Global structure

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: Heineken_N, Global structure: Heineken organises the company into five territories which are then divided into regional operations.The regions are: Western Europe, Central and Eastern Europe, The Americas,  Asia Pacific and Africa and the Middle East. These territories contain 115 brewing plants in more than 65 countries,brewing local brands in addition to the Heineken brand.

Heineken

Heineken

Output: None of the candidates


Executive team

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: Heineken_N, Executive team: The executive team of the company consists of the following people:

[
    "Q180855 - Heineken: Dutch beer company"
]

[
    "Q114121457 - Executive team changes at Xylem: scientific article published in 2020",
    "Q69957120 - Executive teamwork at the University of Michigan Medical Center: scientific article published on 01 January 1989",
    "Q46303047 - Executive teamwork at the University of Michigan Medical Center: scientific article",
    "Q70833918 - Executive team development: scientific article published on 01 September 1983",
    "Q121026257 - Executive Team Pay Gap, Enterprise Strategic Change and Breakthrough Innovation——The Regulating Role of Internal Control: scholarly article in Science of Science and Management of S.& T., no. 5, 2022"
]

Output: None of the candidates

Output: None of the candidates


Brewing plants

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: Heineken_N, Brewing plants: Heineken's brewing plants have been designed and engineered in 4 main parts of the world. Heineken has 23 operating companies inEurope: Heineken has 9 operating companies inthe Americas: Heineken has 15 operating companies inAsia Pacific: Heineken has 18 operating companies in Africa and the Middle East.These include:

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q48240 - 23: natural number",
    "Q87533183 - ㉓: Unicode character",
    "Q23355 - 8: natural number",
    "Q110738526 - 23: Swedish rapper of Moroccan origin (born 1994)",
    "Q23672 - 23: year"
]

[
    "Q46 - Europe: continent in the Northern Hemisphere",
    "Q185144 - Europe: Swedish rock band",
    "Q458 - European Union: political and economic union of 27 European states",
    "Q5412157 - Europe PubMed Central: database of biomedical research",
    "Q198697 - Europe: 1983 debut studio album by Europe"
]

[
    "Q19108 - 9: natural number",
    "Q16549259 - Cirno: fictional character from Touhou Project",
    "Q11033689 - 9: numerical digit",
    "Q47246828 - published more than 95 years ago: sufficient condition for a work's copyright to have expired in the United States",
    "Q10806 - September 11 attacks: 2001 attacks in the United States"
]

[
    "Q828 - Americas: North America and South America taken together as a single continent or landmass",
    "Q27719421 - Américas: journal of the Organization of American States",
    "Q73041133 - Americas: album by Carmina Slovenica",
    "Q4745666 - Americas Society: non-profit organization in New York, New York",
    "Q690256 - history of the Americas: aspect of history, focusing on the America's"
]

[
    "Q38712 - 18: natural number",
    "Q6985 - UTC+08:00: identifier for a time offset from UTC of +8",
    "Q7847 - 1897: calendar year",
    "Q7840 - 1893: year",
    "Q21571490 - Category V: category in the NMHH rating system"
]

[
    "Q1070940 - Asia-Pacific: part of the world in or near the Western Pacific Ocean; typically includes East Asia, South Asia, Southeast Asia, and Oceania",
    "Q15753531 - Asia Pacific Journal of Clinical Nutrition: journal",
    "Q4806254 - Asia-Pacific Journal of Public Health: journal",
    "Q184425 - Pacific War: theater of World War II fought in the Pacific and Asia",
    "Q15757236 - Asia Pacific Journal of Clinical Oncology: journal"
]

[
    "Q108278853 - Africa and The Middle East Telecom: periodical",
    "Q57454762 - Africa and the Middle East: article"
]

Output: None of the candidates


Beer brands

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: Heineken_N, Beer brands: Heineken International owns a worldwide portfolio of over 170 beer brands, mainlypale lager, though some otherbeer stylesare produced. The two largest international brands areHeinekenandAmstel; though the portfolio includesBirra Moretti, Edelweiss,Lagunitas,Sol,DesperadosandTiger.Other regional brands includeAffligem,GösserandSagresin Europe,Dos Equis,Red StripeandTecateinAmericas,Bintang,Kingfisherand South Pacific Export in Asia-Pacific.

