Causes

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, Causes: In 2012, Nike is listed as a partner of the(PRODUCT)REDcampaign together with other brands such asGirl,American Express, andConverse. The campaign's mission is to prevent the transmission ofHIV from mother to child. The campaign's byline is "Fighting For An AIDS Free Generation". The company's goal is to raise and send funds, for education and medical assistance to those who live inareas heavily affected by AIDS.In 2023, Nike became the presenting sponsor ofReviving Baseball in Inner Cities, which encourages youth in underserved communities to participate in baseball and softball.

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

[
    "Q17010333 - Reviving Baseball in Inner Cities: organization"
]

[
    "Q17010333 - Reviving Baseball in Inner Cities: organization"
]

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

[
    "Q624601 - Product Red: company"
]

[
    "Q624601 - Product Red: company"
]

[
    "Q194360 - American Express: American multinational financial services corporation",
    "Q24228273 - American Express credit card: credit card issued by American Express",
    "Q77551558 - American Express: painting by Louis Cameron",
    "Q3803483 - American Express – TED Open: tennis tournament",
    "Q619409 - Brighton Community Stadium: association football stadium in Brighton & Hove"
]

[
    "Q6581072 - female: to be used in \"sex or gender\" (P21) to indicate that the human subject is a female or \"semantic gender\" (P10339) to indicate that a word refers to a female person",
    "Q3031 - girl: young female human",
    "Q51843955 - Girl: 2018 drama film by Lukas Dhont",
    "Q60649975 - Girl: vocal track by The Beatles; 1965 studio recording",
    "Q37567403 - Girl: family name"
]

[
    "Q15787 - HIV: human retrovirus, cause of AIDS",
    "Q12199 - AIDS: spectrum of conditions caused by HIV infection",
    "Q187440 - urticaria: skin disease characterized by skin rash notable for pale red, raised and itchy bumps, located in the upper dermis",
    "Q7242372 - prevention of HIV/AIDS: prevent and minimize the occurrence of HIV and AIDS",
    "Q49029021 - HIV transmission: viral transmission"
]

[
    "Q12199 - AIDS: spectrum of conditions caused by HIV infection",
    "Q4651863 - AIDS: peer-reviewed scientific journal",
    "Q15787 - HIV: human retrovirus, cause of AIDS",
    "Q7242372 - prevention of HIV/AIDS: prevent and minimize the occurrence of HIV and AIDS",
    "Q4651894 - death from AIDS-related complications: cause of death"
]

[
    "Q1990 - 2012: year",
    "Q1489579 - 2012: natural number",
    "Q8577 - 2012 Summer Olympics: Games of the XXX Olympiad, in London, United Kingdom",
    "Q29033603 - 2012: 2016 EP by Chixdiggit",
    "Q184605 - 2012: 2009 film directed by Roland Emmerich"
]


Causes

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, Causes: In 2012, Nike is listed as a partner of the(PRODUCT)REDcampaign together with other brands such asGirl,American Express, andConverse. The campaign's mission is to prevent the transmission ofHIV from mother to child. The campaign's byline is "Fighting For An AIDS Free Generation". The company's goal is to raise and send funds, for education and medical assistance to those who live inareas heavily affected by AIDS.In 2023, Nike became the presenting sponsor ofReviving Baseball in Inner Cities, which encourages youth in underserved communities to participate in baseball and softball.

Nike,_Inc.

Q483915 - Nike: American athletic equipment company

P102 - member of political party: the political party of which a person is or has been a member or otherwise affiliated

P1107 - proportion: to be used as a qualifier, value must be between 0 and 1

P1433 - published in: larger work that a given work was published in, like a journal, a website, a collection, a book or a music album

P478 - volume: volume of a book or music release in a collection/series or a published collection of journal issues in a serial publication

P59 - constellation: the area of the celestial sphere of which the subject is a part (from a scientific standpoint, not an astrological one)

Q17010333 - Reviving Baseball in Inner Cities: organization

Nike,_Inc.

