Adidas

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: Adidas, 2011 All Blacks replica rugby jersey pricing: Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand-basedAll Blacksfans asked for price cuts and began purchasing the jersey from overseas vendors after it was revealed that the local price of NZ$220 was more than twice the price offered on some websites. Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents. It has been labelled a public relations disaster by leading New Zealand PR firms[which?]and Consumer advocate groups. The largest New Zealand sportswear retailerRebel Sporthas stated[when?]it is angry and is considering selling the All Blacks Jerseys to the general public below cost.

[
    "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"
]

[
    "Q664 - New Zealand: island country in the southwest Pacific Ocean",
    "Q23893915 - New Zealand Organisms Register: catalogue of scientific names of New Zealand biota",
    "Q1048192 - New Zealand Labour Party: centre-left political party in New Zealand",
    "Q111645234 - NZThesisProject: community of editors and project aiming to upload the citation data of New Zealand doctoral and masters theses to Wikidata",
    "Q5148518 - Colony of New Zealand: constitutional monarchy in Oceania (1844–1907)"
]

[
    "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"
]

[
    "Q5196444 - customer advocacy: None",
    "Q1329436 - consumer organization: advocacy group favoring consumers and opposing corporate abuse",
    "Q44317179 - Consumer advocacy: scientific article published on January 1, 1977",
    "Q47632582 - Consumer advocacy meetings: an innovative therapeutic tool: scientific article",
    "Q69793832 - Consumer advocacy, elective surgery, and the \"golden era of medicine\": scientific article published on 01 November 1989"
]


Adidas

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: Adidas, 2011 All Blacks replica rugby jersey pricing: Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand-basedAll Blacksfans asked for price cuts and began purchasing the jersey from overseas vendors after it was revealed that the local price of NZ$220 was more than twice the price offered on some websites. Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents. It has been labelled a public relations disaster by leading New Zealand PR firms[which?]and Consumer advocate groups. The largest New Zealand sportswear retailerRebel Sporthas stated[when?]it is angry and is considering selling the All Blacks Jerseys to the general public below cost.

Adidas

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

Q55801 - New Zealand national rugby union team: men's rugby union team of New Zealand

Q3339086 - New Zealand Rugby League: sport governing body

Q3590302 - All Blacks Sevens team: sports team representing New Zealand

Q749756 - Junior All Blacks: rugby union team

Q27961616 - Rugby Challenge: 2011 video game

Adidas

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

Output: None of the candidates


Adidas

2012 "shackle" sneakers

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: Adidas, 2012 "shackle" sneakers: On 14 June 2012, Adidas posted on their Facebook page a picture of a pair ofJeremy Scott-designed shoes containing shackles. The picture was of a planned shoe line that Adidas intended to release in July. The photo caused controversy.Jesse Jacksonwas quoted as saying: "The attempt to commercialize and make popular more than 200 years of human degradation, where blacks were considered three-fifths human by our Constitution is offensive, appalling and insensitive".Jackson threatened a boycott, and NBA commissionerDavid Sternwas at one point reportedly contacted in hopes that he would intervene.Shortly after the outcry, the company cancelled the product.

[
    "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"
]

[
    "Q64019140 - Jeremy Scott: writer and entertainer from Nashville, TN",
    "Q112480972 - Jeremy Scott: None",
    "Q43609229 - Jeremy A. Scott: researcher",
    "Q14484510 - Jeremy Scott: American fashion designer",
    "Q75464744 - Jeremy Scott: (born 1934)"
]

[
    "Q37154545 - Shackle: family name",
    "Q16883050 - Shackleton: family name",
    "Q1430961 - shackle: U-shaped piece of metal acting as a primary connecting link in rigging systems",
    "Q27990014 - Shackletonia: genus of fungi",
    "Q65673166 - Shackled: 1986 video game"
]

[
    "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"
]

[
    "Q155223 - National Basketball Association: North American professional basketball league",
    "Q572316 - National Book Award: United States literary award in five categories",
    "Q60768275 - NBA: 2005 basketball video game released by Sony",
    "Q842793 - NBA G League: American men's basketball minor league",
    "Q36490532 - YoungBoy Never Broke Again: American rapper (born 1999)"
]

