Products

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, Products: Nike produces a wide range of sports equipment and apparel.

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

Q768186 - sports equipment: object used for sport or exercise

Q24928712 - sporting goods manufacturer: company that manufactures sporting and athletic goods, including sports and fitness equipment

Q57264543 - sporting goods industry: the industry of producing and selling sporting goods

Q115755532 - sports equipment in French law: None

Q104650060 - Sports equipment in Ukraine: None

Nike,_Inc.

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

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

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

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

Nike,_Inc.

Q11460 - clothing: covering worn on the body

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

Q98690881 - apparel: ornamental embroidery or other decoration on ecclesiastical vestments

Q311249 - GQ: American monthly men's magazine

Q96326535 - Apparel: US scientific journal

Output: None of the candidates


Sports apparel

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, Sports apparel: Nike's first apparel products were track running shoes.Nike Air Maxis a line of shoes first released by Nike, Inc. in 1987. Additional product lines were introduced later, such as Air Huarache, which debuted in 1992. The most recent additions to their line are the Nike 6.0, Nike NYX, andNike SBshoes, designed for skateboarding. Nike has recently introduced cricket shoes called Air Zoom Yorker, designed to be 30% lighter than their competitors'.In 2008, Nike introduced the Air Jordan XX3, a high-performance basketball shoe designed with the environment in mind. Nike's range of products include shoes, jerseys, shorts,cleats,baselayers, etc. for sports activities such as soccer,basketball, track and field,combat sports, tennis,American football, athletics, golf,ice hockey, andcross trainingfor men, women, and children. Nike also sells shoes for activities such asskateboarding, baseball, cycling, volleyball,wrestling,cheerleading,lacrosse,cricket, aquatic activities, auto racing, and other athletic and recreational uses. Nike partnered withApple Inc.to produce theNike+product that monitors a runner's performance via a radio device in the shoe that links to theiPod nano. While the product generates useful statistics, it has been criticized by researchers who were able to identify users'RFIDdevices from 60 feet (18 m) away using small, concealable intelligencemotesin awireless sensor network. In 2004, Nike launched theSPARQ Training Program/Division.Some of Nike's newest shoes containFlywireand Lunarlite Foam to reduce weight.The Air Zoom Vomero running shoe, introduced in 2006 and currently in its 11th generation, featured a combination of groundbreaking innovations including a full-length air cushioned sole,an external heel counter, a crashpad in the heel for shock absorption, and Fit Frame technology for a stable fit. In 2023, Nike told ESPN that it would cease using kangaroo skins in its products by the end of that year and debut "a new Nike-only, proprietary synthetic upper, [with] a new material that is a better performance solution and replaces the use of kangaroo leather." The Nike Vaporfly first came out in 2017 and their popularity, along with its performance, prompted a new series of running shoes.The Vaporfly series has a new technological composition that has revolutionized long-distance running since studies have shown that these shoes can improve marathon race time up to 4.2%.The composition of the sole contains a foamy material, Pebax, that Nike has altered and now calls it ZoomX (which can be found in other Nike products as well). Pebax foam can also be found in airplane insulation and is "squishier, bouncier, and lighter" than foams in typical running shoes.In the middle of the ZoomX foam there is a full-length carbon fiber plate "designed to generate extra spring in every step".At the time of this writing Nike had just released its newest product from the Vaporfly line, the Nike ZoomX Vaporfly NEXT%, which was marketed as "the fastest shoe we’ve ever made" using Nike's "two most innovative technologies, Nike ZoomX foam and VaporWeave material".

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

[
    "Q4698273 - Nike Air Zoom Vomero: series of running shoes"
]

[
    "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"
]