[
    "Q180855 - Heineken: Dutch beer company",
    "Q131222468 - Heineken International B.V.: None"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q15709638 - pale lager: beer style"
]

[
    "Q9898 - Amstelveen: town and municipality in North Holland, the Netherlands",
    "Q50720 - Amsterdam Amstel: railway station in Amsterdam, Netherlands",
    "Q185405 - Amstel: river in the Netherlands",
    "Q1947050 - Amstel: street in Amsterdam, the Netherlands",
    "Q2334502 - Jan van Amstel: Northern Renaissance painter (1500-1542)"
]

[
    "Q865840 - Birra Moretti: Italian brewery",
    "Q2254825 - Birra Moretti Trophy: football tournament"
]

[
    "Q3136656 - Dos Equis: Mexican beer",
    "Q112201202 - Dos Equis II: painting by Robert Barber",
    "Q1056430 - HEINEKEN Mexico: Brewery based in Monterrey, Nuevo León, México",
    "Q49914079 - Dos Equis: None"
]

[
    "Q37320952 - Gosser: family name",
    "Q696650 - Gösser: brand of beer",
    "Q21871109 - Gössering: watercourse in the Gailtal Alps in Carinthia, tributary of Gail river",
    "Q21879156 - Gösser: mountain in the Grazer Bergland in Styria",
    "Q107436797 - Gosserez: family name"
]

[
    "Q2320825 - Municipio de Tecate: municipality of Baja California, Mexico",
    "Q1020773 - Tecate: city in Baja California, Mexico",
    "Q476569 - Tecate: unincorporated community in California, USA",
    "Q3625680 - Tecate: Mexican beer brand",
    "Q35360975 - Tecate: stream in Baja California, Mexico"
]

[
    "Q382860 - Affligem: municipality in Flemish Brabant, Belgium",
    "Q16010125 - Affligem Brewery: brewery in Belgium",
    "Q2561868 - Affligem beer: None",
    "Q175033 - Affligem Abbey: historic religious building located in Belgium",
    "Q123933329 - Affligem: Belgian periodical"
]

[
    "Q9898 - Amstelveen: town and municipality in North Holland, the Netherlands",
    "Q50720 - Amsterdam Amstel: railway station in Amsterdam, Netherlands",
    "Q185405 - Amstel: river in the Netherlands",
    "Q1947050 - Amstel: street in Amsterdam, the Netherlands",
    "Q2334502 - Jan van Amstel: Northern Renaissance painter (1500-1542)"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q9634978 - Bintang: district in Central Aceh Regency, Aceh Province, Indonesia",
    "Q774859 - Bintan Island: island in Bintan Regency, Riau Islands Province, Indonesia",
    "Q13490880 - Bintang: subdistrict in Pangkal Pinang City, Bangka Belitung Islands, Indonesia",
    "Q99864660 - Bintang: family name",
    "Q12709252 - Bintang: village in Dairi Regency, North Sumatra, Indonesia"
]

[
    "Q1862698 - Tiger Beer: Singaporean brand of beer"
]

Output: None of the candidates

Output: None of the candidates


Beer brands

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: Heineken_N, Beer brands: Heineken International owns a worldwide portfolio of over 170 beer brands, mainlypale lager, though some otherbeer stylesare produced. The two largest international brands areHeinekenandAmstel; though the portfolio includesBirra Moretti, Edelweiss,Lagunitas,Sol,DesperadosandTiger.Other regional brands includeAffligem,GösserandSagresin Europe,Dos Equis,Red StripeandTecateinAmericas,Bintang,Kingfisherand South Pacific Export in Asia-Pacific.

Heineken_International

Q131222468 - Heineken International B.V.: None

Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International

Q64549253 - Heineken: painting by Idelle Weber

Heineken_International

Q9898 - Amstelveen: town and municipality in North Holland, the Netherlands

Q50720 - Amsterdam Amstel: railway station in Amsterdam, Netherlands

Q185405 - Amstel: river in the Netherlands

Q1947050 - Amstel: street in Amsterdam, the Netherlands

Q2334502 - Jan van Amstel: Northern Renaissance painter (1500-1542)

Heineken_International

Q865840 - Birra Moretti: Italian brewery

Q2254825 - Birra Moretti Trophy: football tournament

Heineken_International

Q1200857 - Desperados: video game series

Q2646636 - Desperados: lager produced by Fischer Brewery

Q2972773 - Desperados: collective amusement ride

Q904367 - Desperados: Wanted Dead or Alive: 2001 video game

Heineken_International

Q112201202 - Dos Equis II: painting by Robert Barber

Q1056430 - HEINEKEN Mexico: Brewery based in Monterrey, Nuevo León, México

Heineken_International

Q21871109 - Gössering: watercourse in the Gailtal Alps in Carinthia, tributary of Gail river