[
    "Q310586 - Jesse Jackson: American Baptist minister, activist, civil rights leader, politician, and founder of the Rainbow PUSH Coalition",
    "Q47511059 - Jesse Jackson: painting by Richard Mantel",
    "Q47511441 - Jesse Jackson: painting by Jacob A. Lawrence",
    "Q47510440 - Jesse Jackson: painting by Mark Hess",
    "Q609189 - Jesse Jackson Jr.: United States Congressman from Illinois"
]

[
    "Q713744 - 200: natural number",
    "Q1985 - 2000: calendar year",
    "Q211677 - 200: Faroese band",
    "Q8567 - 2008 Summer Olympics: Games of the XXIX Olympiad, in Beijing, China",
    "Q8558 - 2004 Summer Olympics: Games of the XXVIII Olympiad, in Athens, Greece"
]

[
    "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"
]

[
    "Q37154545 - Shackle: family name",
    "Q16883050 - Shackleton: family name",
    "Q1430961 - shackle: U-shaped piece of metal acting as a primary connecting link in rigging systems",
    "Q27990014 - Shackletonia: genus of fungi",
    "Q65673166 - Shackled: 1986 video game"
]

Output: None of the candidates


Adidas

2012 "shackle" sneakers

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: Adidas, 2012 "shackle" sneakers: On 14 June 2012, Adidas posted on their Facebook page a picture of a pair ofJeremy Scott-designed shoes containing shackles. The picture was of a planned shoe line that Adidas intended to release in July. The photo caused controversy.Jesse Jacksonwas quoted as saying: "The attempt to commercialize and make popular more than 200 years of human degradation, where blacks were considered three-fifths human by our Constitution is offensive, appalling and insensitive".Jackson threatened a boycott, and NBA commissionerDavid Sternwas at one point reportedly contacted in hopes that he would intervene.Shortly after the outcry, the company cancelled the product.

Adidas

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

Q184605 - 2012: 2009 film directed by Roland Emmerich

Adidas

P287 - designed by: person or organization which designed the object. For buildings use "architect" (Property:P84)

P736 - cover art by: name of person or team creating cover artwork for book, record album, single record etc.

P170 - creator: maker of this creative work or other object (where no more specific property exists)

P5026 - lighting designer: person responsible for lighting on a stage

P5028 - sound designer: person responsible for the sound on a stage, in a film or in a radio drama

Q64019140 - Jeremy Scott: writer and entertainer from Nashville, TN

Q14484510 - Jeremy Scott: American fashion designer

Adidas

Adidas

Q310586 - Jesse Jackson: American Baptist minister, activist, civil rights leader, politician, and founder of the Rainbow PUSH Coalition

Q47511059 - Jesse Jackson: painting by Richard Mantel

Q47511441 - Jesse Jackson: painting by Jacob A. Lawrence

Q47510440 - Jesse Jackson: painting by Mark Hess

Q609189 - Jesse Jackson Jr.: United States Congressman from Illinois

Adidas

Adidas

Adidas

P47 - shares border with: countries or administrative subdivisions, of equal level, that this item borders, either by land or water. A single common point is enough.

P4070 - identifier shared with: qualifier, to be used on external identifier IDs, indicating another Wikidata item is also matched to this ID

P9531 - identifier shared with lexeme: qualifier used to state which other lexemes are also matched to this identifier

P10756 - number of reblogs: number of reblogs, reposts, retweets, crossposts, or on-platform shares received on a social media post

