Welfare of animals used in sourcing

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Welfare of animals used in sourcing: Following criticism from animal rights groups,Nike declared its intent in 2023 to phase out the use of wool sourced from lambs that had been subjected tomulesing, a controversial practice involving the removal of skin from live lambs. Also in 2023, Nike pledged to eliminate the use ofkangaroo leatherin favor of a synthetic alternative, in response to animal activists' charge that the killing of kangaroos was unethical.

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

[
    "Q1792537 - artificial leather: man-made material that has similar properties and appearance to animal leather",
    "Q123504454 - Synthetic leather: US patent 11479910",
    "Q123504461 - Synthetic leather: US patent 11479908",
    "Q123504459 - Synthetic leather: US patent 11479909",
    "Q123504451 - Synthetic leather: US patent 11479911"
]

[
    "Q1952146 - Mulesing: removal of strips of wool-bearing skin from around the buttocks of a sheep",
    "Q81107903 - Mulesing of sheep in Australia--a global issue: scientific article published on 01 August 2005"
]

[
    "Q17149864 - kangaroo leather: Material created by tanning kangaroo skin"
]


Welfare of animals used in sourcing

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Welfare of animals used in sourcing: Following criticism from animal rights groups,Nike declared its intent in 2023 to phase out the use of wool sourced from lambs that had been subjected tomulesing, a controversial practice involving the removal of skin from live lambs. Also in 2023, Nike pledged to eliminate the use ofkangaroo leatherin favor of a synthetic alternative, in response to animal activists' charge that the killing of kangaroos was unethical.

Nike,_Inc.

Q483915 - Nike: American athletic equipment company

Q1792537 - artificial leather: man-made material that has similar properties and appearance to animal leather

Q123504454 - Synthetic leather: US patent 11479910

Q123504461 - Synthetic leather: US patent 11479908

Q123504459 - Synthetic leather: US patent 11479909

Q123504451 - Synthetic leather: US patent 11479911

Nike,_Inc.

Q17149864 - kangaroo leather: Material created by tanning kangaroo skin

Nike,_Inc.

Q42329 - wool: natural fibre from the soft hair of sheep or other mammals

Q1218503 - Wool: village and civil parish in Dorset, England, UK

Q135066693 - WOOL: video game developed by tonacapi

Nike,_Inc.

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

Q49622 - 2023: calendar year
Q19241646 - 2023: natural number
Q226112 - 2023 Asaph: asteroid
Q2615604 - 20230 Blanchard: asteroid
Q773285 - 20234 Billgibson: asteroid

Nike,_Inc.

Q1952146 - Mulesing: removal of strips of wool-bearing skin from around the buttocks of a sheep

Q81107903 - Mulesing of sheep in Australia--a global issue: scientific article published on 01 August 2005

Nike,_Inc.

Output: None of the candidates


Environmental record

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Environmental record: In 2007,New England–based environmental organization Clean Air-Cool Planet ranked Nike among the top three companies (out of 56) in a survey of climate-friendly companies.

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

[
    "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"
]

[
    "Q582728 - The New England Journal of Medicine: peer-reviewed medical journal",
    "Q18389 - New England: region of the northeasternmost United States",
    "Q2973794 - New England: Australian federal electoral division",
    "Q4897536 - Beth Israel Deaconess Medical Center: teaching hospital of Harvard Medical School",
    "Q193390 - New England Patriots: National Football League franchise in Foxborough, Massachusetts"
]

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


Environmental record

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Environmental record: In 2007,New England–based environmental organization Clean Air-Cool Planet ranked Nike among the top three companies (out of 56) in a survey of climate-friendly companies.

Nike,_Inc.

P159 - headquarters location: city or town where an organization's headquarters is or has been situated. Use P276 qualifier for specific building

Q582728 - The New England Journal of Medicine: peer-reviewed medical journal

Q18389 - New England: region of the northeasternmost United States

Q2973794 - New England: Australian federal electoral division

Q4897536 - Beth Israel Deaconess Medical Center: teaching hospital of Harvard Medical School

Q193390 - New England Patriots: National Football League franchise in Foxborough, Massachusetts

