, United States

Nike,_Inc.

Q55 - Netherlands: country in Northwestern Europe with territories in the Caribbean

Q29999 - Kingdom of the Netherlands: transcontinental sovereign state and constitutional monarchy

Q476033 - Low Countries: historical coastal landscape in north western Europe

Q16250490 - Netherlands: constituency of the European Parliament

Q170072 - Dutch Republic: predecessor state of the Netherlands (1581–1795)

Nike,_Inc.

Q523716 - 2010 United States Census: national census of the United States taken in 2010

Q92953148 - 2010 United States Census surname index: table of surnames with 100 or more occurrences in the 2010 US census

Q107569809 - 2010 U.S. Gazetteer Files: geographical census data for regions in the United States

Nike,_Inc.

Q42501043 - 2011 Census data  Andhra Pradesh - Villages dataset: 2011 census data Andhra Pradesh - Villages (dataset)

Q1861882 - 2011 census of India: 15th Indian census

Q4350770 - 2011 Population and Housing Census: census in Estonia in 2011

Nike,_Inc.

Q8577 - 2012 Summer Olympics: Games of the XXX Olympiad, in London, United Kingdom

Q184605 - 2012: 2009 film directed by Roland Emmerich

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Colin Kaepernick

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Colin Kaepernick: In September 2018, Nike announced it had signed former American football quarterbackColin Kaepernick, noted for hiscontroversial decision to kneelduring the playing of the US national anthem, to a long-term advertising campaign.According to Charles Robinson ofYahoo! Sports, Kaepernick and Nike agreed to a new contract despite the fact Kaepernick has been with the company since 2011 and said that "interest from other shoe companies" played a part in the new agreement. Robinson said the contract is a "wide endorsement" where Kaepernick will have his own branded line including shoes, shirts, jerseys and more.In response, some people set fire to their own Nike-branded clothes and shoes or cut the Nike swoosh logo out of their clothes, and theFraternal Order of Policecalled the advertisement an "insult";others, such asLeBron James,Serena Williams,and theNational Black Police Association,praised Nike for its campaign. TheCollege of the Ozarksremoved Nike from all their athletic uniforms in response. During the following week, Nike's stock price fell 2.2%, even as online orders of Nike products rose 27% compared with the previous year.In the following three months, Nike reported a rise in sales. In July 2019, Nike released a shoe featuring aBetsy Ross flagcalled the Air Max 1 Quick Strike Fourth of July trainers. The trainers were designed to celebrateIndependence Day. The model was subsequently withdrawn afterColin Kaepernicktold the brand he and others found the flag offensive because of its association with slavery. Nike's decision to withdraw the product drew criticism fromArizona'sRepublican Governor,Doug Ducey, and Texas's Republican SenatorTed Cruz.Nike's decision was praised by others due to the use of the flag bywhite nationalists,but theAnti-Defamation League'sCenter on Extremism has declined to add the flag to its database of "hate symbols."

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q82496 - Colin Kaepernick: American football player (born 1987)",
    "Q123417993 - Colin Kaepernick collectie: None"
]

[
    "Q48769327 - Air Max 1: None",
    "Q1152291 - Nike Air Max: line of shoes released by Nike"
]

[
    "Q4898324 - Betsy Ross flag: flag design of 1776 by Betsy Ross"
]

[
    "Q45921181 - 3 September 2018: Monday in September 2018"
]

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q30 - United States: country located primarily in North America",
    "Q637413 - United States Census Bureau: U.S. agency responsible for the census and related statistics",
    "Q11703 - United States Virgin Islands: unincorporated territory of the United States of America",
    "Q4917 - United States dollar: official currency of the United States",
    "Q214867 - National Gallery of Art: national art museum in Washington, D.C."
]

[
    "Q6971028 - National Black Police Association: None",
    "Q6971029 - National Black Police Association: None"
]