Q355 - Facebook: American online social media and social networking service

Q380 - Meta: American multinational technology corporation

Q754454 - ResearchGate: commercial scientific social network

Q65960697 - facebook: Formula One activities of Alfa Romeo

Q18613161 - Facebook: 300th strip of the webcomic xkcd

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Adidas

Sweatshops and labour standards

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: Adidas, Sweatshops and labour standards: Adidas has been criticized for operatingsweatshops, particularly in Indonesia. Between 2006 and 2007, Adidas rejected many of its suppliers that supported unions in favour of subcontractors with worse labour rights records.By subcontracting work to different suppliers, it is more difficult for Adidas to ensure company labour standards are enforced. Adidas's policy includes the freedom for workers to take part incollective bargainingand a non-retaliation policy towards workers who express concerns.[when?]In practice, however, many of Adidas's suppliers have not upheld these standards. At the Panarub factory inJava, 33 workers were fired after striking for better pay in 2005. PT Kizone is another Indonesian factory where Adidas has been criticized over treatment of workers. They produced products for Adidas as well asNikeand theDallas Cowboysuntil they closed in January 2011. 2,686 workers who were laid off are owed $3 million in severance pay and benefits. Nike has contributed $1.5 million but Adidas has not acted. A campaign has been initiated byUnited Students Against Sweatshopscalling for universities to cut contracts with Adidas. On 16 July 2012,War on Wantorganised activists in London to replace Adidas price tags in sports stores with 34p ones,a reference to the low hourly wage rate paid to the Indonesian workers who make Adidas goods.The campaign groupLabour Behind the Labelclaimed that the basic pay of Indonesian Adidas workers was only £10 a week. William Anderson, head of social and environmental affairs for the Asia Pacific region, posted an entry on the company blog in which he claimed that total wages including bonuses and overtime were often double the hourly wage, and drew attention topurchasing power parity. In April 2014, one of the biggest strikes on mainlandChinatook place at theYue Yuen Industrial HoldingsDongguan shoe factory, producing amongst others for Adidas. In 2022, researchers fromNordhausen University of Applied Sciencesidentified cotton fromXinjiangin Adidas shirts.

[
    "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"
]

[
    "Q17040305 - United Students Against Sweatshops: United States student organization for worker rights"
]

[
    "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"
]

[
    "Q251 - Java: object-oriented programming language",
    "Q2063 - JSON: text-based open standard designed for human-readable data interchange",
    "Q61612399 - Java: genus of insects",
    "Q3757 - Java: island of Indonesia",
    "Q37270782 - Java: family name"
]

[
    "Q483915 - Nike: American athletic equipment company",
    "Q19810283 - Nike: female given name",
    "Q101001185 - Adenike: Female given name",
    "Q266573 - Nike: name",
    "Q672037 - Nike Award: literary award"
]

[
    "Q2019 - 2005: year",
    "Q841374 - 2005: natural number",
    "Q428598 - 2005 United Kingdom general election: election of members to the House of Commons in 2005",
    "Q53385836 - 2005-2006 one-year-period: period of about one year starting in 2005 and ending in 2006",
    "Q520673 - Mazda RX-8: rotary powered sports car manufactured by Japanese automobile manufacturer Mazda from 2003–2012"
]

[
    "Q6467382 - Labour Behind the Label: British workers' rights organisation"
]

[
    "Q177837 - Japanese Wikipedia: Japanese-language edition of Wikipedia",
    "Q48 - Asia: biggest continent in the world",
    "Q726891 - Asia: British rock band",
    "Q3625042 - Asia: female given name",
    "Q37020313 - Asia: family name"
]

[
    "Q8004404 - William Anderson: Scottish painter (1757-1837)",
    "Q4065285 - William Anderson: cyclist (1888–1928)",
    "Q2577868 - William Anderson: United States naval officer (1921–2007)",
    "Q108162581 - William Anderson: British Royal Navy officer, commander in 1817",
    "Q45198670 - William Gilbert Anderson: American teacher and author (1860–1947)"
]

[
    "Q1391209 - Nordhausen University of Applied Sciences: None"
]

[
    "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"
]

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

[
    "Q34800 - Xinjiang: autonomous region in China",
    "Q1191125 - Moghulistan: Mongol breakaway khanate of the Chagatai Khanate",
    "Q1197219 - Xinjiang County: county in Yuncheng Prefecture, Shanxi Province, China",
    "Q11083110 - Xinjiang, Wengyuan County: town in Wengyuan, Shaoguan, Guangdong, China",
    "Q148725 - 2336 Xinjiang: asteroid"
]

[
    "Q76768 - shirt: fabric garment worn on the upper part of the body",
    "Q104844288 - Shirt: musical artist",
    "Q83772 - toplessness: state in which the torso is exposed above the waist or hips",
    "Q66627756 - Shirt: family name",
    "Q92413259 - Shirt: shirt (American) at the Metropolitan Museum of Art (MET, 1979.346.62)"
]

[
    "Q204862 - Dallas Cowboys: American football team",
    "Q1157992 - Dallas Cowboys Cheerleaders: cheerleading squad of the Dallas Cowboys American football team",
    "Q114486333 - Dallas Cowboys and Indians: episode of Storage Wars: Texas (S1 E11)",
    "Q5869307 - history of the Dallas Cowboys: aspect of history",
    "Q5211305 - Dallas Cowboys Radio Network: official radio network of the NFL's Dallas Cowboys"
]