Output: None of the candidates


Recycling

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Recycling: Nike has also been praised for itsNike Grindprogram, which closes theproduct lifecycle, by groups such asClimate Counts. Since 1993, Nike has worked on its Reuse-A-Shoe program.This program is Nike's longest-running program that benefits both the environment and the community by collecting old athletic shoes of any type in order to process and recycle them. The material that is produced is then used to help create sports surfaces such as basketball courts, running tracks, and playgrounds.Nike France made their Reuse-A-Shoe program available online so that they could make it easier for consumers to send in their old shoes.In 2017, it was estimated that 28,000,000 shoes were collected since its start in 1993. Nike limited the mail-in option of the program because they are aware that the emissions from shipping would offset the good, they are trying to do. They work with the National Recycling Coalition to help limit transportation of recycled shoes. During transportation most of the vehicles that are used are using diesel or fuel oil.Diesel oil emits 22.44 pounds of Carbon Dioxide per gallon. A campaign that Nike began for Earth Day 2008 was a commercial that featured basketball starSteve Nashwearing Nike's Trash Talk Shoe, which had been constructed in February 2008 from pieces of leather and synthetic leather waste from factory floors. The Trash Talk Shoe also featured a sole composed of ground-up rubber from a shoe recycling program. Nike claims this is the first performance basketball shoe that has been created from manufacturing waste, but it only produced 5,000 pairs for sale.

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

[
    "Q16582530 - Nike Francesca Pucci: Italian voice actress"
]

[
    "Q192955 - Steve Nash: Canadian basketball player and coach",
    "Q42316961 - Steve Nash: actor",
    "Q16137022 - Steve Nash: English rugby league footballer and coach",
    "Q48851147 - Steve Nash: None",
    "Q133746387 - Steve Nash: None"
]

[
    "Q2004 - 2008: year",
    "Q1069941 - 2008: natural number",
    "Q8567 - 2008 Summer Olympics: Games of the XXIX Olympiad, in Beijing, China",
    "Q135106232 - 2008: vocal track by Annika Norlin; 2008 studio recording",
    "Q130758128 - 2008: exhibition at the Jewish Museum, Amsterdam, The Netherlands (2007)"
]

[
    "Q1210771 - The 5,000 Fingers of Dr. T.: 1953 film by Roy Rowland",
    "Q4639535 - 5,000,000: album by Dread Zeppelin",
    "Q47890158 - 5,000 Prisoners Massacred at Onsong Concentration Camp in 1987: None",
    "Q20801811 - 5,000 Dollar Elopement: 1916 short film directed by Tom Mix",
    "Q115038893 - 5,000 Years of Korean Art: 1981 Exhibition at Metropolitan Museum"
]

[
    "Q573 - day: unit of time lasting 24 hours, derived from the period of Earth's rotation about its axis",
    "Q124473 - Earth Day: annual event on April 22",
    "Q125607871 - Earth Day: None",
    "Q74036050 - Earth Day: print in the National Gallery of Art (NGA 60589)",
    "Q5327064 - Earth Days: 2009 film by Robert Stone"
]

[
    "Q2065 - 1993: year",
    "Q858412 - 1993: natural number",
    "Q53385494 - 1993-1994 one-year-period: period of about one year starting in 1993 and ending in 1994",
    "Q1633212 - 1993 Spanish general election: election",
    "Q700458 - 1993–94 Fußball-Bundesliga: 31st season of the Bundesliga"
]

[
    "Q38423 - diesel fuel: liquid fuel used in diesel engines",
    "Q56748912 - Diesel Fuel Taxation (Administration) Act 1957: Act of the Parliament of Australia, no longer in force, registered as C1957A00098",
    "Q56748913 - Diesel Fuel Tax Act (No. 2) 1957: Act of the Parliament of Australia, no longer in force, registered as C1957A00097",
    "Q5275027 - Diesel fuel tanks in trucks: None",
    "Q56756001 - Diesel Fuel Rebate Scheme Amendment Act 2002: Act of the Parliament of Australia, currently registered as C2004A00974"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Recycling

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Recycling: Nike has also been praised for itsNike Grindprogram, which closes theproduct lifecycle, by groups such asClimate Counts. Since 1993, Nike has worked on its Reuse-A-Shoe program.This program is Nike's longest-running program that benefits both the environment and the community by collecting old athletic shoes of any type in order to process and recycle them. The material that is produced is then used to help create sports surfaces such as basketball courts, running tracks, and playgrounds.Nike France made their Reuse-A-Shoe program available online so that they could make it easier for consumers to send in their old shoes.In 2017, it was estimated that 28,000,000 shoes were collected since its start in 1993. Nike limited the mail-in option of the program because they are aware that the emissions from shipping would offset the good, they are trying to do. They work with the National Recycling Coalition to help limit transportation of recycled shoes. During transportation most of the vehicles that are used are using diesel or fuel oil.Diesel oil emits 22.44 pounds of Carbon Dioxide per gallon. A campaign that Nike began for Earth Day 2008 was a commercial that featured basketball starSteve Nashwearing Nike's Trash Talk Shoe, which had been constructed in February 2008 from pieces of leather and synthetic leather waste from factory floors. The Trash Talk Shoe also featured a sole composed of ground-up rubber from a shoe recycling program. Nike claims this is the first performance basketball shoe that has been created from manufacturing waste, but it only produced 5,000 pairs for sale.