Sports apparel

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, Sports apparel: Nike's first apparel products were track running shoes.Nike Air Maxis a line of shoes first released by Nike, Inc. in 1987. Additional product lines were introduced later, such as Air Huarache, which debuted in 1992. The most recent additions to their line are the Nike 6.0, Nike NYX, andNike SBshoes, designed for skateboarding. Nike has recently introduced cricket shoes called Air Zoom Yorker, designed to be 30% lighter than their competitors'.In 2008, Nike introduced the Air Jordan XX3, a high-performance basketball shoe designed with the environment in mind. Nike's range of products include shoes, jerseys, shorts,cleats,baselayers, etc. for sports activities such as soccer,basketball, track and field,combat sports, tennis,American football, athletics, golf,ice hockey, andcross trainingfor men, women, and children. Nike also sells shoes for activities such asskateboarding, baseball, cycling, volleyball,wrestling,cheerleading,lacrosse,cricket, aquatic activities, auto racing, and other athletic and recreational uses. Nike partnered withApple Inc.to produce theNike+product that monitors a runner's performance via a radio device in the shoe that links to theiPod nano. While the product generates useful statistics, it has been criticized by researchers who were able to identify users'RFIDdevices from 60 feet (18 m) away using small, concealable intelligencemotesin awireless sensor network. In 2004, Nike launched theSPARQ Training Program/Division.Some of Nike's newest shoes containFlywireand Lunarlite Foam to reduce weight.The Air Zoom Vomero running shoe, introduced in 2006 and currently in its 11th generation, featured a combination of groundbreaking innovations including a full-length air cushioned sole,an external heel counter, a crashpad in the heel for shock absorption, and Fit Frame technology for a stable fit. In 2023, Nike told ESPN that it would cease using kangaroo skins in its products by the end of that year and debut "a new Nike-only, proprietary synthetic upper, [with] a new material that is a better performance solution and replaces the use of kangaroo leather." The Nike Vaporfly first came out in 2017 and their popularity, along with its performance, prompted a new series of running shoes.The Vaporfly series has a new technological composition that has revolutionized long-distance running since studies have shown that these shoes can improve marathon race time up to 4.2%.The composition of the sole contains a foamy material, Pebax, that Nike has altered and now calls it ZoomX (which can be found in other Nike products as well). Pebax foam can also be found in airplane insulation and is "squishier, bouncier, and lighter" than foams in typical running shoes.In the middle of the ZoomX foam there is a full-length carbon fiber plate "designed to generate extra spring in every step".At the time of this writing Nike had just released its newest product from the Vaporfly line, the Nike ZoomX Vaporfly NEXT%, which was marketed as "the fastest shoe we’ve ever made" using Nike's "two most innovative technologies, Nike ZoomX foam and VaporWeave material".

Nike,_Inc.

Q483915 - Nike: American athletic equipment company

Q208513 - Whitesnake: 1987 studio album by Whitesnake

Q918472 - 1987 United Kingdom general election: election for members of the British House of Commons

Q701794 - 1987 West German federal election: election

Nike,_Inc.

Nike,_Inc.

Q4698273 - Nike Air Zoom Vomero: series of running shoes

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

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

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

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

Nike,_Inc.

Nike,_Inc.

Q125908302 - Air Max 270: shoe line by Nike

Q124980645 - Nike Air Max 270 React: None

Nike,_Inc.

Q125230840 - Nike Air Zoom Pegasus 38: model of running shoe by Nike

Nike,_Inc.

Nike,_Inc.

Q13653445 - Nike Skateboarding: clothing brand

Q124448994 - Slovak Basketball League 2023-24: Slovak basketball competition

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q41421 - Michael Jordan: American basketball player and businessman (born 1963)

Q420953 - Air Jordan: brand of shoes and athletic clothing designed, owned, and produced for Michael Jordan by Nike

Q108883102 - Michael Jordan: main protagonist in the 1996 film Space Jam

Q56279631 - Air Jordan: former airline (Jordan)

Q76562360 - Air Jordan: show jumping horse

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.