[
    "Q17603681 - January 1, 2011: date",
    "Q17974125 - 2011-01-01: Egipt: wybuch samochodu-pułapki przed kościołem w Aleksandrii. 21 zabitych: Wikinews article"
]

[
    "Q7968725 - War on Want: British anti-poverty charity",
    "Q132471625 - War on Want (Northern Ireland): international NGO from the United Kingdom",
    "Q71901767 - War on Want: journal article; published in Nature on 1970-10-01"
]

[
    "Q2021 - 2006: year",
    "Q841361 - 2006: natural number",
    "Q9130667 - 2006: album by Cool Kids of Death",
    "Q9672 - 2006 Winter Olympics: 20th edition of Winter Olympics, in Turin, Italy",
    "Q329679 - 2006 Asian Games: 15th edition of the Asian Games"
]

[
    "Q2024 - 2007: calendar year",
    "Q907344 - 2007: natural number",
    "Q135047694 - 2007: video game developed by Hit'n'Run Digital Studio",
    "Q53389304 - 2007-2008 one-year-period: period of about one year starting in 2007 and ending in 2008",
    "Q15729072 - 2007 Census of the Philippines: item representing census of Philippines 2007 for source"
]

Output: None of the candidates

Output: None of the candidates


Adidas

Sweatshops and labour standards

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: Adidas, Sweatshops and labour standards: Adidas has been criticized for operatingsweatshops, particularly in Indonesia. Between 2006 and 2007, Adidas rejected many of its suppliers that supported unions in favour of subcontractors with worse labour rights records.By subcontracting work to different suppliers, it is more difficult for Adidas to ensure company labour standards are enforced. Adidas's policy includes the freedom for workers to take part incollective bargainingand a non-retaliation policy towards workers who express concerns.[when?]In practice, however, many of Adidas's suppliers have not upheld these standards. At the Panarub factory inJava, 33 workers were fired after striking for better pay in 2005. PT Kizone is another Indonesian factory where Adidas has been criticized over treatment of workers. They produced products for Adidas as well asNikeand theDallas Cowboysuntil they closed in January 2011. 2,686 workers who were laid off are owed $3 million in severance pay and benefits. Nike has contributed $1.5 million but Adidas has not acted. A campaign has been initiated byUnited Students Against Sweatshopscalling for universities to cut contracts with Adidas. On 16 July 2012,War on Wantorganised activists in London to replace Adidas price tags in sports stores with 34p ones,a reference to the low hourly wage rate paid to the Indonesian workers who make Adidas goods.The campaign groupLabour Behind the Labelclaimed that the basic pay of Indonesian Adidas workers was only £10 a week. William Anderson, head of social and environmental affairs for the Asia Pacific region, posted an entry on the company blog in which he claimed that total wages including bonuses and overtime were often double the hourly wage, and drew attention topurchasing power parity. In April 2014, one of the biggest strikes on mainlandChinatook place at theYue Yuen Industrial HoldingsDongguan shoe factory, producing amongst others for Adidas. In 2022, researchers fromNordhausen University of Applied Sciencesidentified cotton fromXinjiangin Adidas shirts.

Adidas

Q76768 - shirt: fabric garment worn on the upper part of the body

Q83772 - toplessness: state in which the torso is exposed above the waist or hips

Q92413259 - Shirt: shirt (American) at the Metropolitan Museum of Art (MET, 1979.346.62)

Adidas

Q1515807 - Yehuda Shoenfeld: Israeli physician

Q152355 - cobbler: person who makes or repairs shoes (umbrella concept; see also shoe repairer)"

Adidas

Q349 - sport: forms of recreational activity, usually physical

Q27020041 - sports season: season of a sports league or competition, generally a specific one year period

Q2066131 - athlete: person who participates regularly in a sport

Q2215841 - cycle sport: competitive physical activity using bicycles

Q50806913 - Sports: Academic journal published by MDPI AG , covering the subjects: Geography. Anthropology. Recreation: Recreation. Leisure: Sports

Adidas

Q58803081 - subdistrict of Mongolia: administrative subdivision of Mongolia

Q1323314 - bag: simple tool in the form of a non-rigid container

Q62 - San Francisco: consolidated city and county in California, United States

Q161884 - Bagneux: commune in Hauts-de-Seine, France

Adidas

Q875696 - stationery: variety of office or domestic supplies including paper

Q16668826 - stationery shop: shop that sells stationery

Q3115652 - Stationery Stores F.C.: association football club in Nigeria

Q135023481 - Stationery Frontier: 2021 video game developed by Ivan

Q51801746 - Stationery for Mac OS X: file format

Adidas

Q376 - timepiece: instrument that measures the passage of time

Q1946176 - Watch: 1978 studio album by Manfred Mann's Earth Band

Q178794 - watch: personal timepiece, typically worn on the wrist or carried in a pocket