Product_(RED)

P9588 - causesanti.va ID: identifier for a Catholic blessed or a Catholic saint in the database causesanti.va by the Congregation for the Causes of Saints

Q7242372 - prevention of HIV/AIDS: prevent and minimize the occurrence of HIV and AIDS

Q63583659 - Prevention of HIV/AIDS and STDs in Women Over Fifty: clinical trial

Q44756520 - Prevention of HIV/AIDS and other blood-borne diseases among injection drug users. A national survey on the regulation of syringes and needles: scientific article published in January 1997

Q80984065 - Prevention of HIV/AIDS among injecting drug users in Russia: opportunities and barriers to scaling-up of harm reduction programmes: scientific article published on 04 September 2007

Q35879979 - Prevention of HIV/AIDS in Native American communities: promising interventions.: scientific article published in January 2002

Product_(RED)

P26 - spouse: the subject has the object as their spouse (husband, wife, partner, etc.). Use "unmarried partner" (P451) for non-married companions

P451 - unmarried partner: someone with whom the person is in a relationship without being married. Use "spouse" (P26) for married couples

P1327 - partner in business or sport: professional collaborator

P190 - twinned administrative body: twin towns, sister cities, twinned municipalities and other localities that have a partnership or cooperative agreement, either legally or informally acknowledged by their governments

P2652 - partnership with: partnership (commercial or/and non-commercial) between this organization and another organization or institution

Q194360 - American Express: American multinational financial services corporation

Q24228273 - American Express credit card: credit card issued by American Express

Q77551558 - American Express: painting by Louis Cameron

Q3803483 - American Express – TED Open: tennis tournament

Q619409 - Brighton Community Stadium: association football stadium in Brighton & Hove

Product_(RED)

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

Product_(RED)

Q6581072 - female: to be used in "sex or gender" (P21) to indicate that the human subject is a female or "semantic gender" (P10339) to indicate that a word refers to a female person

Q51843955 - Girl: 2018 drama film by Lukas Dhont

Q60649975 - Girl: vocal track by The Beatles; 1965 studio recording

Nike,_Inc.

Nike,_Inc.

P3831 - object of statement has role: (qualifier) role or generic identity of the predicate value/argument of a statement ("object") in the context of that statement; for the role of the item the statement is on ("subject"), use P2868

P2868 - subject has role: role/generic identity of the item ("subject") in the context of a statement. For the role of the value of the statement ("object"), use P3831 ("object has role"). For acting roles, use P453 ("character role"). For persons, use P39.

P366 - has use: main use of the subject (includes current and former usage)

P453 - character role: specific role played or filled by subject -- use only as qualifier of "cast member" (P161), "voice actor" (P725)

P737 - influenced by: the subject (person, idea, etc.) was influenced or inspired by this object entity, e.g. "Heidegger was influenced by Aristotle"

Q125757711 - presenting sponsor: sponsorship level

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Program

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, Program: The Nike Community Ambassador Program, allows Nike employees from around the world to go out and give to their community. Over 3,900 employees from various Nike stores have participated in teaching children to be active and healthy.

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

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

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


Program

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, Program: The Nike Community Ambassador Program, allows Nike employees from around the world to go out and give to their community. Over 3,900 employees from various Nike stores have participated in teaching children to be active and healthy.

Nike,_Inc

Nike_Community_Ambassador_Program

Q483915 - Nike: American athletic equipment company

Nike_Community_Ambassador_Program

P1132 - number of participants: number of participants of an event, e.g. people or groups of people that take part in the event (NO units)

Output: None of the candidates


Research

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, Research: In 2016, a study done by RTG Consulting Group reflected that Nike was the 3rd most relevant brand forGen-ZinChina. Roth MKM's 2023Millennialsurvey reported in March that millennials with health and wellness concerns in the aftermath of thepandemicranked brands like Nike,AdidasandLululemonas their preferred brands for purchases. In January 2023, a study byRakutenconcluded that Nike was themost popularsportswearbrand in the US, followed by Lululemon and Adidas. In July 2023, a study byKantarfound that Americans consider Nike as the Most Inclusive Brands (alongside other top brands likeAmazon, andDisney).

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

[
    "Q3895 - Adidas AG: German multinational corporation",
    "Q12358242 - Adidas: trade mark of Adidas AG",
    "Q2824396 - Adidas Power Soccer: 1996 video game",
    "Q4682709 - Adidas Championship Football: 1990 video game",
    "Q113884442 - Adidas Power Soccer International '97: 1997 video game"
]

[
    "Q124965942 - lululemon: athletic apparel brand owned by Lululemon Athletica",
    "Q6702957 - Lululemon Athletica: Canadian athletic apparel retailer",
    "Q20273352 - Lululemon murder: 2011 murder",
    "Q132086507 - Lululemon Athletica Gmbh: None"
]