Street fashions

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, Street fashions: The Nike brand, with its distinctive "Swoosh" logo, quickly became regarded as a status symbolin modernurban fashionandhip-hop fashiondue to its association with success in sport.Beginning in the 1980s, various items of Nike clothing became staples of mainstream Americanyouth fashion, especially tracksuits,shell suits,baseball caps,Air Jordans, Air Force 1's, andAir Maxrunning shoeswith thick, air cushioned rubber soles and contrasting blue, yellow, green, white, or red trim.Limited edition sneakers and prototypes with a regional early release were known asQuickstrikes,and became highly desirable itemsfor teenage members of thesneakerhead subculture. By the 1990s and2000s, American and European teenagersassociated with thepreppyorpopular cliquebegan combining these sneakers,leggings, sweatpants,crop tops,and tracksuits with regularcasual chicstreet clothessuch as jeans, skirts,leg warmers,slouch socks, andbomber jackets. Particularly popularwere the unisex spandex Nike Tempocompression shortsworn for cycling and running,which had a mesh lining, waterproofing, and, later in the 2000s, a zip pocket for aWalkmanorMP3 player. From the late 2000s into the 2010s, Nike Elite basketball socks began to be worn as everyday clothes by hip-hop fans and young children.Originally plain white or black, these socks had special shock absorbing cushioning in the soleplus a moisture wicking upper weave.Later, Nike Elite socks became available in bright colors inspired by throwbackbasketball uniforms,often with contrasting bold abstract designs, images of celebrities,and freehand digital printto capitalize upon the emerging nostalgia for1990s fashion. In 2015, a new self-lacing shoe was introduced. Called theNike Mag, which are replicas of the shoes featured inBack to the Future Part II, it had a preliminary limited release, only available by auction with all proceeds going to theMichael J. Fox Foundation.This was done again in 2016. Nike have introduced a premium line, focused more on streetwear than sports wear called NikeLab. In March 2017, Nike announced its launch of a plus-size clothing line,which will feature new sizes 1X through 3X on more than 200 products.Another significant development at this time was theChuck Taylor All-Star Modern, an update of the classic basketball sneaker that incorporated the circular knit upper and cushioned foam sole of Nike's Air Jordans.

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

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

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

[
    "Q41421 - Michael Jordan: American basketball player and businessman (born 1963)",
    "Q420953 - Air Jordan: brand of shoes and athletic clothing designed, owned, and produced for Michael Jordan by Nike",
    "Q108883102 - Michael Jordan: main protagonist in the 1996 film Space Jam",
    "Q56279631 - Air Jordan: former airline (Jordan)",
    "Q76562360 - Air Jordan: show jumping horse"
]

[
    "Q3341503 - Nike Mag: brand of footwear"
]

[
    "Q507085 - The Michael J. Fox Foundation: non-profit organization in the USA",
    "Q35219481 - Michael J. Fox Foundation LRRK2 Consortium: geographical differences in returning genetic research data to study participants: scientific article"
]

[
    "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"
]

[
    "Q11401 - hip-hop: rhythmic music accompanied by rhyming speech",
    "Q100493654 - hip-hop musician: musician that plays hip-hop music",
    "Q2267754 - Northwest hip hop: music genre or scene",
    "Q4571124 - Devansu Rathva: Music production"
]

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "Q3341503 - Nike Mag: brand of footwear"
]

[
    "Q109331 - Back to the Future Part II: 1989 film by Robert Zemeckis",
    "Q4034784 - Back to the Future Part II: 1990 video game",
    "Q230552 - Back to the Future Part III: 1990 film by Robert Zemeckis",
    "Q1937111 - Back to the Future Part III: 1991 video game",
    "Q1942249 - Back to the Future Part II & III: 1990 video game released for the NES"
]

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

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "Q41421 - Michael Jordan: American basketball player and businessman (born 1963)",
    "Q420953 - Air Jordan: brand of shoes and athletic clothing designed, owned, and produced for Michael Jordan by Nike",
    "Q108883102 - Michael Jordan: main protagonist in the 1996 film Space Jam",
    "Q56279631 - Air Jordan: former airline (Jordan)",
    "Q76562360 - Air Jordan: show jumping horse"
]

[
    "Q507085 - The Michael J. Fox Foundation: non-profit organization in the USA",
    "Q35219481 - Michael J. Fox Foundation LRRK2 Consortium: geographical differences in returning genetic research data to study participants: scientific article"
]

[
    "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"
]

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "Q109331 - Back to the Future Part II: 1989 film by Robert Zemeckis",
    "Q4034784 - Back to the Future Part II: 1990 video game",
    "Q230552 - Back to the Future Part III: 1990 film by Robert Zemeckis",
    "Q1937111 - Back to the Future Part III: 1991 video game",
    "Q1942249 - Back to the Future Part II & III: 1990 video game released for the NES"
]

[
    "Q1893985 - hip hop fashion: hip hop clothing"
]

[
    "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"
]