Nike,_Inc.

Nike_Grind

P5994 - recycling code: used to identify the material from which an item is made, to facilitate easier recycling or other reprocessing

Q483915 - Nike: American athletic equipment company

Nike,_Inc.

Reuse-A-Shoe

Q53385494 - 1993-1994 one-year-period: period of about one year starting in 1993 and ending in 1994

Q1633212 - 1993 Spanish general election: election

Q700458 - 1993–94 Fußball-Bundesliga: 31st season of the Bundesliga

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

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

Q116031595 - Air Max: 2020 single by Comethazine

Q4698092 - Air Max Africa: airline from Gabon

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

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

Nike,_Inc.

Nike,_Inc.

Q61883 - air force: military branch of service primarily concerned with aerial warfare

Q2427600 - Air Force Cross: British military decoration

Q695850 - air base: aerodrome used by a military force for the operation of military aircraft

Q679165 - squadron: military aviation unit

Q921432 - Soviet Air Forces: aerial warfare branch of the Soviet Union's armed forces

Nike,_Inc.

Nike,_Inc.

Q125908302 - Air Max 270: shoe line by Nike

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q19574830 - Air Pegasus: former airline in India

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q407113 - Air Force 1: athletic shoe by Nike

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

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

Q179969 - Air Force One: official United States presidential aircraft

Nike,_Inc.

Nike,_Inc.

Q48976796 - Nike Air Max 97: part of the Nike Air Max line of shoes

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

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


Sulfur hexafluoride

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sulfur hexafluoride: Sulfur hexafluorideis an extremely potent and persistent greenhouse gas that was used to fill the cushion bags in all "Air"-branded shoes from 1992 to 2006.277 tons was used during the peak in 1997.

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

[
    "Q279055 - sulfur hexafluoride: chemical compound, non-toxic, climate-damaging gas",
    "Q26371730 - Sulfur Hexafluoride: publication published in 1978",
    "Q21175323 - sulfur hexafluoride exposure: hazardous chemical exposure",
    "Q24236971 - Sulfur hexafluoride as a mine ventilation research tool - recent field applications: report of investigations published in 1982",
    "Q1228904 - sulfur hexafluoride circuit breaker: switching device used in high voltage power grids"
]

[
    "Q279055 - sulfur hexafluoride: chemical compound, non-toxic, climate-damaging gas",
    "Q26371730 - Sulfur Hexafluoride: publication published in 1978",
    "Q21175323 - sulfur hexafluoride exposure: hazardous chemical exposure",
    "Q24236971 - Sulfur hexafluoride as a mine ventilation research tool - recent field applications: report of investigations published in 1982",
    "Q1228904 - sulfur hexafluoride circuit breaker: switching device used in high voltage power grids"
]

[
    "Q2060 - 1992: year",
    "Q858275 - 1992: natural number",
    "Q16160088 - 1992: 1992 compilation album; various artists",
    "Q8488 - 1992 Summer Olympics: Games of the XXV Olympiad in Barcelona, Catalonia, Spain",
    "Q9660 - 1992 Winter Olympics: 16th edition of Winter Olympics, in Albertville, France"
]

[
    "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"
]

[
    "Q2088 - 1997: year",
    "Q859303 - 1997: natural number",
    "Q122235542 - 1997: 2023 video game",
    "Q918503 - 1997 United Kingdom general election: election for members of the British House of Commons",
    "Q53385594 - 1997-1998 one-year-period: period of about one year starting in 1997 and ending in 1998"
]


Sulfur hexafluoride

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sulfur hexafluoride: Sulfur hexafluorideis an extremely potent and persistent greenhouse gas that was used to fill the cushion bags in all "Air"-branded shoes from 1992 to 2006.277 tons was used during the peak in 1997.

Nike,_Inc.