[
    "Q1451416 - Fraternal Order of Police: US fraternal organization of law enforcement officers",
    "Q49491612 - Fraternal Order of Police Park: park in Wayne County, Michigan, United States of America",
    "Q131849106 - Fraternal Order of Police 218 Camden County Police: None"
]

[
    "Q36159 - LeBron James: American basketball player (born 1984)",
    "Q108870659 - LeBron James: main protagonist in the 2021 film Space Jam: A New Legacy based on the real-life LeBron James",
    "Q1035043 - lebron james: species of plant",
    "Q22302425 - Bronny James: American basketball player (born 2004)",
    "Q110539680 - James family: fictional family from Space Jam: A New Legacy"
]

[
    "Q11459 - Serena Williams: American tennis player (1981)",
    "Q3488059 - Serena Williams career statistics: tennis statistics of Serena Williams",
    "Q17166057 - Serena Williams' early career: None",
    "Q124025330 - Serena Williams at the 2013 US Open: None",
    "Q124026503 - Serena Williams at the 2011 AEGON International: None"
]

[
    "Q7896711 - University of the Ozarks: private university in Clarksville, Arkansas",
    "Q5146892 - College of the Ozarks: liberal-arts college in Point Lookout, Missouri"
]

[
    "Q158818 - Anti-Defamation League: international Jewish non-governmental organization",
    "Q113951518 - Anti-Defamation League Nevada Region Office Records: contains photographs, advertisements, and invitations of various Anti-Defamation League (ADL) Nevada Region Office events",
    "Q67611060 - ANTI-DEFAMATION LEAGUE, WASHINGTON, D.C (NAID 12196): item in the National Archives and Records Administration's holdings",
    "Q67608074 - ANTI-DEFAMATION LEAGUE DINNER WITH PRESIDENT DWIGHT D. EISENHOWER (NAID 119729): item in the National Archives and Records Administration's holdings",
    "Q67609656 - ANTI DEFAMATION LEAGUE DINNER INTERVIEW WITH FRANCHOT TONE AT RECEPTION (NAID 120804): item in the National Archives and Records Administration's holdings"
]

[
    "Q5300433 - Doug Ducey: American businessman and politician (born 1964)"
]

[
    "Q2036942 - Ted Cruz: American politician (born 1970)",
    "Q19881031 - Ted Cruz presidential campaign, 2016: 2016 presidential campaign of Ted Cruz, US Senator from Texas",
    "Q113430289 - Ted Cruz aide's firm targets McCain, prompting tensions with GOP: news article",
    "Q24908818 - Ted Cruz–Zodiac meme: mock conspiracy theory",
    "Q110085822 - Ted Cruz | The Ben Shapiro Show Sunday Special Ep. 54: episode of the Ben Shapiro Show"
]

[
    "Q45921190 - 4 September 2018: Tuesday in September 2018"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Colin Kaepernick

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Colin Kaepernick: In September 2018, Nike announced it had signed former American football quarterbackColin Kaepernick, noted for hiscontroversial decision to kneelduring the playing of the US national anthem, to a long-term advertising campaign.According to Charles Robinson ofYahoo! Sports, Kaepernick and Nike agreed to a new contract despite the fact Kaepernick has been with the company since 2011 and said that "interest from other shoe companies" played a part in the new agreement. Robinson said the contract is a "wide endorsement" where Kaepernick will have his own branded line including shoes, shirts, jerseys and more.In response, some people set fire to their own Nike-branded clothes and shoes or cut the Nike swoosh logo out of their clothes, and theFraternal Order of Policecalled the advertisement an "insult";others, such asLeBron James,Serena Williams,and theNational Black Police Association,praised Nike for its campaign. TheCollege of the Ozarksremoved Nike from all their athletic uniforms in response. During the following week, Nike's stock price fell 2.2%, even as online orders of Nike products rose 27% compared with the previous year.In the following three months, Nike reported a rise in sales. In July 2019, Nike released a shoe featuring aBetsy Ross flagcalled the Air Max 1 Quick Strike Fourth of July trainers. The trainers were designed to celebrateIndependence Day. The model was subsequently withdrawn afterColin Kaepernicktold the brand he and others found the flag offensive because of its association with slavery. Nike's decision to withdraw the product drew criticism fromArizona'sRepublican Governor,Doug Ducey, and Texas's Republican SenatorTed Cruz.Nike's decision was praised by others due to the use of the flag bywhite nationalists,but theAnti-Defamation League'sCenter on Extremism has declined to add the flag to its database of "hate symbols."