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

Output: None of the candidates


Street fashions

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, Street fashions: The Nike brand, with its distinctive "Swoosh" logo, quickly became regarded as a status symbolin modernurban fashionandhip-hop fashiondue to its association with success in sport.Beginning in the 1980s, various items of Nike clothing became staples of mainstream Americanyouth fashion, especially tracksuits,shell suits,baseball caps,Air Jordans, Air Force 1's, andAir Maxrunning shoeswith thick, air cushioned rubber soles and contrasting blue, yellow, green, white, or red trim.Limited edition sneakers and prototypes with a regional early release were known asQuickstrikes,and became highly desirable itemsfor teenage members of thesneakerhead subculture. By the 1990s and2000s, American and European teenagersassociated with thepreppyorpopular cliquebegan combining these sneakers,leggings, sweatpants,crop tops,and tracksuits with regularcasual chicstreet clothessuch as jeans, skirts,leg warmers,slouch socks, andbomber jackets. Particularly popularwere the unisex spandex Nike Tempocompression shortsworn for cycling and running,which had a mesh lining, waterproofing, and, later in the 2000s, a zip pocket for aWalkmanorMP3 player. From the late 2000s into the 2010s, Nike Elite basketball socks began to be worn as everyday clothes by hip-hop fans and young children.Originally plain white or black, these socks had special shock absorbing cushioning in the soleplus a moisture wicking upper weave.Later, Nike Elite socks became available in bright colors inspired by throwbackbasketball uniforms,often with contrasting bold abstract designs, images of celebrities,and freehand digital printto capitalize upon the emerging nostalgia for1990s fashion. In 2015, a new self-lacing shoe was introduced. Called theNike Mag, which are replicas of the shoes featured inBack to the Future Part II, it had a preliminary limited release, only available by auction with all proceeds going to theMichael J. Fox Foundation.This was done again in 2016. Nike have introduced a premium line, focused more on streetwear than sports wear called NikeLab. In March 2017, Nike announced its launch of a plus-size clothing line,which will feature new sizes 1X through 3X on more than 200 products.Another significant development at this time was theChuck Taylor All-Star Modern, an update of the classic basketball sneaker that incorporated the circular knit upper and cushioned foam sole of Nike's Air Jordans.

Nike,_Inc.

Nike,_Inc.

Q116031595 - Air Max: 2020 single by Comethazine

Q4698092 - Air Max Africa: airline from Gabon

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

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q96395642 - Nike Elite Youth Basketball League: basketball circuit for teams of players aged 17 and under

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

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

P8972 - small logo or icon: logo or icon that represents the item and can be rendered at sizes as small as 16×16 pixels

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

P10286 - podcast logo URL: URL for the image used as the promotional cover of a podcast

P8058 - symbol of: item of which the image is a symbol

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

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

Q135073395 - SwooshCat: video game developed by PixelForestStudio

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

Nike,_Inc.

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.

Q608441 - tracksuit: clothing article consisting of two parts: trousers and a jacket usually with front zipper

Q7831601 - Tracksuit Manager: 1988 video game

Q2426768 - tracksuit bottoms: soft trousers made for athletic or leisure purposes

Q126184638 - Tracksuit Manager 90: 1990 video game

Q126187794 - Tracksuit Manager 2: 1997 Update Disk: 1997 video game

Nike,_Inc.

Q660782 - leggings: type of garment that covers the leg, usually up to the ankle

Q104650737 - Leggings: painting by Tadeusz Brzozowski

Q64571189 - Leggings: drawing in the National Gallery of Art (NGA 24191)

Q3651083 - tights: heavy, opaque stockings woven in one with panties

Q3059530 - hose: historical men's clothing for the legs and lower body

Nike,_Inc.

Q7547485 - sneaker collecting: person who collects, trades, or admires sneakers as a hobby

Q52146202 - Sneakerhead: episode of Numbers (S5 E14)

Q99510550 - Sneakerheads: 2020 American television series

Q50331007 - Sneakerheadz: 2015 documentary film by David T. Friendly and Mick Partridge

Q124966474 - Sneakerheads: episode of Apple & Onion (S2 E29)

Nike,_Inc.