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

P176 - manufacturer: (main or final) manufacturer or producer of this product

P2079 - fabrication method: method, process or technique used to grow, cook, weave, build, assemble, manufacture the item

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

P1071 - location of creation: place where the item was conceived or made; where applicable, location of final assembly

Q279055 - sulfur hexafluoride: chemical compound, non-toxic, climate-damaging gas

Q26371730 - Sulfur Hexafluoride: publication published in 1978

Q21175323 - sulfur hexafluoride exposure: hazardous chemical exposure

Q24236971 - Sulfur hexafluoride as a mine ventilation research tool - recent field applications: report of investigations published in 1982

Q1228904 - sulfur hexafluoride circuit breaker: switching device used in high voltage power grids

Sulfur_hexafluoride

Q167336 - greenhouse gas: heat-trapping gas in a planet's atmosphere

Q310667 - carbon footprint: total set of greenhouse gas emissions caused by an individual, event, organisation, or product, expressed as carbon dioxide equivalent

Q57584895 - Greenhouse Gases: science and technology: 2152-3878

Q110612403 - greenhouse gas reduction mandate: mandate to fuel suppliers to reduce greenhouse gas emissions from gasoline and diesel

Q111461869 - greenhouse gas emissions from agriculture: greenhouse gas emissions from agriculture on the Earth

Nike,_Inc.

Air_(brand)

Q918503 - 1997 United Kingdom general election: election for members of the British House of Commons

Q53385594 - 1997-1998 one-year-period: period of about one year starting in 1997 and ending in 1998

Air_(brand)


Toxic chemicals

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Toxic chemicals: In 2008, a project through theUniversity of North Carolina at Chapel Hillfound workers were exposed to toxicisocyanatesand other chemicals in footwear factories in Thailand. In addition to inhalation, dermal exposure was the biggest problem found. This could result in allergic reactions including asthmatic reactions.

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

[
    "Q192334 - University of North Carolina at Chapel Hill: public research university in Chapel Hill, North Carolina, United States",
    "Q3098948 - North Carolina Tar Heels: intercollegiate sports teams of the University of North Carolina at Chapel Hill",
    "Q101016054 - University of North Carolina at Chapel Hill School of Medicine: medical school",
    "Q7895945 - UNC Gillings School of Global Public Health: American university program",
    "Q101015984 - University of North Carolina at Chapel Hill College of Arts and Sciences: college within University of North Carolina at Chapel Hill"
]

[
    "Q2004 - 2008: year",
    "Q1069941 - 2008: natural number",
    "Q8567 - 2008 Summer Olympics: Games of the XXIX Olympiad, in Beijing, China",
    "Q135106232 - 2008: vocal track by Annika Norlin; 2008 studio recording",
    "Q130758128 - 2008: exhibition at the Jewish Museum, Amsterdam, The Netherlands (2007)"
]

[
    "Q17669916 - Toxic chemicals found in four Canadian politicians: Wikinews article",
    "Q105545752 - Toxic Chemicals Working Group: UK working group",
    "Q45099114 - Toxic chemicals in sediments and biota from a creosote-polluted harbor: relationships with hepatic neoplasms and other hepatic lesions in English sole (Parophrys vetulus).: scientific article published in October 1985",
    "Q69833761 - Toxic chemicals in marine sediment and biota from Mukilteo, Washington: relationships with hepatic neoplasms and other hepatic lesions in English sole (Parophrys vetulus): scientific article published on 01 February 1985",
    "Q83938298 - Toxic chemicals-induced genotoxicity detected by random amplified polymorphic DNA (RAPD) in bean (Phaseolus vulgaris L.) seedlings: scientific article published on 27 May 2009"
]

[
    "Q869 - Thailand: country in Southeast Asia",
    "Q6940 - UTC+07:00: identifier for a time offset from UTC of +7",
    "Q239549 - Thailand men's national football team: men's national association football team representing Thailand",
    "Q114540565 - Thailand: episode of Wildboyz (S4 E1)",
    "Q6138059 - Thailand in World War II: involvement of Thailand in World War II"
]

[
    "Q35869 - asthma: long-term disease involving inflamed airways",
    "Q2551913 - occupational asthma: respiratory medical condition caused or exacerbated by the workplace",
    "Q1772450 - Asthmatic Kitty: American independent record label",
    "Q826759 - status asthmaticus: type of asthma",
    "Q36177 - Euphorbia hirta: species of hairy herb grown in open grasslands, roadsides, and pathways"
]

[
    "Q408365 - isocyanate: salt or ester of isocyanic acid",
    "Q55664098 - isocyanate ester: ester of isocyanic acid",
    "Q55664017 - isocyanate salt: salt of isocyanic acid",
    "Q26327890 - Isocyanates: chapter published in 1977",
    "Q55009105 - isocyanates allergic asthma: allergic asthma that has allergic trigger isocyanates"
]

Output: None of the candidates


Toxic chemicals

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Toxic chemicals: In 2008, a project through theUniversity of North Carolina at Chapel Hillfound workers were exposed to toxicisocyanatesand other chemicals in footwear factories in Thailand. In addition to inhalation, dermal exposure was the biggest problem found. This could result in allergic reactions including asthmatic reactions.

Nike,_Inc.