Q21879156 - Gösser: mountain in the Grazer Bergland in Styria

Heineken_International

Q1013136 - Kingfisher: city in and the county seat of Kingfisher County, Oklahoma, United States

Q1858886 - Vought OS2U Kingfisher: naval observation floatplane family by Chance-Vought

Heineken_International

Q634165 - Lagunitas: unincorporated community in California, United States

Q56475540 - Lagunitas: human settlement in Sonora, Mexico

Q1800568 - Lagunitas-Forest Knolls: census-designated place in Marin County, California

Q6473071 - Lagunitas Creek: Stream in California, United States

Q6473070 - Lagunitas Brewing Company: brewery founded in Lagunitas, California

Heineken_International

Q642291 - FC Dallas: soccer club in Frisco, Texas, USA

Q1090779 - Prodasineura verticalis: species of insect

Q102229035 - Peppermint Butler: fictional character from Adventure Time

Q62470269 - Red Stripe with Green Background: painting by Felrath Hines

Heineken_International

Q2320825 - Municipio de Tecate: municipality of Baja California, Mexico

Q1020773 - Tecate: city in Baja California, Mexico

Q476569 - Tecate: unincorporated community in California, USA

Q35360975 - Tecate: stream in Baja California, Mexico

Heineken_International

Q61968665 - Tiger: Name: given name and family name

Heineken_International

Heineken_International

Heineken_International

Q746406 - 170: natural number
Q31282 - 170: year
Q110319 - 170 Maria: main-belt asteroid
Q3225762 - 1709: natural number
Q6813 - 1700: year

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: Heineken_N, Ownership: The shares of Heineken N.V are traded on theNYSE Euronext AmsterdamandOTCQXunder the symbols:HEIAandHEINYrespectively. As at May 2023, the shareholding in the group's stock was as depicted in the table below:

[
    "Q180855 - Heineken: Dutch beer company"
]

[
    "Q13677 - New York Stock Exchange: American stock exchange",
    "Q65022476 - New York Stock Exchange: print in the National Gallery of Art (NGA 9575)",
    "Q74039012 - New York Stock Exchange: print in the National Gallery of Art (NGA 61327)",
    "Q76443001 - New York Stock Exchange: print in the National Gallery of Art (NGA 146035)",
    "Q65575956 - New York Stock Exchange: print in the National Gallery of Art (NGA 54745)"
]

[
    "Q9899 - Amsterdam: municipality in the Netherlands, containing the cities of Amsterdam and Weesp",
    "Q727 - Amsterdam: capital and most populous city of the Netherlands",
    "Q37471983 - Amsterdam: family name",
    "Q190804 - Rijksmuseum: museum in Amsterdam, Netherlands",
    "Q2011634 - Yef Miossu: island in Tambrauw Regency, Southwest Papua Province, Indonesia"
]

[
    "Q69306816 - May 31, 2023: date in Gregorian calendar"
]

[
    "Q180855 - Heineken: Dutch beer company"
]

Output: None of the candidates

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: Heineken_N, Ownership: The shares of Heineken N.V are traded on theNYSE Euronext AmsterdamandOTCQXunder the symbols:HEIAandHEINYrespectively. As at May 2023, the shareholding in the group's stock was as depicted in the table below:

Heineken_N.V.

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

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

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

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

Heineken_N.V.

Q13677 - New York Stock Exchange: American stock exchange

Q65022476 - New York Stock Exchange: print in the National Gallery of Art (NGA 9575)

Q74039012 - New York Stock Exchange: print in the National Gallery of Art (NGA 61327)

Q76443001 - New York Stock Exchange: print in the National Gallery of Art (NGA 146035)

Q65575956 - New York Stock Exchange: print in the National Gallery of Art (NGA 54745)

Heineken_N.V.

Q107188706 - OTCQX Marketplace: Electronic quotation system for premium market tier (operated by Pink Sheets)

Heineken_N.V.

Q9899 - Amsterdam: municipality in the Netherlands, containing the cities of Amsterdam and Weesp

Q727 - Amsterdam: capital and most populous city of the Netherlands

Q190804 - Rijksmuseum: museum in Amsterdam, Netherlands

Q2011634 - Yef Miossu: island in Tambrauw Regency, Southwest Papua Province, Indonesia

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Advertising

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Heineken_N, Advertising: Heineken's main advertising slogan in the UK was "Refreshes the parts other beers cannot reach",some of which featured voice-over narration by Danish comedian/pianistVictor Borge. The British TV campaign ran for over 30 years – stopping in 2005.From March 2011 they have been advertising using the song 'The Golden Age' byThe Asteroids Galaxy Tour. After the success of The Entrance, a web advert (4M views in YouTube), Heineken launched The Date in May 2011. In March 2017 in Amsterdam, Heineken opened a pop-up bakery for five days to promote the yeast used in its brewing. The bread was made by Mark Plaating and proceeds were donated to a local baking guild.

