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.

[["Adidas", "Company"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["Panarub", "Company"], ["Java", "Location"], ["Nike", "Company"], ["Adidas", "Product"], ["PT_Kizone", "Company"], ["2005", "Year"], ["Adidas", "Company"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Product"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Company"], ["Asia", "Location"], ["William_Anderson", "Person"], ["Adidas", "Product"], ["Nordhausen_University_of_Applied_Sciences", "Company"], ["Dongguan", "Location"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Xinjiang", "Place"], ["Shirt", "Product"], ["Adidas", "Company"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Product"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Company"], ["Panarub", "Company"], ["Java", "Location"], ["2005", "Year"], ["Adidas", "Product"], ["Nike", "Company"], ["Dallas_Cowboys", "Company"], ["Adidas", "Company"], ["34.0", "number"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Company"], ["Asia", "Location"], ["William_Anderson", "Person"], ["Adidas", "Product"], ["Nordhausen_University_of_Applied_Sciences", "Company"], ["Dongguan", "Location"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Xinjiang", "Place"], ["Shirt", "Product"], ["Adidas", "Company"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Product"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Company"], ["Panarub", "Company"], ["Java", "Location"], ["2005", "Year"], ["Adidas", "Product"], ["Nike", "Company"], ["Dallas_Cowboys", "Company"], ["Adidas", "Company"], ["34.0", "number"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Company"], ["Asia", "Location"], ["William_Anderson", "Person"], ["Adidas", "Product"], ["Nordhausen_University_of_Applied_Sciences", "Company"], ["Dongguan", "Location"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Xinjiang", "Place"], ["Shirt", "Product"], ["2011-01-01", "Date"], ["Adidas", "Company"], ["PT_Kizone", "Company"], ["Nike", "Company"], ["Dallas_Cowboys", "Company"], ["Labour_Behind_the_Label", "Organisation"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["3.0 (million dollars)", "number"], ["2012-07-16", "Date"], ["Adidas", "Company"], ["War_on_Want", "Organisation"], ["Adidas", "Product"], ["United_Students_Against_Sweatshops", "Organisation"], ["Labour_Behind_the_Label", "Organisation"], ["Adidas", "Company"], ["Asia", "Location"], ["William_Anderson", "Person"], ["Adidas", "Product"], ["Nordhausen_University_of_Applied_Sciences", "Company"], ["Dongguan", "Location"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Xinjiang", "Place"], ["Shirt", "Product"], ["Adidas", "Company"], ["Labour_Behind_the_Label", "Organisation"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["3.0 (million dollars)", "number"], ["2014-04-22", "Date"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Dongguan", "Location"], ["Labour_Behind_the_Label", "Organisation"], ["United_Students_Against_Sweatshops", "Organisation"], ["Adidas", "Product"], ["Adidas", "Company"], ["Asia", "Location"], ["William_Anderson", "Person"], ["Adidas", "Product"], ["Nordhausen_University_of_Applied_Sciences", "Company"], ["Dongguan", "Location"], ["Yue_Yuen_Industrial_Holdings", "Company"], ["Adidas", "Company"], ["Xinjiang", "Place"], ["Shirt", "Product"]]