Q408365 - isocyanate: salt or ester of isocyanic acid

Q55664098 - isocyanate ester: ester of isocyanic acid

Q55664017 - isocyanate salt: salt of isocyanic acid

Q26327890 - Isocyanates: chapter published in 1977

Q55009105 - isocyanates allergic asthma: allergic asthma that has allergic trigger isocyanates

Nike,_Inc.

Q192334 - University of North Carolina at Chapel Hill: public research university in Chapel Hill, North Carolina, United States

Q3098948 - North Carolina Tar Heels: intercollegiate sports teams of the University of North Carolina at Chapel Hill

Q101016054 - University of North Carolina at Chapel Hill School of Medicine: medical school

Q7895945 - UNC Gillings School of Global Public Health: American university program

Q101015984 - University of North Carolina at Chapel Hill College of Arts and Sciences: college within University of North Carolina at Chapel Hill

Nike,_Inc.

Q35869 - asthma: long-term disease involving inflamed airways

Q2551913 - occupational asthma: respiratory medical condition caused or exacerbated by the workplace

Q1772450 - Asthmatic Kitty: American independent record label

Q826759 - status asthmaticus: type of asthma

Q36177 - Euphorbia hirta: species of hairy herb grown in open grasslands, roadsides, and pathways

Nike,_Inc.

Q4669896 - skin absorption: route through which substances may be received

Q24091383 - Dermal exposure to the commonly used antimicrobial chemical triclosan promotes allergic responses in skin and lung (HYP7P.317): abstract published in 2014

Q66630564 - Dermal exposure and absorption of chemicals: chapter published in 2017

Q24854874 - Dermal exposure research program: abstract published in 2003

Q26158644 - Dermal exposure to JP-8 jet fuel during pregnancy alters immunological function in F1 mice: abstract published in 2004

Output: None of the candidates


Water pollution

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Water pollution: In July 2011, environmental groupGreenpeacepublished a report regardingwater pollutionimpacting theYangtze Riveremitted from a major textile factory operated by Nike supplierYoungor Group.Following the report, Nike, as well asAdidas,Puma, and a number of other brands included in the report announced an agreement to stop discharging hazardous chemicals by 2020.However, in July 2016 Greenpeace released a follow-up report which found that Nike "does not take individual responsibility" for eliminating hazardous chemicals, stating that Nike had not made an explicit commitment to riding itself ofperfluorinated compounds, and that "Nike does not ensure its suppliers report their hazardous chemical discharge data and has not made a commitment to do so". Back in 2016, Nike started to use water free dyeing materials so that they can help reduce their water use in their Southeast Asian factories.

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

[
    "Q81307 - Greenpeace: non-governmental environmental organization",
    "Q6719575 - Elbe: tugboat",
    "Q131347242 - Greenpeace: organisation (1971-)",
    "Q110723757 - Greenpeace European Unit: nonprofit organization",
    "Q31708704 - Greenpeace India: Indian non-governmental organisation"
]

[
    "Q183129 - water pollution: contamination of water bodies",
    "Q70440357 - chemical water pollution: adverse effect on bodies of water caused by chemical pollutants",
    "Q7973580 - water pollution in India: water pollution",
    "Q125804504 - Water Pollution: Textbook of Geography",
    "Q134516001 - Water Pollution: 1995 stamp of Algeria"
]

[
    "Q5413 - Yangtze: longest river in Asia, and the third-longest in the world",
    "Q15758768 - Yangtze River basin: None",
    "Q188130 - baiji: species of river dolphin",
    "Q19855970 - Yangtze River passageway: Proposed high-speed rail line in China",
    "Q1195076 - Yangtze Delta: delta of the Yangtze River, China"
]

[
    "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"
]

[
    "Q11708 - Southeast Asia: south-eastern region of Asia",
    "Q43467 - Mainland Southeast Asia: region in Southeast Asia",
    "Q15754995 - Southeast Asian Journal of Tropical Medicine and Public Health: journal",
    "Q135523 - Opisthorchis viverrini: species of trematode",
    "Q877484 - SEA Games: biennial multi-sport event involving participants from countries in Southeast Asia"
]

[
    "Q1112652 - Youngor Group: company"
]

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

[
    "Q129753135 - Water free dyeing of polypropylene fabric under supercritical carbon dioxide and comparison with its aqueous analogue: scholarly article published in September 2018"
]

[
    "Q25337 - 2020: year",
    "Q5978933 - 2020: natural number",
    "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"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Water pollution

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Water pollution: In July 2011, environmental groupGreenpeacepublished a report regardingwater pollutionimpacting theYangtze Riveremitted from a major textile factory operated by Nike supplierYoungor Group.Following the report, Nike, as well asAdidas,Puma, and a number of other brands included in the report announced an agreement to stop discharging hazardous chemicals by 2020.However, in July 2016 Greenpeace released a follow-up report which found that Nike "does not take individual responsibility" for eliminating hazardous chemicals, stating that Nike had not made an explicit commitment to riding itself ofperfluorinated compounds, and that "Nike does not ensure its suppliers report their hazardous chemical discharge data and has not made a commitment to do so". Back in 2016, Nike started to use water free dyeing materials so that they can help reduce their water use in their Southeast Asian factories.