Q91540 - Back to the Future: 1985 film by Robert Zemeckis

Q91558 - Back to the Future: 1985–1990 trilogy of films directed by Robert Zemeckis

Q113403402 - Back to the Future: 1986 MSX and PC88 video game

Q98112763 - Back to the Future: science fiction media franchise

Q230520 - Back to the Future: The Animated Series: animated television series

Output: None of the candidates


Collectibles

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, Collectibles: On July 23, 2019, a pair of Nike Inc. running shoes sold for $437,500 at aSotheby'sauction. The so-called "Moon Shoes"were designed by Nike co-founder and track coach Bill Bowerman for runners participating in the 1972 Olympics trials. The buyer wasMiles Nadal, a Canadian investor and car collector, who had just paid $850,000 for a group of 99 rare of limited collection pairs of sport shoes. The purchase price was the highest for one pair of sneakers, the previous record being $190,373 in 2017 for a pair of signedConverseshoes in California, said to have been worn byMichael Jordanduring the 1984 basketball final of the Olympics that year.

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

[
    "Q861967 - Bill Bowerman: American runner, track and field coach, and businessman (1911–1999)",
    "Q22021876 - Bill Bowerman: outdoor 2000 sculpture by Diana Lee Jackson"
]

[
    "Q17077739 - Moon shoes: Trampoline Toy"
]

[
    "Q50824633 - Miles Nadal: International entrepreneur and philanthropist",
    "Q128627714 - Miles Nadal Jewish Community Centre: community centre in Toronto, Canada"
]

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

[
    "Q99 - California: state of the United States of America",
    "Q16047894 - California: genus of plants",
    "Q93480414 - California: vocal track by Grimes; 2015 studio recording",
    "Q104057194 - California: Italian singer and disc jockey (1990-)",
    "Q161562 - California Institute of Technology: private research university located in Pasadena, California, USA"
]

[
    "Q41421 - Michael Jordan: American basketball player and businessman (born 1963)",
    "Q1928047 - Michael Jordan: German draughtsperson, artist and comics artist",
    "Q6831716 - Michael Jordan: English footballer (born 1984)",
    "Q3308285 - Michael I. Jordan: American computer scientist, University of California, Berkeley",
    "Q6831719 - Michael Jordan: British mycologist, writer, and television presenter"
]

Output: None of the candidates

Output: None of the candidates


Collectibles

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, Collectibles: On July 23, 2019, a pair of Nike Inc. running shoes sold for $437,500 at aSotheby'sauction. The so-called "Moon Shoes"were designed by Nike co-founder and track coach Bill Bowerman for runners participating in the 1972 Olympics trials. The buyer wasMiles Nadal, a Canadian investor and car collector, who had just paid $850,000 for a group of 99 rare of limited collection pairs of sport shoes. The purchase price was the highest for one pair of sneakers, the previous record being $190,373 in 2017 for a pair of signedConverseshoes in California, said to have been worn byMichael Jordanduring the 1984 basketball final of the Olympics that year.

Nike,_Inc.

Q5389 - Olympic Games: major international multi-sport event organised by International Olympic Committee

Q58825429 - Olympic competitor: athlete participating in the Olympic Games

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

Q114505677 - Olympics: episode of Yo Gabba Gabba! (S4 E6)

Q109682174 - Olympics: episode of Superstore

Nike,_Inc.

Q99 - California: state of the United States of America

Q93480414 - California: vocal track by Grimes; 2015 studio recording

Q104057194 - California: Italian singer and disc jockey (1990-)

Q161562 - California Institute of Technology: private research university located in Pasadena, California, USA

Nike,_Inc.

Q41421 - Michael Jordan: American basketball player and businessman (born 1963)

Q1928047 - Michael Jordan: German draughtsperson, artist and comics artist

Q6831716 - Michael Jordan: English footballer (born 1984)

Q3308285 - Michael I. Jordan: American computer scientist, University of California, Berkeley

Q6831719 - Michael Jordan: British mycologist, writer, and television presenter

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Q50824633 - Miles Nadal: International entrepreneur and philanthropist

Q128627714 - Miles Nadal Jewish Community Centre: community centre in Toronto, Canada

Nike,_Inc.

Nike,_Inc.

Nike,_Inc.

Output: None of the candidates


Virtual

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, Virtual: After acquiring RTFKT, Nike launched the Dunk Genesis Cryptokicks collection, which features over 20,000 NFTs.One design byTakashi Murakamiwas sold for $134,000 in April 2022.