Q26965868 - wristwatch: watch designed to be worn on a wrist, attached by a watch strap or other type of bracelet

Adidas

Q1033564 - list of musical instruments: Wikimedia list article

Adidas

Q43229 - organization: social entity established to meet needs or pursue goals

Q2029841 - organization: concept in economics: activity in which something is accomplished

Q4479442 - founder: person who creates an institution intended to perpetuate itself after the founder's association ends

Q15642541 - human-geographic territorial entity: territorial entity of which the borders are determined by physiographic and human features

Adidas

Q11424 - film: sequence of images that give the impression of movement, stored on film stock

Q1137203 - thin film: a layer of material ranging from fractions of a nanometer to several micrometers in thickness

Q2526255 - film director: person who controls the artistic and dramatic aspects of a film production

Q41253 - movie theater: venue, usually a building, for viewing films

Q93204 - documentary film: nonfictional motion picture

Adidas

Q50602 - Manchester City F.C.: association football club in Manchester, England

Q90 - Paris: capital city and largest city of France

Q19444 - Birmingham City F.C.: association football club in Birmingham, England

Q18736 - Stoke City F.C.: association football club in Stoke-on-Trent, England

Adidas

Q83440 - country music: genre of American popular music

Q6256 - country: distinct territorial body or political entity

Q3624078 - sovereign state: state that has the highest authority over a territory

Q902 - Bangladesh: country in South Asia

Q175185 - rural area: geographic area that is located outside towns and cities

Adidas

Q315 - language: structured system of communication

Q34770 - language: particular system of communication, often named for the region or peoples that use it

Q75488338 - Language: controlled vocabulary that list language names of the European Union

Q60317586 - Language: ESRB video game content descriptor for games that contains some use of profanity.

Adidas

Adidas

Q618779 - award: something given to a person or a group of people to recognize their merit or excellence

Q4504495 - award ceremony: event and/or television programming genre based on the presentation of an award

Q21096945 - award winner: person who has received an award

Q38033430 - class of award: class of award (order, medal, etc.)

Q2988681 - achievement: meta-goal in a video game

Adidas

Q5 - human: any single member of Homo sapiens, unique extant species of the genus Homo

Q215627 - person: being that has certain capacities or attributes constituting personhood (for humans, use Q5 [human] with P31 [instance of])

Adidas

Q2221906 - geographic location: point, line or area on or near Earth

Q202142 - locative case: grammatical case which indicates a location

Q98929991 - place: small area known by a geographical name

Q17334923 - physical location: property describing the position of something in space

Adidas

Q217438 - demonym: name for a resident of a locality

Q134617828 - Demonyms in Canada: website for English demonyms for Canadian places

Q3100940 - list of demonyms for US states and territories: Wikimedia list article

Q18915259 - adjectival form of toponym: None

Q105942275 - demonym of: inverse label of property P1549 (demonym)

Adidas

Adidas

Q8142 - currency: generally accepted medium of exchange for goods or services

Q1144846 - Curren$y: American rapper (born 1981)

Q266516 - generic currency sign: glyph (¤) used for unspecified currency

Q5194999 - Currency Press: specialist performing arts publisher in Australia

Q63520572 - Currency: parish of Cook County, New South Wales, Australia

Adidas

Q5372 - basketball: team sport played on a court with baskets on either end

Q3665646 - basketball player: person who plays in basketball

Q155223 - National Basketball Association: North American professional basketball league

Adidas

Q19204627 - American football player: athlete who plays American football

Q42331263 - American football coach: coach of American football

Adidas

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

Adidas

Q3464665 - television series season: set of episodes produced for a television series

Q1539532 - sports season of a sports club: activity of a sports club during the portion of one year in which regulated games of a sport are played as part of a league or other multi match tournament

Q30227593 - one-year-period overlapping two calendar years: one-year-period lasting over two calendar years

Q24384 - season: weather- or climate-based subdivision of the year

Adidas

Q223341 - League of Legends: 2009 multiplayer online battle arena video game

Q623109 - sports league: group of sports teams or individual athletes that compete against each other