Nike,_Inc.

Q1152291 - Nike Air Max: line of shoes released by Nike

Nike,_Inc.

Nike,_Inc.

Q125908302 - Air Max 270: shoe line by Nike

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q116031595 - Air Max: 2020 single by Comethazine

Q4698092 - Air Max Africa: airline from Gabon

Q2801840 - Air Maxi: low-cost airline from Serbia and Montenegro

Q65326219 - Nike Air Max 90: line of shoes released by Nike

Nike,_Inc.

Q407113 - Air Force 1: athletic shoe by Nike

Q2828193 - Air Force 1: album by The High & Mighty

Q13655801 - Air Force 1st: community in Haidian District, Beijing

Q179969 - Air Force One: official United States presidential aircraft

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Air_Force_1_270

Air_Force_1_270

Air_Force_1_270

Air_Force_1_270

Air_Force_1_270

Air_Force_1_270


Hong Kong protests

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Hong Kong protests: U.S. Vice PresidentMike Pencecriticized Nike for "siding with theChinese Communist Partyand silencing free speech". He claimed that afterHouston Rocketsgeneral managerDaryl Moreywas criticized by the Chinese government forhis tweetsupporting the2019 Hong Kong protests, Nike removed Rockets merchandise from its stores in China. On January 31, 2020, theWorld Athleticsissued new guidelines concerning shoes to be used in the upcomingTokyo 2020 Olympics.These updates came in response to criticisms concerning technology in the Nike Vaporfly running shoes, which had been submitted beginning around 2017–2018.These criticisms stated that the shoes provided athletes with an unfair advantage over their opponents and some critics considered it to be a form oftechnology doping.According to Nike funded research, the shoes can improve efficiency by up to 4.2%and runners who have tested the shoe are saying that it causes reduced soreness in the legs; sports technologist Bryce Dyer attributes this to the ZoomX and carbon fiber plate since it absorbs the energy and "spring[s] runners forward".Some athletes, scientists, and fans have compared this to the 2008LAZR swimsuit controversy. Some of the major changes in the guidelines that have come about as a result of these criticisms include that the "sole must be no thicker than 40mm" and that "the shoe must not contain more than one rigid embedded plate or blade (of any material) that runs either the full length or only part of the length of the shoe. The plate may be in more than one part but those parts must be located sequentially in one plane (not stacked or in parallel) and must not overlap". The components of the shoes are not the only thing that had major changes; starting April 30, 2020, "any shoe must have been available for purchase by any athlete on the open retail market (online or in store) for a period of four months before it can be used in competition".Prior to these new guidelines World Athletics reviewed the Vaporfly shoes and "concluded that there is independent research that indicates that the new technology incorporated in the soles of road and spiked shoes may provide a performance advantage" and that it recommends further research to "establish the true impact of [the Vaporfly] technology."