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

[
    "Q90097519 - Takashi Murakami: researcher (ORCID 0000-0002-0250-0856)",
    "Q352437 - Takashi Murakami: Japanese contemporary artist, film director (born in 1962)",
    "Q47645497 - Takashi Murakami: researcher (ORCID 0000-0002-9429-5705)",
    "Q114083086 - Takashi Murakami: Japanese medical researcher",
    "Q50803995 - Takashi Murakami: researcher ORCID: 0000-0002-2661-2633"
]

[
    "Q69306412 - April 27, 2022: date in Gregorian calendar",
    "Q123651857 - 2022-04-27 ALBA Berlin gegen Hamburg Towers: None"
]

Output: None of the candidates


Virtual

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, Virtual: After acquiring RTFKT, Nike launched the Dunk Genesis Cryptokicks collection, which features over 20,000 NFTs.One design byTakashi Murakamiwas sold for $134,000 in April 2022.

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.

Q69306386 - April 1, 2022: date in Gregorian calendar

Nike,_Inc.

Dunk_Genesis_Cryptokicks

Q1488587 - 200000 Danielparrott: asteroid

Dunk_Genesis_Cryptokicks

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

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

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

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

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

Q90097519 - Takashi Murakami: researcher (ORCID 0000-0002-0250-0856)

Q352437 - Takashi Murakami: Japanese contemporary artist, film director (born in 1962)

Q47645497 - Takashi Murakami: researcher (ORCID 0000-0002-9429-5705)

Q114083086 - Takashi Murakami: Japanese medical researcher

Q50803995 - Takashi Murakami: researcher ORCID: 0000-0002-2661-2633

Dunk_Genesis_Cryptokicks

P2284 - price: published price listed or paid for a product (use with unit of currency)

P9622 - Price One Penny work ID: identifier for a work in the Price One Penny database

P4030 - PLU code: identification numbers affixed to fruits and vegetables for sale, used in checkout and inventory

P9619 - Price One Penny author ID: identifier for an author in the Price One Penny database

P9623 - Price One Penny library ID: identifier for a library in the Price One Penny database

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Headquarters

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, Headquarters: Nike's world headquarters are surrounded by the city of Beaverton but are withinunincorporatedWashington County. The city attempted to forcibly annex Nike's headquarters, which led to a lawsuit by Nike, andlobbyingby the company that ultimately ended in Oregon Senate Bill 887 of 2005. Under that bill's terms, Beaverton is specifically barred from forcibly annexing the land that Nike andColumbia Sportswearoccupy in Washington County for 35 years, whileElectro Scientific IndustriesandTektronixreceive the same protection for 30 years. Nike is planning to build a 3.2 million square foot expansion to its World Headquarters in Beaverton.The design will targetLEEDPlatinum certification and will be highlighted by natural daylight, and a gray water treatment center.

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

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

[
    "Q484538 - Washington County: county in Oregon, United States",
    "Q94164180 - 2020 COVID-19 pandemic in Washington County, Oregon: pandemic in Washington County, Oregon"
]

[
    "Q39121 - Leeds: city in West Yorkshire, England, United Kingdom",
    "Q503424 - University of Leeds: university in Leeds, United Kingdom",
    "Q1202117 - Leeds Rhinos: English professional rugby league football club",
    "Q7366 - song: musical composition for voice(s)",
    "Q774015 - Leeds: metropolitan borough in West Yorkshire, England"
]

[
    "Q1112588 - Columbia Sportswear Company: United States company that manufactures and distributes outerwear and sportswear",
    "Q132081660 - Columbia Sportswear Europe: None"
]

[
    "Q1347679 - Tektronix: company",
    "Q134684099 - Tektronix: None",
    "Q7695508 - Tektronix extended HEX: file format",
    "Q7695504 - Tektronix 405x: None",
    "Q105856651 - Tektronix TDS extended waveform data: file format"
]

[
    "Q115367385 - Gray Water Recovery System Model by Solar Photocatalysis with TiO2 Nanoparticles for Crop Irrigation: scientific article published in 2021",
    "Q56743142 - Gray water recycle: Effect of pretreatment technologies on low pressure reverse osmosis treatment: article by Veera M. Boddu et al published December 2016 in Journal of environmental chemical engineering",
    "Q43848136 - Gray water reuse in domestic environment: implications for public health: scientific article published in May 2010"
]