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q7925742 - Victor Borge: Norwegian rock musician",
    "Q251417 - Victor Borge: Danish comedian and pianist (1909–2000)",
    "Q10390340 - Victor Borges: Portuguese architecture and writer (1962-2012)",
    "Q52146819 - Victor M. Borges: researcher",
    "Q7925740 - Victor Borges Plads: public square in Østerbro, Copenhagen, Denmark"
]

[
    "Q114519751 - The Date: episode of The Amazing World of Gumball (S1 E27)",
    "Q114509436 - The Date: episode of Love (S1 E5)",
    "Q114506403 - The Date: episode of Blossom (S5 E17)",
    "Q114465766 - The Date: episode of The Doris Day Show (S1 E27)",
    "Q114527910 - The Date: episode of Archie Bunker's Place (S3 E3)"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q114519751 - The Date: episode of The Amazing World of Gumball (S1 E27)",
    "Q114509436 - The Date: episode of Love (S1 E5)",
    "Q114506403 - The Date: episode of Blossom (S5 E17)",
    "Q114465766 - The Date: episode of The Doris Day Show (S1 E27)",
    "Q114527910 - The Date: episode of Archie Bunker's Place (S3 E3)"
]

[
    "Q9899 - Amsterdam: municipality in the Netherlands, containing the cities of Amsterdam and Weesp",
    "Q727 - Amsterdam: capital and most populous city of the Netherlands",
    "Q37471983 - Amsterdam: family name",
    "Q190804 - Rijksmuseum: museum in Amsterdam, Netherlands",
    "Q2011634 - Yef Miossu: island in Tambrauw Regency, Southwest Papua Province, Indonesia"
]

[
    "Q5356025 - The Entrance: state electoral district of New South Wales, Australia",
    "Q7732312 - The Entrance: town in New South Wales, Australia",
    "Q7732313 - The Entrance: 2006 film by Damon Vignale",
    "Q114815300 - The Entrance: embroidered panel by Phoebe Anna Traquair",
    "Q119344150 - The Entrance: painting by Louis James (1920–1996), Laing Art Gallery"
]

[
    "Q274393 - bakery: establishment that produces and sells flour-based food baked in an oven",
    "Q124063327 - Bakery: 2017 video game",
    "Q7715368 - The Bakery: 1921 film",
    "Q112249140 - Bakery Simulator: 2022 video game",
    "Q21619846 - Bakery: painting by Paul Gorge"
]

[
    "Q145 - United Kingdom: country in north-west Europe",
    "Q174193 - United Kingdom of Great Britain and Ireland: historical sovereign state (1801–1922)",
    "Q7979 - British English: forms of the English language used in Britain",
    "Q161885 - Kingdom of Great Britain: constitutional monarchy in Western Europe (1707–1800)",
    "Q11010 - Parliament of the United Kingdom: supreme legislative body of the United Kingdom"
]

[
    "Q22663018 - March 1, 2005: date",
    "Q17874387 - 2005-03-01: Papież odzyskuje głos: Wikinews article",
    "Q17874382 - 2005-03-01: Od 1 marca obowiązuje 22 proc. VAT na Internet: Wikinews article",
    "Q17874391 - 2005-03-01: Zmasowany atak na Radio Maryja trwa: Wikinews article"
]

[
    "Q274393 - bakery: establishment that produces and sells flour-based food baked in an oven",
    "Q124063327 - Bakery: 2017 video game",
    "Q7715368 - The Bakery: 1921 film",
    "Q112249140 - Bakery Simulator: 2022 video game",
    "Q21619846 - Bakery: painting by Paul Gorge"
]

[
    "Q134967775 - 5.0: 2019 video game developed by Tamerlan Satualdypov",
    "Q2457418 - 5.0: 2010 studio album by Nelly",
    "Q54994751 - 5.0: competition class in Le Mans endurance racing",
    "Q113962579 - 5.0 GPA scale: rating of student performance on a 0-5 scale",
    "Q134394626 - 5.00 Return to Peace: 1963 stamp of Algeria"
]

[
    "Q866 - YouTube: American video-sharing platform owned by Alphabet Inc.",
    "Q112247183 - YouTube LLC: American audiovisual communications company",
    "Q28404534 - YouTube Music: music streaming website",
    "Q17125263 - YouTuber: person that produces videos on YouTube",
    "Q18157148 - YouTube: official channel of YouTube"
]