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q161345 - Houston Rockets: National Basketball Association team in Houston, Texas",
    "Q16995995 - Houston Rockets all-time roster: Wikimedia list article",
    "Q5869510 - history of the Houston Rockets: aspect of history",
    "Q123653553 - Houston Rockets vs Washington Wizards, 7 November 2016: None",
    "Q123653295 - Houston Rockets vs Toronto Raptors, 2 January 2009: None"
]

[
    "Q24313 - Mike Pence: Vice President of the United States from 2017 to 2021",
    "Q119104914 - Mike Pence 2024 presidential campaign: presidential campaign of Mike Pence",
    "Q116406924 - Mike Pence classified documents incident: 2023-present political incident",
    "Q130473842 - Mike Pence's fly: Individual fly",
    "Q131852102 - Mike Pence, Joe Rogan, tech CEOs attend Trump's inauguration: None"
]

[
    "Q64509602 - 2019–2020 Hong Kong protests: pro-democracy demonstrations and other civil disobedience sparked by a proposed extradition bill in Hong Kong"
]

[
    "Q96395644 - Nike Vaporfly and Tokyo 2020 Olympics Controversy: clothing controversy in sports"
]

[
    "Q1158 - World Athletics: international sports governing body organizing track and field athletics",
    "Q54960205 - World Athletics database: online sports database from World Athletics",
    "Q182653 - World Athletics Championships: biennial international athletics competition",
    "Q1141381 - World Athletics Cross Country Championships: competition in international cross country running",
    "Q725169 - World Athletics Indoor Championships: international track and field athletics indoor competitions"
]

[
    "Q181278 - 2020 Summer Olympics: games of the XXXII Olympiad, in Tokyo, Japan, held in 2021"
]

[
    "Q96395644 - Nike Vaporfly and Tokyo 2020 Olympics Controversy: clothing controversy in sports"
]

[
    "Q1158 - World Athletics: international sports governing body organizing track and field athletics",
    "Q54960205 - World Athletics database: online sports database from World Athletics",
    "Q182653 - World Athletics Championships: biennial international athletics competition",
    "Q1141381 - World Athletics Cross Country Championships: competition in international cross country running",
    "Q725169 - World Athletics Indoor Championships: international track and field athletics indoor competitions"
]

[
    "Q98712705 - 2017-2018 Massachusetts legislature: 190th Massachusetts General Court",
    "Q47015413 - 2017-2018 one-year-period: period of about one year starting in 2017 and ending in 2018",
    "Q29433368 - 2017–18 Russian Premier League: 26th season of top-tier football league in Russia",
    "Q43138931 - 2017–18 Euro Hockey Tour: 2017–2018 season of the Euro Hockey Tour",
    "Q44673007 - 2017–2018 Tour de Ski: 2017–2018 season of the Tour de Ski"
]

[
    "Q1158 - World Athletics: international sports governing body organizing track and field athletics",
    "Q54960205 - World Athletics database: online sports database from World Athletics",
    "Q182653 - World Athletics Championships: biennial international athletics competition",
    "Q1141381 - World Athletics Cross Country Championships: competition in international cross country running",
    "Q725169 - World Athletics Indoor Championships: international track and field athletics indoor competitions"
]

[
    "Q910977 - carbon-fiber-reinforced polymer: extremely strong and light fiber-reinforced polymer",
    "Q5860 - carbon fibers: material fibers about 5–10 μm in diameter composed of carbon",
    "Q28923891 - Carbon Fiber Music: American record label",
    "Q56224083 - carbon fiber + epoxy: composite materials class",
    "Q228252 - carbon fiber testing: set of various different tests for carbon fiber"
]

[
    "Q2066131 - athlete: person who participates regularly in a sport",
    "Q11513337 - athletics competitor: sportsperson that competes in athletics (track and field, running, walking)",
    "Q58840094 - sportsperson who competes in this club or team: Wikidata metaclass",
    "Q580268 - Athlete: English indie rock band",
    "Q57503980 - Mahfujur Rahman: researcher ORCID ID = 0000-0002-6495-4407"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Hong Kong protests