Nike,_Inc.

Q28823 - textile: various fiber-based materials

Q607081 - textile industry: economic sector

Q10694573 - textile artist: person who creates art objects using plant, animal, or synthetic fibers

Q4893623 - Textile Museum and Documentation Centre: cultural property in Terrassa, Spain

Q192296 - weaving: technology for the production of textiles

Nike,_Inc.

P7888 - merged into: the subject was dissolved and merged into the object, that existed previously

P11812 - afterward owned by: person or institution that owned the subject after stated event. Meant to be used as qualifier of “significant event (P793)” for events like sales, auctions, gifts, etc

P11811 - beforehand owned by: person or institution that owned the subject just before stated event. Meant to be used as qualifier of "significant event (P793)" for events like sales, auctions, gifts, etc

Nike,_Inc.

Q1515807 - Yehuda Shoenfeld: Israeli physician

Q152355 - cobbler: person who makes or repairs shoes

Nike,_Inc.

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

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q11460 - clothing: covering worn on the body

Q3501317 - fashion designer: one who designs clothing and related items

Q1065579 - costume accessory: item carried or worn which is used to contribute to the wearer's outfit

Q56876960 - heraldic clothing: category of heraldic charges

Q11828862 - clothing industry: industry encompassing the design, manufacturing, wholesaling and retailing of clothes

Nike,_Inc.

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

Nike,_Inc.

Q11422 - toy: object intended to be played with

Q53268 - Toyota: Japanese multinational automotive manufacturer

Q1150311 - Toy's Factory: Japanese record label

Nike,_Inc.

Q81307 - Greenpeace: non-governmental environmental organization

Q131347242 - Greenpeace: organisation (1971-)

Q110723757 - Greenpeace European Unit: nonprofit organization

Q31708704 - Greenpeace India: Indian non-governmental organisation

Nike,_Inc.

Q161928 - footwear: garments worn on feet

Q152355 - cobbler: person who makes or repairs shoes

Q5915560 - footwear industry: process of making footwear

Nike,_Inc.

Q11660 - artificial intelligence: field of computer science that develops and studies software enabling machines to exhibit intelligent behavior

Q11019 - machine: tool containing one or more parts that uses energy to perform an intended action

Q2539 - machine learning: scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Q101333 - mechanical engineering: engineering discipline

Q11034 - printing: process for reproducing text and images using a master form or template

Nike,_Inc.

Q47505958 - Accessories: 1983 fiction film directed by Inna Tumanyan

Q7313526 - rental accessories and attachments: accessories used in the rental industry

Q123931175 - Accessories: instrument in the collections of the Swedish Museum of Performing Arts (N84676c)

Q123930702 - Accessories: instrument in the collections of the Swedish Museum of Performing Arts (M1393.3)

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q12140 - medication: substance used to diagnose, cure, treat, or prevent disease

Q28885102 - pharmaceutical product: qualitative and quantitative composition of a medicinal product in the dose form authorized for administration by a medicines regulatory agency and as represented with any corresponding regulated product information

Q7180737 - Pharmaceutical Research: peer-reviewed scientific journal

Q26853820 - Yao xue xue bao: scientific journal

Q7180763 - pharmaceutical sciences: area of study concerned with drugs at every stage, from design to disposition

Nike,_Inc.

Q2095 - food: any substance consumed to provide nutritional support for the body; form of energy stored in chemical

Q8195619 - human food: food eaten by Homo sapiens

Q173710 - esophagus: vertebrate organ through which food passes to the stomach

Q3007875 - Food Chemistry: peer-reviewed scientific journal

Nike,_Inc.

Nike,_Inc.

Q131207 - cosmetics: substances used to enhance the appearance or odor of the human body

Q26255597 - CosIng database: chemical database of the European union for substances used as cosmetic ingredient

Q757232 - medical tourism: people traveling abroad to obtain medical treatment

Q11194935 - Cosmetic: 2010 single by SID

Q12752882 - cosmetics industry: industry that manufactures and distributes cosmetic products

Nike,_Inc.