Q170156 - confederation: union of sovereign states linked by treaties, whose common government does not directly exercise its sovereignty over their territory

Adidas

Q36133 - soil: natural body consisting of layers that are primarily composed of minerals

Q220599 - cytosol: part of the cytoplasm that does not contain organelles but which does contain other particulate matter, such as protein complexes

Q9212 - United States Army: land service branch of the United States Armed Forces

Q161598 - groundwater: water located beneath the ground surface

Adidas

Q6881511 - enterprise: organizational unit producing goods or services, which benefits from a certain degree of autonomy in decision-making, especially for the allocation of its current resources

Q783794 - company: legal entity representing an association of people, whether natural, legal or a mixture of both, with a specific objective

Q102538 - company: military unit size designation

Q1162163 - director: person who leads a particular area of a company or organization

Adidas

Q541947 - anthem: music composition of a celebration; short sacred choral work; specific form of liturgical music of the Anglican churches of the 16th–18th centuries

Q677488 - Elevance Health, Inc.: American healthcare company

Q2852731 - Anthem: song from Chess album and musical

Adidas

Adidas

Q1248784 - airport: location where aircraft take off and land with extended support facilities, mostly for commercial air transport

Q135264374 - Airport: Tony Hawk's Pro Skater 3 level

Q409022 - Airport: 1970 film directed by George Seaton

Q4698923 - Airport: metro station serving Newcastle Airport

Adidas

Q206021 - flight: process by which an object moves, through an atmosphere or beyond it

Q116026358 - Flight: Finnish musician, part of Flight & Jontey

Q51879220 - flight: superpower ability to lift off the ground, to ride air currents or to fly self-propelled through the air

Q1430466 - flight: response to distress by fleeing

Q13583449 - flight: military aviation unit size designation

Adidas

Q43115 - saint: person who has been recognized for having an exceptional degree of holiness, sanctity, and virtue; may or may not be a canonised saint

Q42716 - Saint-Étienne: commune in Loire, France

Q3464126 - Catholic saint: person deemed by the Catholic Church to be in Heaven, and worthy of public veneration

Adidas

Q23084914 - Team: 2016 song by Iggy Azalea

Q8418 - handball: team sport played with a thrown ball and goals

Q626225 - teamwork: collaborative effort of a team to achieve a common goal

Q327245 - team: group linked in a common purpose

Adidas

Q11436 - aircraft: machine that is able to fly by gaining support from the air other than the reactions of the air against the earth’s surface

Q2095549 - aircraft pilot: person controlling an aircraft in flight

Q744913 - aviation accident: aviation occurrence involving serious injury, death, or destruction of aircraft

Q15056995 - aircraft model: specific aircraft design and specification, differentiated from others of its family

Q743004 - aircraft engine: engine designed for use in powered aircraft

Adidas

Adidas

Q40614 - fossil: preserved remains or traces of organisms from a past geological age

Q796451 - Fossilworks: online resource for fossil animals, plants, and microorganisms

Q23038290 - fossil taxon: taxon described on the basis of fossil material

Q2455930 - Fossil Record: scientific journal

Adidas

Adidas

Q188451 - music genre: category that identifies pieces of music as belonging to a shared tradition or set of conventions

Q483394 - genre: category of creative works based on stylistic, thematic or technical criteria

Q214127 - genre painting: paintings of scenes or events from everyday life

Q1047337 - genre art: art genre that depicts scenes from everyday life

Adidas

Q3918 - university: academic institution for further education

Q34433 - University of Oxford: collegiate research university in Oxford, England (Est. 1096)

Q219563 - University of Washington: public research university in Seattle, Washington, United States

Q35794 - University of Cambridge: collegiate public research university in Cambridge, England, United Kingdom

Q503424 - University of Leeds: university in Leeds, United Kingdom

Adidas

Q1644344 - Timezone: German record label

Q12143 - time zone: region on Earth that has a uniform standard time for legal, commercial, and social purposes

Q12521502 - Timezone: Chain of amusement arcades founded in Australia

Q21168029 - Yemen time zone: official timezone in the country of Yemen

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


Adidas

Soviet themed items, and advertising

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: Adidas, Soviet themed items, and advertising: In 2018, Adidas promoted a line of Soviet themed items. After a social media outcry, they were taken off the market.