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Hong Kong protests: U.S. Vice PresidentMike Pencecriticized Nike for "siding with theChinese Communist Partyand silencing free speech". He claimed that afterHouston Rocketsgeneral managerDaryl Moreywas criticized by the Chinese government forhis tweetsupporting the2019 Hong Kong protests, Nike removed Rockets merchandise from its stores in China. On January 31, 2020, theWorld Athleticsissued new guidelines concerning shoes to be used in the upcomingTokyo 2020 Olympics.These updates came in response to criticisms concerning technology in the Nike Vaporfly running shoes, which had been submitted beginning around 2017–2018.These criticisms stated that the shoes provided athletes with an unfair advantage over their opponents and some critics considered it to be a form oftechnology doping.According to Nike funded research, the shoes can improve efficiency by up to 4.2%and runners who have tested the shoe are saying that it causes reduced soreness in the legs; sports technologist Bryce Dyer attributes this to the ZoomX and carbon fiber plate since it absorbs the energy and "spring[s] runners forward".Some athletes, scientists, and fans have compared this to the 2008LAZR swimsuit controversy. Some of the major changes in the guidelines that have come about as a result of these criticisms include that the "sole must be no thicker than 40mm" and that "the shoe must not contain more than one rigid embedded plate or blade (of any material) that runs either the full length or only part of the length of the shoe. The plate may be in more than one part but those parts must be located sequentially in one plane (not stacked or in parallel) and must not overlap". The components of the shoes are not the only thing that had major changes; starting April 30, 2020, "any shoe must have been available for purchase by any athlete on the open retail market (online or in store) for a period of four months before it can be used in competition".Prior to these new guidelines World Athletics reviewed the Vaporfly shoes and "concluded that there is independent research that indicates that the new technology incorporated in the soles of road and spiked shoes may provide a performance advantage" and that it recommends further research to "establish the true impact of [the Vaporfly] technology."

Nike,_Inc.

Q96395644 - Nike Vaporfly and Tokyo 2020 Olympics Controversy: clothing controversy in sports

Nike,_Inc.

P1476 - title: published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work

P793 - significant event: significant or notable events associated with the subject

P2561 - name: name the subject is known by; preferably use a more specific subproperty if available

P800 - notable work: notable scientific, artistic or literary work, or other work of significance among subject's works

P1317 - floruit: date when the person was known to be active or alive, when birth or death not documented

Q161345 - Houston Rockets: National Basketball Association team in Houston, Texas

Q16995995 - Houston Rockets all-time roster: Wikimedia list article

Q5869510 - history of the Houston Rockets: aspect of history

Q123653553 - Houston Rockets vs Washington Wizards, 7 November 2016: None

Q123653295 - Houston Rockets vs Toronto Raptors, 2 January 2009: None

Nike,_Inc.

Q64509602 - 2019–2020 Hong Kong protests: pro-democracy demonstrations and other civil disobedience sparked by a proposed extradition bill in Hong Kong

Nike,_Inc.

Q24313 - Mike Pence: Vice President of the United States from 2017 to 2021

Q119104914 - Mike Pence 2024 presidential campaign: presidential campaign of Mike Pence

Q116406924 - Mike Pence classified documents incident: 2023-present political incident

Q130473842 - Mike Pence's fly: Individual fly

Q131852102 - Mike Pence, Joe Rogan, tech CEOs attend Trump's inauguration: None

Nike,_Inc.

Q1158 - World Athletics: international sports governing body organizing track and field athletics

Q54960205 - World Athletics database: online sports database from World Athletics

Q182653 - World Athletics Championships: biennial international athletics competition

Q1141381 - World Athletics Cross Country Championships: competition in international cross country running

Q725169 - World Athletics Indoor Championships: international track and field athletics indoor competitions

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q910977 - carbon-fiber-reinforced polymer: extremely strong and light fiber-reinforced polymer

Q5860 - carbon fibers: material fibers about 5–10 μm in diameter composed of carbon

Q28923891 - Carbon Fiber Music: American record label

Q56224083 - carbon fiber + epoxy: composite materials class

Q228252 - carbon fiber testing: set of various different tests for carbon fiber

Nike,_Inc.