Q2876213 - aerospace: field of activity involving utilization of the atmosphere and outer space

Q3798668 - aerospace engineering: branch of engineering

Q50816273 - Aerospace: Academic journal published by MDPI AG , covering the subjects: Technology: Motor vehicles. Aeronautics. Astronautics

Q15750523 - Aerospace and Environmental Medicine: scientific journal

Nike,_Inc.

Q190117 - automotive industry: range of organizations associated with motor vehicles, such as automobiles, trucks and motorcycles

Q786820 - automobile manufacturer: company that makes automobiles

Q850270 - concept car: car designed to showcase prototype features

Q2090494 - automobile safety: study and practice to minimize the occurrence and consequences of motor vehicle accidents

Q124192 - automotive engineering: discipline of engineering

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q178827 - monomer: molecule that, as a unit, binds chemically or supramolecularly to other molecules to form a supramolecular polymer

Q107269510 - Monomeropus: genus of fishes

Q5223284 - Monomeria barbata: species of plant

Q15472585 - Monomeria longipes: species of plant

Nike,_Inc.

Q81163 - polymer: substance composed of macromolecules with repeating structural units

Q178593 - macromolecule: molecule of high relative molecular mass, the structure of which essentially comprises the multiple repetition of units derived, actually or conceptually, from molecules of low relative molecular mass

Q15764137 - Polymer International: journal

Q57337397 - Polymers: scientific journal

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

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

Nike,_Inc.

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

Nike,_Inc.

Nike,_Inc.

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

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Carbon footprint

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Carbon footprint: Nike reported TotalCO2e emissions(Direct + Indirect) for the twelve months ending June 30, 2020 at 317 Kt (+12/+4% y-o-y)and plans to reduce emissions 65% by 2030 from a 2015 base year.Thisscience-based targetis aligned withParis Agreementto limit global warming to 1.5 °C above pre-industrial levels.According to a study done in 2017, Nike contributed 3,002,529 metric tons of Carbon Dioxide in 2017 combined from different sectors in the company like retail, manufacturing, management, and more. While emissions of Nike's two corporate jets represent less than 0.1% of its total emissions, they have increased by 20% from 2015 to 2023. Partnership with Newlight In 2021, Nike announced they were working with Newlight Technologies to find more eco-friendly materials for their sneakers. They specifically mentioned Newlight's AirCarbon product which is a bioplastic that can be used to make shoes. The bioplastic is used as a replacement to leather, plastic, and other materials that are like that.Newlight was reported saying that the goal is to reduce Nike's carbon footprint.

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

[
    "Q22078222 - Newlight Technologies: None"
]

[
    "Q2677397 - 100.000 of niets: Belgian TV series or program",
    "Q164766 - Centomila dollari per Ringo: 1965 film by Alberto De Martino",
    "Q15931516 - 100.0 FM: Wikimedia list article",
    "Q130242464 - 100.000 kilómetros tras los ovnis: written work by J. J. Benítez",
    "Q157608 - Reichswehr: 1921–1935 combined military forces of Germany"
]

[
    "Q19241654 - 2030: natural number",
    "Q12799 - 2030: year",
    "Q122235581 - 2030: 2022 video game",
    "Q713789 - 2030s: next decade in the Gregorian calendar (2030-2039)",
    "Q29389034 - 2030 Winter Olympics: 26th edition of the Winter Olympics in the French Alps, France"
]

[
    "Q11184976 - ⅒: irreducible fraction",
    "Q20444040 - 0.1: single",
    "Q775523 - 130 nm lithography process: semiconductor manufacturing processes with a 130 nm MOSFET technology node",
    "Q17987561 - ⅛: irreducible fraction",
    "Q867398 - 180 nm lithography process: semiconductor manufacturing processes with a 180 nm MOSFET technology node"
]

[
    "Q2002 - 2015: year",
    "Q11187127 - 2015: natural number",
    "Q63758826 - 2015-2021 term (departments of France): None",
    "Q53386085 - 2015-2016 one-year-period: period of about one year starting in 2015 and ending in 2016",
    "Q114717231 - World Economic Forum Annual Meeting 2015: None"
]

[
    "Q549163 - 20,000 Leagues Under the Sea: 1997 television film directed by Michael Anderson",
    "Q58364741 - 20.000 mil podmorskiej żeglugi: None",
    "Q17744423 - 20.000 dollari sul 7: 1967 film by Alberto Cardone",
    "Q121294946 - 20.0 Megaquake: 2022 film directed by Jared Cohn",
    "Q11683298 - 20.000 Leagues Under the Sea: 2004 film"
]