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

[
    "Q111604053 - LEED Platinum: LEED certification level"
]

[
    "Q34504242 - Natural daylight restricted to twilights delays the timing of testicular regression but does not affect the timing of the daily activity rhythm of the house sparrow (Passer domesticus): scientific article"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Headquarters

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, Headquarters: Nike's world headquarters are surrounded by the city of Beaverton but are withinunincorporatedWashington County. The city attempted to forcibly annex Nike's headquarters, which led to a lawsuit by Nike, andlobbyingby the company that ultimately ended in Oregon Senate Bill 887 of 2005. Under that bill's terms, Beaverton is specifically barred from forcibly annexing the land that Nike andColumbia Sportswearoccupy in Washington County for 35 years, whileElectro Scientific IndustriesandTektronixreceive the same protection for 30 years. Nike is planning to build a 3.2 million square foot expansion to its World Headquarters in Beaverton.The design will targetLEEDPlatinum certification and will be highlighted by natural daylight, and a gray water treatment center.

Nike,_Inc.

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

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

Nike,_Inc.

Q484538 - Washington County: county in Oregon, United States

Q94164180 - 2020 COVID-19 pandemic in Washington County, Oregon: pandemic in Washington County, Oregon

Nike,_Inc.

Output: None of the candidates

Output: None of the candidates


Ownership

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, Ownership: Nike is mainly owned by institutional investors, who hold around 68% of all shares. The 10 largest shareholders of Nike in early 2024 were:

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

[
    "Q79381677 - Institutional Investors Group on Climate Change: organization",
    "Q110720028 - Institutional Investors: Arbitrageurs or Rational Trend Chasers: doctoral thesis by Yeqin Zeng, University of Washington, 2013",
    "Q120645703 - Institutional investors, corporate financial decisions and performance in UK firms: doctoral thesis",
    "Q110716479 - Institutional Investors Must Help Close the Race and Gender Gaps in Venture Capital: magazine article from 'Harvard Business Review' published in 2020",
    "Q60731747 - Institutional Investors and Equity Prices: article published in 2001"
]

[
    "Q69307032 - January 1, 2024: date in Gregorian calendar"
]

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

Output: None of the candidates


Ownership

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, Ownership: Nike is mainly owned by institutional investors, who hold around 68% of all shares. The 10 largest shareholders of Nike in early 2024 were:

Nike,_Inc.

Q1665188 - institutional investor: investors who invest professionally and as their main occupation in the stock market

Q11095058 - Institutional Investor: monthly magazine founded in 1967

Q79381677 - Institutional Investors Group on Climate Change: organization

Q110720028 - Institutional Investors: Arbitrageurs or Rational Trend Chasers: doctoral thesis by Yeqin Zeng, University of Washington, 2013

Q110719872 - Institutional Investor Myopia, Ownership, Earnings, and Returns: doctoral thesis by Michael Joseph Eames, University of Washington, 1995

Nike,_Inc.


Controversies

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, Controversies: Nike has contracted with more than 700 shops around the world and has offices located in 45 countries outside the United States.Most of the factories are located in Asia, including Indonesia, China, Taiwan, India,Thailand, Vietnam, Pakistan, Philippines, and Malaysia.Nike is hesitant to disclose information about the contract companies it works with. However, due to harsh criticism from some organizations like CorpWatch, Nike has disclosed information about its contract factories in its Corporate Governance Report.

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

[
    "Q721126 - 700: natural number",
    "Q23494 - 700: year",
    "Q21065230 - Campylobacter jejuni subsp. jejuni NCTC 11168 = ATCC 700819: bacterial strain",
    "Q1142538 - 7000: number",
    "Q367823 - 7002 Bronshten: asteroid"
]

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

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

[
    "Q129028517 - Corporate governance report compliance rate and accounting conservatism: New evidence from Korea: scholarly article",
    "Q127152439 - Corporate governance reporting in Bangladesh: scholarly article"
]

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

[
    "Q627861 - 45: natural number",
    "Q6128115 - 7″ single: vinyl single; type of physical music format",
    "Q87533390 - ㊺: Unicode character",
    "Q1087 - rhodium: chemical element with symbol Rh and atomic number 45",
    "Q30313 - 45: year"
]

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

[
    "Q5172354 - CorpWatch: organization",
    "Q58588955 - CorpWatch: None"
]

Output: None of the candidates


Controversies

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, Controversies: Nike has contracted with more than 700 shops around the world and has offices located in 45 countries outside the United States.Most of the factories are located in Asia, including Indonesia, China, Taiwan, India,Thailand, Vietnam, Pakistan, Philippines, and Malaysia.Nike is hesitant to disclose information about the contract companies it works with. However, due to harsh criticism from some organizations like CorpWatch, Nike has disclosed information about its contract factories in its Corporate Governance Report.