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", "has_product_line", "Shirt"], ["Adidas", "has_product_line", "Shoe"], ["Adidas", "has_product_line", "Sports"], ["Adidas", "has_product_line", "Bag"], ["Adidas", "has_product_line", "Stationery"], ["Adidas", "has_product_line", "Watch"], ["Adidas", "has_product_line", "MusicalInstrument"], ["Adidas", "has_product_line", "Organisation"], ["Adidas", "has_product_line", "Film"], ["Adidas", "has_product_line", "City"], ["Adidas", "has_product_line", "Country"], ["Adidas", "has_product_line", "Athlete"], ["Adidas", "has_product_line", "Language"], ["Adidas", "has_product_line", "Food"], ["Adidas", "has_product_line", "WrittenWork"], ["Adidas", "has_product_line", "Award"], ["Adidas", "has_product_line", "Anthem"], ["Adidas", "has_product_line", "Currency"], ["Adidas", "has_product_line", "EthnicGroup"], ["Adidas", "has_product_line", "State"], ["Adidas", "has_product_line", "CityType"], ["Adidas", "has_product_line", "Industry"], ["Adidas", "has_product_line", "Occupation"], ["Adidas", "has_product_line", "Person"], ["Adidas", "has_product_line", "Place"], ["Adidas", "has_product_line", "Demonym"], ["Adidas", "has_product_line", "League"], ["Adidas", "has_product_line", "Season"], ["Adidas", "has_product_line", "Company"], ["Adidas", "has_product_line", "AthleticDirector"], ["Adidas", "has_product_line", "Stadium"], ["Adidas", "has_product_line", "SportsTeam"], ["Adidas", "has_product_line", "College'], ["Adidas", "has_product_line", "Background"], ["Adidas", "has_product_line", "Army'], ["Adidas", "has_product_line", "EthicalIssue"], ["Adidas", "has_product_line", "Country'], ["Adidas", "has_product_line", "City"], ["Adidas", "has_product_line", "Language", "WrittenWork"], ["Adidas", "has_product_line", "Sport"], ["Adidas", "has_product_line", "Person"], ["Adidas", "has_product_line", "Place"], ["Adidas", "has_product_line", "Film"], ["Adidas", "has_product_line", "Organisation"], ["Adidas", "has_product_line", "Anthem"], ["Adidas", "has_product_line", "Award"], ["Adidas", "has_product_line", "MusicalWork'], ["Adidas", "has_product_line", "Instrument'], ["Adidas", "has_product_line', "League"], ["Adidas", "has_product_line', "City"], ["Adidas", "has_product_line', "Season"], ["Adidas", "has_product_line', "State"], ["Adidas", "has_product_line', "Country"], ["Adidas", "has_product_line', "Language"], ["Adidas", "has_product_line', "WrittenWork"], ["Adidas", "has_product_line', "Company"], ["Adidas", "has_product_line', "Person"], ["Adidas", "has_product_line', "Place"], ["Adidas", "has_product_line', "EthnicGroup"], ["Adidas", "has_product_line', "CityType"], ["Adidas", "has_product_line', "Sport"], ["Adidas", "has_product_line', "AthleticDirector"], ["Adidas", "has_product_line', "Stadium"], ["Adidas", "has_product_line', "Army"], ["Adidas", "has_product_line', "Background"], ["Adidas", "has_product_line', "EthicalIssue"], ["Adidas", "has_product_line', "MusicalArtist"], ["Adidas", "has_product_line', "RecordLabel"], ["Adidas", "has_product_line', "Genre'], ["Adidas", "has_product_line', "Place'], ["Adidas", "has_product_line', "Country"], ["Adidas", "has_product_line', "City"], ["Adidas", "has_product_line', "Person"], ["Adidas", "has_product_line', "Organisation"], ["Adidas", "has_product_line', "Athlete"], ["Adidas", "has_product_line', "SportsTeam"], ["Adidas", "has_product_line', "College"], ["Adidas", "has_product_line', "League"], ["Adidas", "has_product_line', "Season"], ["Adidas", "has_product_line', "Instrument"], ["Adidas", "has_product_line', "MusicalWork"], ["Adidas", "has_product_line', "Language"], ["Adidas", "has_product_line', "Film"], ["Adidas", "has_product_line', "Award"], ["Adidas", "has_product_line', "WrittenWork"], ["Adidas", "has_product_line', "Army"], ["Adidas", "has_product_line', "Background"], ["Adidas", "has_product_line', "EthnicGroup"], ["Adidas", "has_product_line', "CityType"], ["Adidas", "has_product_line', "State"], ["Adidas", "has_product_line', "Country"], {"Adidas", "has_stakeholder": "United_Students_Against_Sweatshops"}, {"Adidas", "has_stakeholder": "War_on_Want"}, {"Adidas", "has_stakeholder": "Labour_Behind_the_Label"}, {"Adidas", "has_stakeholder": "Nordhausen_University_of_Applied_Sciences"}, {"Adidas", "has_stakeholder": "William_Anderson"}, {"Adidas", "has_stakeholder": "Yue_Yuen_Industrial_Holdings"}, {"Adidas", "has_stakeholder": "Panarub_factory"}, {"Adidas", "has_stakeholder": "PT_Kizone"}, {"Adidas", "has_stakeholder": "University"}, {"Adidas", "has_stakeholder": "Dongguan"}, {"Adidas", "has_stakeholder": "Indonesia"}, {"Adidas", "has_stakeholder": "Shoe"], {"Adidas", "has_stakeholder": "SportsTeam"}, {"Adidas", "has_stakeholder": "Athlete"}, {"Adidas", "has_stakeholder": "City"}, {"Adidas", "has_stakeholder": "Country"}, {"Adidas", "has_stakeholder": "League"}, {"Adidas", "has_stakeholder": "Person"}, {"Adidas", "has_stakeholder": "Place"}, {"Adidas", "has_stakeholder": "State"}, {"Adidas", "has_stakeholder": "Language"}, {"Adidas", "has_stakeholder": "WrittenWork"}, {"Adidas", "has_stakeholder": "Film"}, {"Adidas", "has_stakeholder": "Award"}, {"Adidas", "has_stakeholder": "MusicalWork"}, {"Adidas", "has_stakeholder": "Instrument'}, {"Adidas", "has_stakeholder": "MusicalArtist'}, {"Adidas", "has_stakeholder": "RecordLabel"}, {"Adidas", "has_stakeholder": "CityType"}, {"Adidas", "has_stakeholder": "EthnicGroup"}, {"Adidas", "has_stakeholder": "Army"}, {"Adidas", "has_stakeholder": "Background"}, {"Adidas", "has_stakeholder": "College"}, {"Adidas", "has_stakeholder": "Season"}]