[
    "Q21707860 - Paris Agreement: international agreement from 12 December 2015 within the United Nations Framework Convention on Climate Change (UNFCCC) dealing with greenhouse gas emissions mitigation, adaptation and finance starting in the year 2020",
    "Q2052417 - Paris Agreements: agreements signed after the London and Paris Conferences in 1954",
    "Q17221781 - Paris Agreement of 1864: None",
    "Q61984373 - Paris Agreement withdrawal protest in Berlin: None",
    "Q59595760 - Paris Agreement on Climate Change: U.S. Letter to United Nations: Congressional Research Service report"
]

[
    "Q25290 - 2017: year",
    "Q12681718 - 2017: natural number",
    "Q25052149 - 2017 United Kingdom general election: general election held in United Kingdom",
    "Q47015413 - 2017-2018 one-year-period: period of about one year starting in 2017 and ending in 2018",
    "Q27915850 - 2017 UCI Europe Tour: None"
]

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


Carbon footprint

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Carbon footprint: Nike reported TotalCO2e emissions(Direct + Indirect) for the twelve months ending June 30, 2020 at 317 Kt (+12/+4% y-o-y)and plans to reduce emissions 65% by 2030 from a 2015 base year.Thisscience-based targetis aligned withParis Agreementto limit global warming to 1.5 °C above pre-industrial levels.According to a study done in 2017, Nike contributed 3,002,529 metric tons of Carbon Dioxide in 2017 combined from different sectors in the company like retail, manufacturing, management, and more. While emissions of Nike's two corporate jets represent less than 0.1% of its total emissions, they have increased by 20% from 2015 to 2023. Partnership with Newlight In 2021, Nike announced they were working with Newlight Technologies to find more eco-friendly materials for their sneakers. They specifically mentioned Newlight's AirCarbon product which is a bioplastic that can be used to make shoes. The bioplastic is used as a replacement to leather, plastic, and other materials that are like that.Newlight was reported saying that the goal is to reduce Nike's carbon footprint.

Nike,_Inc.

P5991 - carbon footprint: total emissions caused by an individual, event, organisation, or product, expressed as carbon dioxide equivalent; has to be calculated via a scientificly proven methodology

Nike,_Inc.


Sustainability

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sustainability: Nike has taken steps to reduce its environmental impact. It has worked to reduce carbon emissions nearly 3% across its value chain from its FY11 baseline,and sourced from fewer, higher-performing contract factories. In 2019, Nike began a program called "Move to Zero" in an effort to achieve zero waste and zero carbon in the organization's supply chain and product lifetime.The men's and women's sections of the collection contain at least 60% organic and recycled materials, including sustainably sourced cotton.

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

[
    "Q25274 - 2019: year",
    "Q12681717 - 2019: natural number",
    "Q84263196 - COVID-19: contagious disease caused by SARS-CoV-2",
    "Q81068910 - COVID-19 pandemic: pandemic caused by SARS-CoV-2",
    "Q82069695 - SARS-CoV-2: virus that causes COVID-19"
]

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

[
    "Q3271915 - 60000: number",
    "Q17683526 - 60.000 запослених у Србији месецима не добија плату: Wikinews article",
    "Q17683514 - 60.000 potpisa za regionalnu saradnju u oblasti životne sredine: Wikinews article"
]

[
    "Q7101837 - organic cotton: cotton from non-genetically modified plants, and without the use of any synthetic agricultural chemicals such as fertilizers or pesticides",
    "Q58400817 - Organic Cotton Production as an Adaptation Option in North-West Benin: article",
    "Q56836971 - Organic cotton: production practices and post-harvest considerations: article"
]

[
    "Q94861615 - NCAA Division I men's basketball: college sports league in the United States",
    "Q101003088 - men's college basketball: basketball played by male college students",
    "Q6816139 - men's college basketball on television: None",
    "Q114316030 - Men's college basketball announcers in the United States: None",
    "Q135647351 - men's college basketball in the United States: overview of men's college basketball in the United States"
]

[
    "Q135647375 - women's college basketball: college basketball played by women",
    "Q135647352 - women's college basketball in the United States: overview of women's college basketball in the United States"
]

Output: None of the candidates


Sustainability

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sustainability: Nike has taken steps to reduce its environmental impact. It has worked to reduce carbon emissions nearly 3% across its value chain from its FY11 baseline,and sourced from fewer, higher-performing contract factories. In 2019, Nike began a program called "Move to Zero" in an effort to achieve zero waste and zero carbon in the organization's supply chain and product lifetime.The men's and women's sections of the collection contain at least 60% organic and recycled materials, including sustainably sourced cotton.

Nike,_Inc.

Nike,_Inc.