Nike,_Inc

Nike,_Inc

Nike,_Inc

Q177837 - Japanese Wikipedia: Japanese-language edition of Wikipedia

Q48 - Asia: biggest continent in the world

Nike,_Inc

Q252 - Indonesia: island country in Southeast Asia and Oceania

Q9240 - Indonesian: official language of Indonesia

Q155214 - Indonesian Wikipedia: Indonesian-language edition of Wikipedia

Q112136164 - Indonesian Minister of Home Affairs Decree Number 050-145 of 2022: Decree of the Minister of Home Affairs of the Republic of Indonesia Number 050-145 of 2022 concerning the Granting and Updating of Codes, Government Administration Area Data, and Islands in 2021

Q109692162 - Indonesian 2009 DPR Ballot: None

Nike,_Inc

Q881 - Vietnam: country in Southeast Asia

Q9199 - Vietnamese: Austroasiatic language originating in Vietnam

Q200180 - Vietnamese Wikipedia: Vietnamese-language edition of Wikipedia

Q8740 - Vietnam War: armed conflict in Vietnam, Laos, and Cambodia between North Vietnam and South Vietnam, from 1955 to 1975

Q430309 - Dai Viet: Vietnamese monarchy (10th–19th century)

Nike,_Inc

Q928 - Philippines: archipelagic country in Southeast Asia

Q2652138 - National Historical Commission of the Philippines: Philippine government agency

Q21562164 - National Historical Commission of the Philippines historical marker: commemorative plaque or set of commemorative plaques created by the National Historical Commission of the Philippines or any of its predecessors

Q18579098 - Member of the Senate of the Philippines: None

Q219854 - Philippines men's national association football team: men's national association football team representing the Philippines

Nike,_Inc

Q833 - Malaysia: country in Southeast Asia

Q17374096 - Malaysia Airlines Flight 17: passenger flight from Amsterdam to Kuala Lumpur

Q15065 - Malaysian Malay: standard Malay dialect spoken in Malaysia

Q23329683 - Malaysian Journal of Pathology: medical journal

Q318922 - Malaysia men's national football team: men's national association football team representing Malaysia

Nike,_Inc

Q843 - Pakistan: sovereign state in South Asia

Q2006542 - Dominion of Pakistan: period of Pakistani history between 1947 to 1956

Q3310017 - Pakistanis: citizens and national of the Republic Pakistan

Q2342822 - West Pakistan: Western Wing of Pakistan between 1947–1970

Q15746372 - Pakistan Journal of Pharmaceutical Sciences: scientific journal

Nike,_Inc

Q22502 - Taiwan Island: island in East Asia; main land of the Republic of China (Taiwan)

Q137816 - Taiwan under Japanese rule: period of Taiwanese history (1895–1945)

Q262828 - Standard Taiwanese Mandarin: variety of Modern Standard Chinese serving as the official language of the Republic of China (Taiwan)

Nike,_Inc

Q148 - People's Republic of China: country in East Asia

Q29520 - China: cultural region, ancient civilization, and nation in East Asia, mostly refer to the People's Republic of China in political situation and rarely refer to the Republic of China

Nike,_Inc

Q129286 - British Raj: British colonial rule on the Indian subcontinent (1858–1947)

Q1775277 - Dominion of India: period of Indian history between 1947 and 1950

Q1415 - Indiana: state of the United States of America

Nike,_Inc

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

Nike,_Inc

Nike,_Inc

Q129028517 - Corporate governance report compliance rate and accounting conservatism: New evidence from Korea: scholarly article

Q127152439 - Corporate governance reporting in Bangladesh: scholarly article

Output: None of the candidates