Nike,_Inc.

Q2066131 - athlete: person who participates regularly in a sport

Q11513337 - athletics competitor: sportsperson that competes in athletics (track and field, running, walking)

Q58840094 - sportsperson who competes in this club or team: Wikidata metaclass

Q580268 - Athlete: English indie rock band

Q57503980 - Mahfujur Rahman: researcher ORCID ID = 0000-0002-6495-4407

Nike,_Inc.

Q192864 - Science: academic journal of the American Association for the Advancement of Science

Q336 - science: systematic endeavor that builds and organizes knowledge, and the set of knowledge produced by this system

Q859363 - Sciences Po: higher-education institution in Paris, France

Q7991 - natural science: branch of science about the natural world

Q5633421 - scientific journal: periodical journal publishing scientific research

Nike,_Inc.

Q2312877 - sports technology: field of work in sports and engineering sciences

Q104871438 - Sports Technology and Innovation Group: UK advisory group

Nike,_Inc.

Nike,_Inc.

Q57396723 - April 30, 2020: Thursday in April 2020

Nike,_Inc.

Q7692598 - technology doping: practice of gaining a competitive advantage using sports equipment

Nike,_Inc.

Q17427 - Chinese Communist Party: founding and ruling party of the People's Republic of China

Q97185636 - Chinese Communist Party Provincial Standing Committee: top ranks of the provincial-level organizations of the Chinese Communist Party (CCP)

Q30060320 - Chinese Communist Party Committee Secretary of Shaanxi: None

Q23373938 - Chinese Communist Party Committee Secretary of Hunan: None

Q10873174 - Shanghai Municipal Committee of the Chinese Communist Party: political body comprising the top leaders of Shanghai

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Forced Uyghur labor allegations

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Forced Uyghur labor allegations: In December 2021, theEuropean Center for Constitutional and Human Rightsfiled a criminal complaint in a Dutch court against Nike and other brands, alleging that they benefited from the use of forcedUyghurlabor inXinjiang.In July 2023, theCanadian Ombudsperson for Responsible Enterpriseopened an investigation into Nike to probe allegations of forced Uyghur labor in its supply chain.Research of the social democratic party in the European Parliament, the Sheffield Hallam University and further groups accused Nike in 2023 of using forced labor camps exploiting Muslim Uyghurs in China provided by the Anhui Huamao Group Co., Ltd. for production.

[
    "Q483915 - Nike: American athletic equipment company"
]

[
    "Q1376405 - European Center for Constitutional and Human Rights: human rights organization based in Berlin",
    "Q131845668 - European Center for Constitutional and Human Rights e.V.: None"
]

[
    "Q16 - Canada: country in North America",
    "Q14624136 - Canada: moth genus of Pteromalidae",
    "Q13265725 - Canada: family name",
    "Q54856571 - Canada: unisex given name",
    "Q2003 - Newfoundland and Labrador: province of Canada"
]

[
    "Q69306290 - December 31, 2021: date in the Gregorian calendar"
]

[
    "Q1752117 - Sheffield Hallam University: university in Sheffield, United Kingdom",
    "Q101045692 - Sheffield Hallam University Psychology: academic department",
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Forced Uyghur labor allegations

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Forced Uyghur labor allegations: In December 2021, theEuropean Center for Constitutional and Human Rightsfiled a criminal complaint in a Dutch court against Nike and other brands, alleging that they benefited from the use of forcedUyghurlabor inXinjiang.In July 2023, theCanadian Ombudsperson for Responsible Enterpriseopened an investigation into Nike to probe allegations of forced Uyghur labor in its supply chain.Research of the social democratic party in the European Parliament, the Sheffield Hallam University and further groups accused Nike in 2023 of using forced labor camps exploiting Muslim Uyghurs in China provided by the Anhui Huamao Group Co., Ltd. for production.

Nike,_Inc.