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.

[["Adidas", "Company"], ["2018", "Year"], ["Soviet-themed_adidas_items", "Product"], ["Soviet-themed_adidas_items", "Company"], ["\"Social media outcry\"", "string"]]


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", "launched_campaign", "\"Soviet themed items\""], ["Adidas", "had_revenue_of", "2018"], ["Adidas", "launched_campaign", "\"Soviet themed items\""], ["Adidas", "had_revenue_of", "\"Soviet themed items\""]]


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.

[["Adidas", "Company"], ["James_Gatto", "Person"], ["2017_NCAA_Division_I_men's_basketball_corruption_scandal", "Event"]]


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", "has_executive", "James_Gatto"], ["James_Gatto", "was_indicted_in", "2017_NCAA_Division_I_men's_basketball_corruption_scandal"]]


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.

[["Adidas", "Company"], ["Karen_Parkin", "Person"], ["\"June 2020\"", "Date"], ["\"Karen Parkin stepped down voluntarily after concerns were raised by black employees about her failure to address racism and discrimination in the workplace.\"", "string"], ["Adidas", "Person"], ["\"Racial diversity controversy\"", "Issue"], ["\"Karen Parkin said that racism is noise\"", "string"], ["Adidas", "Product"], ["\"Karen Parkin apologised and offered her support to the company's continued progress towards racial justice\"", "string"]]


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", "has_product_line", "Racial_diversity_controversy"], ["Racial_diversity_controversy", "occurred_in_year", "2020"], ["Racial_diversity_controversy", "occurred_in_year", "June_2020"], ["Adidas", "has_executive", "Karen_Parkin"], ["Karen_Parkin", "statement", "\"I support the company's continued progress towards racial justice and I apologize for falling short.\""], ["Racial_diversity_controversy", "has_executive", "Karen_Parkin"], ["Racial_diversity_controversy", "characteristic", "\"Racism is noise.\""]]


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.

[["Adidas", "Company"], ["1972_Munich_Olympics", "Date"], ["Adidas_SL_72", "Product"], ["Bella_Hadid", "Person"], ["Adidas", "Person"], ["\"In July 2024, Adidas withdrew and apologized for their advertising campaign starring Bella Hadid for the SL 72 which was originally created for the 1972 Munich Olympics where the massacre of 11 Israeli athletes took place.\"", "string"]]


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", "launched_campaign", "SL_72"], ["SL_72", "occurred_in_year", "1972_Munich_Olympics"], ["1972_Munich_Olympics", "resulted_in", "Massacre_at_the_Munich_Olympics"], ["Adidas", "advertising_campaign", "Bella_Hadid"], ["Adidas", "advertising_campaign", "\"In July 2024, Adidas withdrew and apologized for their advertising campaign starring Bella Hadid for the SL 72 which was originally created for the 1972 Munich Olympics where the massacre of 11 Israeli athletes took place.\""]]