[
    "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"
]

[
    "Q25291 - 2018: year",
    "Q12681715 - 2018: natural number",
    "Q9680 - 2018 Winter Olympics: 23rd edition of Winter Olympics, in Pyeongchang, South Korea",
    "Q170645 - 2018 FIFA World Cup: 21st FIFA World Cup, held in Russia",
    "Q47517245 - 2018 UCI World Ranking: None"
]


Adidas

Soviet themed items, and advertising

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: Adidas, Soviet themed items, and advertising: In 2018, Adidas promoted a line of Soviet themed items. After a social media outcry, they were taken off the market.

Adidas

Q9680 - 2018 Winter Olympics: 23rd edition of Winter Olympics, in Pyeongchang, South Korea

Q170645 - 2018 FIFA World Cup: 21st FIFA World Cup, held in Russia

Q47517245 - 2018 UCI World Ranking: None

Output: None of the candidates


Adidas

NCAA corruption scandal

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: Adidas, NCAA corruption scandal: Adidas executive James Gatto was indicted in the2017 NCAA Division I men's basketball corruption scandal.

[
    "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"
]


Adidas

NCAA corruption scandal

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: Adidas, NCAA corruption scandal: Adidas executive James Gatto was indicted in the2017 NCAA Division I men's basketball corruption scandal.

Adidas

P6 - head of government: head of the executive power of this town, city, municipality, state, country, or other governmental body

P797 - authority: entity having executive power on given entity

P1431 - executive producer: executive producer of a movie, TV show, or other piece of media or entertainment

P169 - chief executive officer: highest-ranking corporate officer appointed as the CEO within an organization

P1555 - Executive Order number: identifier for an executive order of the USA

James_Gatto


Adidas

Racial diversity controversy

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: Adidas, Racial diversity controversy: In June 2020, the head of global human resources at Adidas, Karen Parkin, voluntarily stepped down after some black employees raised concerns about her failure to address racism and discrimination in the workplace.The former HR chief had also made a comment about racism being "noise", in addition to not adequately addressing workforce diversity.Upon her resignation, she made a statement saying that she supports the company's continued progress towards racial justice and offered an apology.

[
    "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"
]

[
    "Q56703526 - Karen Parkin: None"
]

[
    "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"
]

[
    "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"
]

Output: None of the candidates


Adidas

Racial diversity controversy

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: Adidas, Racial diversity controversy: In June 2020, the head of global human resources at Adidas, Karen Parkin, voluntarily stepped down after some black employees raised concerns about her failure to address racism and discrimination in the workplace.The former HR chief had also made a comment about racism being "noise", in addition to not adequately addressing workforce diversity.Upon her resignation, she made a statement saying that she supports the company's continued progress towards racial justice and offered an apology.

Adidas

Racial_diversity_controversy

Q107442757 - 2020 Philippine census: census of the Philippines as of 1 May 2020

Q23766566 - 2020 United States Census: 24th national census of the United States, taken on April 1, 2020

Q99659280 - Second Nagorno-Karabakh War: armed conflict in South Caucasus

Racial_diversity_controversy

Q6312132 - June 2020 lunar eclipse: Penumbral lunar eclipse

Q100915140 - June 2020 Cameron County shelter-in-place order: stay-at-home order issued in Cameron County, Texas, in response to the 2020 coronavirus pandemic

Q97158052 - June 2020 Brazil bomb cyclone: None

Adidas

Racial_diversity_controversy

Output: None of the candidates

Output: None of the candidates


Adidas

SL 72 sneaker advertising campaign

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: Adidas, SL 72 sneaker advertising campaign: In July 2024, Adidas withdrew and apologized for their advertising campaign starringBella Hadidfor theSL 72which was originally created for the1972 Munich Olympicswhere themassacreof 11 Israeli athletes took place.

[
    "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"
]

[
    "Q130329369 - Adidas SL 72: line of shoes by Adidas"
]

[
    "Q21286612 - Bella Hadid: American model"
]

[
    "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"
]


Adidas

SL 72 sneaker advertising campaign

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: Adidas, SL 72 sneaker advertising campaign: In July 2024, Adidas withdrew and apologized for their advertising campaign starringBella Hadidfor theSL 72which was originally created for the1972 Munich Olympicswhere themassacreof 11 Israeli athletes took place.

Adidas

SL_72

1972_Munich_Olympics

Adidas

Output: None of the candidates

Output: None of the candidates