Output: None of the candidates

Output: None of the candidates


Advertising

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Heineken_N, Advertising: Heineken's main advertising slogan in the UK was "Refreshes the parts other beers cannot reach",some of which featured voice-over narration by Danish comedian/pianistVictor Borge. The British TV campaign ran for over 30 years – stopping in 2005.From March 2011 they have been advertising using the song 'The Golden Age' byThe Asteroids Galaxy Tour. After the success of The Entrance, a web advert (4M views in YouTube), Heineken launched The Date in May 2011. In March 2017 in Amsterdam, Heineken opened a pop-up bakery for five days to promote the yeast used in its brewing. The bread was made by Mark Plaating and proceeds were donated to a local baking guild.

Heineken

Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International

Q64549253 - Heineken: painting by Idelle Weber

Q114519751 - The Date: episode of The Amazing World of Gumball (S1 E27)

Q114509436 - The Date: episode of Love (S1 E5)

Q114506403 - The Date: episode of Blossom (S5 E17)

Q114465766 - The Date: episode of The Doris Day Show (S1 E27)

Q114527910 - The Date: episode of Archie Bunker's Place (S3 E3)

Heineken

Q5356025 - The Entrance: state electoral district of New South Wales, Australia

Q7732312 - The Entrance: town in New South Wales, Australia

Q7732313 - The Entrance: 2006 film by Damon Vignale

Q114815300 - The Entrance: embroidered panel by Phoebe Anna Traquair

Q119344150 - The Entrance: painting by Louis James (1920–1996), Laing Art Gallery

Heineken

Heineken

P625 - coordinate location: geocoordinates of the subject. For Earth, please note that only the WGS84 geodetic datum is currently supported

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

P937 - work location: location where persons or organisations were actively participating in employment, business or other work

P3005 - valid in place: place where a statement is valid

P131 - located in the administrative territorial entity: the item is located on the territory of the following administrative entity. Use P276 for specifying locations that are non-administrative places and for items about events. Use P1382 if the item falls only partially into the administrative entity.

Heineken

Heineken

Heineken


Sponsorships

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: Heineken_N, Sponsorships: Heineken sponsors several sporting events. TheHeineken Cupwas an annualrugby unionknock-outcompetition involving leading club, regional and provincial teams from the Six Nations:England,France,Scotland,Wales,Ireland, andItaly. Heineken was the title sponsor from the cup'sinauguraltournament in1995–96, until the tournament ceased in 2014 and was replaced by theChampions Cup. Heineken continued its sponsorship of European Club Rugby as the principle partner of theEuropean Rugby Champions Cupreturning to title sponsorship of the Champions Cup from2018–19. They have been credited as theFounding Partner of European Rugby. Heineken has been an integral partner of theUEFA Champions Leaguesince 2005, with a theme of "Enjoyed together around the world."TheHeineken Open (tennis)is atennistournamenton theATPInternational Seriesplayed inAuckland, New Zealand. Heineken also sponsors the music events: theHeineken Open'er Festival, a contemporary music festival held inPoland; and, since 2004, theOxegenmusic festival in Ireland. Heineken sponsors the Ballyheigue Summerfest inCounty Kerry, Ireland. In 2016, Heineken became the Official Beer of theFormula OneWorld Championship after theCanadian Grand Prix.During theknockout stageof the2019–20season, Heineken 0,0% became the official beer of theUEFA Europa Leagueas the season resumed followed with the start of the2020–21season.

[
    "Q180855 - Heineken: Dutch beer company"
]

[
    "Q726917 - Auckland Region: region of New Zealand's North Island",
    "Q37100 - Auckland: city on the North Island, New Zealand",
    "Q492467 - University of Auckland: university in Auckland, New Zealand",
    "Q21396346 - Auckland: male given name",
    "Q758659 - Auckland University of Technology: university at Auckland"
]

[
    "Q3502395 - Open'er Festival: contemporary music festival held in Gdynia, Poland"
]

[
    "Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition",
    "Q104223305 - final of the European Rugby Champions Cup: None",
    "Q121637540 - season of the European Rugby Champions Cup: season of the European Rugby Champions Cup (Heineken Cup 1996-2014, ER Champions Cup 2014-)",
    "Q877423 - Heineken Cup: rugby competition",
    "Q63279183 - 2019–20 European Rugby Champions Cup: Rugby competition"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q2717948 - Oxegen: music festival at Punchestown Racecourse, Ireland",
    "Q7115163 - Oxegen 2008: None",
    "Q7115161 - Oxegen 2006: None",
    "Q7115159 - Oxegen 2005: music festival in Ireland",
    "Q7115162 - Oxegen 2007: Irish musical festival held in July 2007"
]