[
    "Q973054 - Rakuten Group, Inc.: Japanese e-commerce company",
    "Q135024343 - Rakuten: 2021 video game developed by TheRealDan",
    "Q1375077 - Tohoku Rakuten Golden Eagles: Nippon Professional Baseball team in the Pacific League",
    "Q78851123 - Rakuten Monkeys: professional baseball team in the Chinese Professional Baseball League in Taiwan",
    "Q332707 - Viber: cross-platform instant messaging voice-over-Internet Protocol application for smartphones"
]

[
    "Q6365337 - Kantar: village in Gercüş, Batman, Turkey",
    "Q3676311 - Kantar: village in Nusaybin, Mardin Province, Turkey",
    "Q10776572 - Kantar: neighborhood in Söğütlü, Sakarya, Turkey",
    "Q135471002 - kantar: None",
    "Q1140929 - Kantar Group: business division, subsidiary company of WPP"
]

[
    "Q25245 - 2016: year",
    "Q12681716 - 2016: natural number",
    "Q111610221 - 2016–2020 American Community Survey: American Community Survey on 5-year basis, data aggregated between 2016 and 2020",
    "Q32859555 - 2016 U.S. Gazetteer Files: geographical census data for regions in the United States",
    "Q33128519 - Australian Census 2016: 17th census of the population of Australia"
]

[
    "Q1987589 - Generation Z: cohort succeeding Millennials, born from 1997 to 2012",
    "Q103426418 - GEN Z Project: full hydrogen-powered vehicle",
    "Q28448286 - Gen-Z: trade group of technology vendors",
    "Q127197418 - 2010s kids: cuspers between Generation Z and Generation Alpha",
    "Q83141696 - Gen Zhang: researcher (ORCID 0000-0001-5948-8635)"
]

[
    "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."
]

[
    "Q5690781 - Health and Wellness Center: recreation Center in West Virginia, USA",
    "Q63598436 - Health and Wellness in Healthcare Providers Post-disaster: clinical trial",
    "Q112948122 - Health and wellness of Royal Navy personnel : trial of a health & wellness programme for the Royal New Zealand Navy: 2019 master's thesis by Chance Rapheal Crawford-Mickleson at Massey University",
    "Q108670952 - Health and Wellness Business School: None",
    "Q64219802 - Health and Wellness in Obesity: clinical trial"
]

[
    "Q12184 - pandemic: epidemic of infectious disease that has spread across a large region with global health impact",
    "Q114944723 - Pandemic: The Board Game: 2018 video game",
    "Q2723958 - influenza pandemic: epidemic of an influenza virus that spreads on a worldwide scale and infects a large proportion of the human population",
    "Q531592 - Pandemic: 2008 cooperative board game",
    "Q28299 - Pandemic Studios: former American independent video game developer"
]

[
    "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."
]

[
    "Q3884 - Amazon: American multinational technology company",
    "Q40040 - Amazonas: state in the North Region of Brazil",
    "Q456065 - Amazon: 1984 video game",
    "Q4740856 - Amazon Prime Video: American video streaming service",
    "Q177567 - Amazon rainforest: large rainforest in South America"
]

[
    "Q7414 - The Walt Disney Company: American multinational mass media company",
    "Q8704 - Walt Disney: American animator, producer, director, screenwriter, voice actor and entrepreneur, founder of The Walt Disney Company (1901–1966)",
    "Q54958752 - Disney+: American video streaming service",
    "Q178837 - Disney Channel: US youth-targeted television channel owned by the Walt Disney Company",
    "Q3260264 - Disney: town in Mayes County, Oklahoma, USA"
]

Output: None of the candidates


External links

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, External links: 45°30′33″N122°49′48″W / 45.5093°N 122.8299°W /45.5093; -122.8299

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


External links

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, External links: 45°30′33″N122°49′48″W / 45.5093°N 122.8299°W /45.5093; -122.8299

Nike,_Inc.

Q41641739 - Athletic shoes: scientific article published on June 13, 1979

Q87958353 - Athletic Shoes: Finding the Right Fit: scientific article published on 01 March 1993

Q82572603 - Athletic shoes: finding a good match: scientific article published on 01 September 1997

Q71461970 - Athletic shoes--wish and reality. Orthopedic considerations of athletic shoe construction and athletic shoe purchase: scientific article published on 01 March 1996

Nike,_Inc.

Output: None of the candidates