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

[
    "Q108861375 - Formula One season: annual series of auto racing events",
    "Q3077549 - Formula One World Championship: Beyond the Limit: 1994 Formula 1 video game",
    "Q755013 - list of Formula One World Championship points scoring systems: Wikipedia list article"
]

[
    "Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition",
    "Q877423 - Heineken Cup: rugby competition",
    "Q29720029 - h-cup: protein-coding gene in the species Drosophila melanogaster",
    "Q29814298 - Heineken-cup Dmel_CG6130: protein found in Drosophila melanogaster",
    "Q104223305 - final of the European Rugby Champions Cup: None"
]

[
    "Q1999 - 2014: year",
    "Q5371769 - 2014: natural number",
    "Q9678 - 2014 Winter Olympics: 22nd edition of Winter Olympics, in Sochi, Russia",
    "Q108816797 - 2014 Indonesian People's Representative Council election: None",
    "Q16735873 - 2014 Macedonian general election: None"
]

[
    "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"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition",
    "Q877423 - Heineken Cup: rugby competition",
    "Q29720029 - h-cup: protein-coding gene in the species Drosophila melanogaster",
    "Q29814298 - Heineken-cup Dmel_CG6130: protein found in Drosophila melanogaster",
    "Q104223305 - final of the European Rugby Champions Cup: None"
]

[
    "Q21 - England: country in north-west Europe, part of the United Kingdom",
    "Q179876 - Kingdom of England: historic kingdom on the British Isles (927–1649; 1660–1707)",
    "Q11111401 - England: family name",
    "Q47762 - England men's national association football team: men's national association football team representing England",
    "Q9448 - Premier League: English men's association football top league"
]

[
    "Q22 - Scotland: country in north-west Europe, part of the United Kingdom",
    "Q230791 - Kingdom of Scotland: historic sovereign kingdom on the British Isles from the 9th century and up to 1707",
    "Q107636801 - Scotland: given name",
    "Q22669063 - Robert W. Scotland: British botanist",
    "Q34044 - Scotland men's national football team: men's association football team representing Scotland"
]

[
    "Q38 - Italy: country in Southern Europe",
    "Q172579 - Kingdom of Italy: kingdom in Southern Europe from 1861 to 1946",
    "Q223936 - Kingdom of Italy: kingdom in southern Europe between 1805 and 1814",
    "Q145694 - Italian Peninsula: peninsula of southern Europe",
    "Q838931 - Kingdom of Italy: (962 – 1801) constituent kingdom of the Holy Roman Empire"
]

[
    "Q25 - Wales: country in north-west Europe, part of the United Kingdom",
    "Q16944475 - Wales: family name",
    "Q20136480 - Wales: given name",
    "Q9309 - Welsh: Brittonic language spoken natively in Wales",
    "Q7961747 - Wales: constituency of the European Parliament"
]

[
    "Q27 - Ireland: country in Northwestern Europe",
    "Q22890 - Ireland: island in the North Atlantic Ocean",
    "Q215530 - Kingdom of Ireland: kingdom on the island of Ireland between 1542 and 1801",
    "Q57695350 - Ireland: part of the United Kingdom (1801–1922)",
    "Q28199768 - Ireland: family name"
]

[
    "Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition",
    "Q22097654 - Champions Cup: men's and women's Grand Slam of Curling event",
    "Q1061252 - Champions Cup: snooker tournament",
    "Q30602649 - Champions Cup: first edition of an annual international football tournament in India",
    "Q65069221 - Champions Cup: Association football competition"
]

[
    "Q1996 - 2009: year",
    "Q51134 - 2009: natural number",
    "Q109682732 - 2009 Indonesian People's Representative Council election: 2009 election in Indonesia",
    "Q53385935 - 2009-2010 one-year-period: period of about one year starting in 2009 and ending in 2010",
    "Q212197 - 2009–10 UCI Europe Tour: None"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition",
    "Q104223305 - final of the European Rugby Champions Cup: None",
    "Q121637540 - season of the European Rugby Champions Cup: season of the European Rugby Champions Cup (Heineken Cup 1996-2014, ER Champions Cup 2014-)",
    "Q877423 - Heineken Cup: rugby competition",
    "Q63279183 - 2019–20 European Rugby Champions Cup: Rugby competition"
]

[
    "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"
]

[
    "Q18760 - UEFA Europa League: annual association football competition in Europe",
    "Q18740965 - 2016–17 UEFA Europa League: 46th season of Europe's secondary club football tournament organised by UEFA",
    "Q30126418 - 2018–19 UEFA Europa League: 48th season of Europe's secondary club football tournament organised by UEFA",
    "Q22976888 - 2017–18 UEFA Europa League: 47th season of Europe's secondary club football tournament organised by UEFA",
    "Q534168 - 2013–14 UEFA Europa League: 43rd season of Europe's secondary club football tournament organised by UEFA"
]

[
    "Q81068910 - COVID-19 pandemic: pandemic caused by SARS-CoV-2",
    "Q53386137 - 2019-2020 one-year-period: period of about one year starting in 2019 and ending in 2020",
    "Q84055514 - COVID-19 pandemic in India: ongoing COVID-19 viral pandemic in India",
    "Q83741704 - COVID-19 pandemic by country and territory: details regarding the COVID-19 pandemic by country and territory",
    "Q64509602 - 2019–2020 Hong Kong protests: pro-democracy demonstrations and other civil disobedience sparked by a proposed extradition bill in Hong Kong"
]

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


Sponsorships

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: Heineken_N, Sponsorships: Heineken sponsors several sporting events. TheHeineken Cupwas an annualrugby unionknock-outcompetition involving leading club, regional and provincial teams from the Six Nations:England,France,Scotland,Wales,Ireland, andItaly. Heineken was the title sponsor from the cup'sinauguraltournament in1995–96, until the tournament ceased in 2014 and was replaced by theChampions Cup. Heineken continued its sponsorship of European Club Rugby as the principle partner of theEuropean Rugby Champions Cupreturning to title sponsorship of the Champions Cup from2018–19. They have been credited as theFounding Partner of European Rugby. Heineken has been an integral partner of theUEFA Champions Leaguesince 2005, with a theme of "Enjoyed together around the world."TheHeineken Open (tennis)is atennistournamenton theATPInternational Seriesplayed inAuckland, New Zealand. Heineken also sponsors the music events: theHeineken Open'er Festival, a contemporary music festival held inPoland; and, since 2004, theOxegenmusic festival in Ireland. Heineken sponsors the Ballyheigue Summerfest inCounty Kerry, Ireland. In 2016, Heineken became the Official Beer of theFormula OneWorld Championship after theCanadian Grand Prix.During theknockout stageof the2019–20season, Heineken 0,0% became the official beer of theUEFA Europa Leagueas the season resumed followed with the start of the2020–21season.

Heineken

Heineken

Q2717948 - Oxegen: music festival at Punchestown Racecourse, Ireland

Q7115159 - Oxegen 2005: music festival in Ireland

Q7115162 - Oxegen 2007: Irish musical festival held in July 2007

Heineken

Q15120374 - European Rugby Champions Cup: European top-tier rugby union club competition

Q104223305 - final of the European Rugby Champions Cup: None

Q121637540 - season of the European Rugby Champions Cup: season of the European Rugby Champions Cup (Heineken Cup 1996-2014, ER Champions Cup 2014-)

Q877423 - Heineken Cup: rugby competition

Q63279183 - 2019–20 European Rugby Champions Cup: Rugby competition

Heineken

Q3502395 - Open'er Festival: contemporary music festival held in Gdynia, Poland

Heineken

Q1968 - Formula One: motorsport championship held worldwide

Q8564669 - Formula One: 1985 racing video game published by CRL

Q1437614 - F1: video game series by Codemasters

Q108886221 - Formula One event entry: entry by a participant in a specific Formula One event

Q2418753 - Formula 1: racing video game by Bizarre

Heineken

Heineken

Heineken

Q29720029 - h-cup: protein-coding gene in the species Drosophila melanogaster

Q29814298 - Heineken-cup Dmel_CG6130: protein found in Drosophila melanogaster

Heineken

Heineken

Output: None of the candidates


Holland Heineken House

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: Heineken_N, Holland Heineken House: Since 1992 Heineken organises, together withNOC*NSF, the Dutch meeting place at all theOlympic Games, called theHolland Heineken House.

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q1966490 - Holland Heineken House: type of place where people meet during the summer and winter olympics"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q1650156 - NOC*NSF: sporting organisation that functions as the combined National Olympic Committee and National Paralympic Committee for The Netherlands",
    "Q113569280 - NOC*NSF: website of the Dutch sports umbrella organisation NOC*NSF",
    "Q99349561 - Collection Sports Heritage NOC*NSF: collection olympic sports heritage"
]

[
    "Q5389 - Olympic Games: major international multi-sport event organised by International Olympic Committee",
    "Q188468 - ancient Olympic Games: athletic competitions in ancient Greece",
    "Q9672 - 2006 Winter Olympics: 20th edition of Winter Olympics, in Turin, Italy",
    "Q8143 - 1932 Summer Olympics: Games of the X Olympiad, in Los Angeles, USA",
    "Q995653 - 2024 Summer Olympics: games of the XXXIII Olympiad, in Paris, France"
]

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


Holland Heineken House

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: Heineken_N, Holland Heineken House: Since 1992 Heineken organises, together withNOC*NSF, the Dutch meeting place at all theOlympic Games, called theHolland Heineken House.

Heineken

Q1966490 - Holland Heineken House: type of place where people meet during the summer and winter olympics

Holland_Heineken_House

Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International

Q64549253 - Heineken: painting by Idelle Weber

Heineken

Q1650156 - NOC*NSF: sporting organisation that functions as the combined National Olympic Committee and National Paralympic Committee for The Netherlands

Q113569280 - NOC*NSF: website of the Dutch sports umbrella organisation NOC*NSF

Q99349561 - Collection Sports Heritage NOC*NSF: collection olympic sports heritage

Heineken

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

Q188468 - ancient Olympic Games: athletic competitions in ancient Greece

Q9672 - 2006 Winter Olympics: 20th edition of Winter Olympics, in Turin, Italy

Q8143 - 1932 Summer Olympics: Games of the X Olympiad, in Los Angeles, USA

Q995653 - 2024 Summer Olympics: games of the XXXIII Olympiad, in Paris, France


Heineken Experience

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: Heineken_N, Heineken Experience: TheHeineken Experienceis a museum aboutHeineken Pilsenerand the Heineken brewery, based in the original brewery inAmsterdam. The original building was built in 1867, and was in use as a brewery until 1988.In 1991, when part of the establishment was torn down, the Heineken Reception and Information Centre (Dutch:Heineken ontvangst- en informatiecentrum) was opened in the remaining building. In 2001 the name was changed to Heineken Experience. The museum features "rides", interactive exhibits, and two bars. It also gives an insight into the company's history and brewing processes through the years. Visitors receive one small tasting glass and two full-sized glasses of Heineken beer to drink at the end of the tour, both paid for by the 21 euro entry fee.

[
    "Q2087161 - Heineken Experience: industrial museum in Amsterdam, Netherlands"
]

[
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q180855 - Heineken: Dutch beer company"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q19241498 - 1867: natural number",
    "Q7715 - 1867: year",
    "Q2617765 - 18676 Zdeňkaplavcová: asteroid",
    "Q7890225 - 1867 United States House of Representatives elections in California: 1867 House elections in California",
    "Q105777758 - 30 & 31 Vic: set of Public Acts of Parliament of the United Kingdom passed in 1867"
]

[
    "Q1988 - 2001: year",
    "Q841792 - 2001: natural number",
    "Q182280 - 2001: 1999 studio album by Dr. Dre",
    "Q103474 - 2001: A Space Odyssey: 1968 film by Stanley Kubrick",
    "Q10806 - September 11 attacks: 2001 attacks in the United States"
]

[
    "Q9899 - Amsterdam: municipality in the Netherlands, containing the cities of Amsterdam and Weesp",
    "Q727 - Amsterdam: capital and most populous city of the Netherlands",
    "Q37471983 - Amsterdam: family name",
    "Q190804 - Rijksmuseum: museum in Amsterdam, Netherlands",
    "Q2011634 - Yef Miossu: island in Tambrauw Regency, Southwest Papua Province, Indonesia"
]

[
    "Q180855 - Heineken: Dutch beer company",
    "Q16870666 - Heineken: family name",
    "Q854383 - Heineken: pale lager beer by the Dutch brewing company Heineken International",
    "Q64549253 - Heineken: painting by Idelle Weber",
    "Q17425857 - Heinekenia: genus of plants"
]

[
    "Q2426 - 1988: year",
    "Q5371775 - 1988: natural number",
    "Q8470 - 1988 Summer Olympics: Games of the XXIV Olympiad, in Seoul, South Korea",
    "Q9658 - 1988 Winter Olympics: 15th edition of Winter Olympics, in Calgary, Canada",
    "Q53389177 - 1988-1989 one-year-period: period of about one year starting in 1988 and ending in 1989"
]

[
    "Q2087161 - Heineken Experience: industrial museum in Amsterdam, Netherlands"
]