Executive team

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

[["Heineken_N", "has_executive_team", "\"The executive team of the company consists of the following people:\""]]


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:

[["Heineken", "Company"], ["23", "number"], ["Europe", "Place"], ["9", "number"], ["Americas", "Place"], ["18", "number"], ["Asia_Pacific", "Place"], ["Africa_and_the_Middle_East", "Place"]]


Brewing plants

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, 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:

[["Heineken", "has_brewing_plant_in", "\"4 regions of the world\""], ["Heineken", "has_operating_company", "\"23 in Europe\""], ["Heineken", "has_operating_company", "\"9 in the Americas\""], ["Heineken", "has_operating_company", "\"15 in Asia Pacific\""], ["Heineken", "has_operating_company", "\"18 in Africa and the Middle East\""]]


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.

[["Heineken_International", "Company"], ["Heineken", "Product"], ["\"Over 170\"", "number"], ["Pale_lager", "Product"], ["Amstel", "Product"], ["Birra_Moretti", "Product"], ["Desperados_(beer)", "Product"], ["Dos_Equis", "Product"], ["Gösser", "Product"], ["Kingfisher_Brewery", "Product"], ["Lagunitas_Pizza_Brewing_Company", "Product"], ["Red_Strike", "Product"], ["Sagres_(beer)", "Product"], ["Tecate", "Product"], ["South_Pacific_Export", "Product"], ["Affligem", "Product"], ["Amstel", "Company"], ["Heineken", "Company"], ["Bintang", "Product"], ["Edelweiss_(beer)", "Product"], ["Sol_(beer)", "Product"], ["Tiger_Beer", "Product"]]


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", "beer_brand", "Heineken"], ["Heineken_International", "beer_brand", "Amstel"], ["Heineken_International", "beer_brand", "Birra_Moretti"], ["Heineken_International", "beer_brand", "Desperados"], ["Heineken_International", "beer_brand", "Dos_Equis"], ["Heineken_International", "beer_brand", "Gösser"], ["Heineken_International", "beer_brand", "Kingfisher"], ["Heineken_International", "beer_brand", "Lagunitas"], ["Heineken_International", "beer_brand", "Red_Stripe"], ["Heineken_International", "beer_brand", "Tecate"], ["Heineken_International", "beer_brand", "Tiger"], ["Heineken_International", "beer_brand", "South_Pacific_Export"], ["Heineken_International", "beer_style", "Pale_lager"], ["Heineken_International", "number_of_beer_brands", "170"]]


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:

[["Heineken_N.V.", "company"], ["New_York_Stock_Exchange", "market"], ["\"HEIA, HEINY\", \"OTCQX\"", "string"], ["Amsterdam", "market"], ["2023-05-31", "date"], ["Heineken_N.V.", "product"], ["Heineken_N.V._shareholders", "number"]]


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.", "shares", "Heineken_N.V."], ["Heineken_N.V.", "shares", "New_York_Stock_Exchange"], ["Heineken_N.V.", "shares", "OTCQX"], ["Heineken_N.V.", "shares", "Amsterdam"], ["Heineken_N.V.", "shares", "\"HEIA\""], ["Heineken_N.V.", "shares", "\"HEINY\""]]


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.

[["Heineken", "Company"], ["\"The Golden Age\" by The Asteroids Galaxy Tour", "string"], ["Heineken", "Product"], ["\"Refreshes the parts other beers cannot reach\"", "string"], ["Victor_Borge", "Person"], ["The_Date", "Product"], ["Heineken", "Advertising"], ["The_Date", "Company"], ["Amsterdam", "Location"], ["Mark_Plating", "Person"], ["The_Entrance", "Product"], ["Heineken", "Product"], ["Bakery", "Product"], ["Heineken", "Company"], ["United_Kingdom", "Place"], ["2011-03-01", "Date"], ["2005-03-01", "Date"], ["Bakery", "Company"], ["Heineken", "Product"], ["5.0", "number"], ["Bakery", "Product"], ["Heineken", "Company"], ["United_Kingdom", "Place"], ["The_Entrance", "Product"], ["Heineken", "Company"], ["Bakery", "Company"], ["Heineken", "Product"], ["The_Date", "Product"], ["Mark_Plating", "Person"], ["Amsterdam", "Location"], ["Bakery", "Product"], ["Heineken", "Company"], ["United_Kingdom", "Place"], ["4.0 (million views)", "number"], ["Heineken", "Product"], ["The_Entrance", "Product"], ["YouTube", "Company"]]


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", "launches", "The_Date"], ["Heineken", "launches", "The_Entrance"], ["Heineken", "launches", "\"The Golden Age\" by The Asteroids Galaxy Tour"], ["Heineken", "launches", "\"Refreshes the parts other beers cannot reach\""], ["Heineken", "launches", "A_Pop-Up_Bakery"], ["Heineken", "location", "Amsterdam"], ["Heineken", "has_advertising_slogan", "\"Refreshes the parts other beers cannot reach\""], ["Heineken", "has_advertising_slogan", "\"The Golden Age\" by The Asteroids Galaxy Tour"], ["Heineken", "has_advertising_slogan", "The_Date"], ["Heineken", "has_advertising_slogan", "The_Entrance"], ["Heineken", "has_advertising_slogan", "A_Pop-Up_Bakery"], ["Heineken", "has_advertising_slogan", "\"Victor Borge\""]]


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.

[["Heineken_N", "Company"], ["Heineken_Open_(tennis)", "Product"], ["Auckland", "Location"], ["Heineken_Open'er_Festival", "Product"], ["European_Rugby_Champions_Cup", "Product"], ["Heineken", "Company"], ["Oxegen", "Product"], ["2005", "Year"], ["Heineken_0.0%", "Product"], ["Formula_One_World_Championship", "Product"], ["Ballyheigue_Summerfest", "Product"], ["Heineken_Cup", "Product"], ["2014", "Year"], ["European_Club_Rugby", "Company"], ["Canada", "Country"], ["Heineken", "Sponsor"], ["Heineken_Cup", "Company"], ["England", "City"], ["Scotland", "City"], ["Italy", "City"], ["Wales", "City"], ["Ireland", "City"], ["Champions_Cup", "Product"], ["2009", "Year"], ["Heineken", "Person"], ["European_Rugby_Champions_Cup", "Company"], ["2016", "Year"], ["Heineken", "Sponsor"], ["Heineken_0.0%", "Company"], ["UEFA_Europa_League", "Company"], ["2019–20", "Year"]]


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", "sponsoring", "Heineken_Open_(tennis)"], ["Heineken", "sponsoring", "Oxegen"], ["Heineken", "sponsoring", "European_Rugby_Champions_Cup"], ["Heineken", "sponsoring", "Heineken_Open'er_Festival"], ["Heineken", "sponsoring", "Formula_One"], ["Heineken", "sponsoring", "Ballyheigue_Summerfest"], ["Heineken", "sponsoring", "Heineken_Europa_League"], ["Heineken", "sponsoring", "Heineken_Cup"], ["Heineken", "has_product_line", "Heineken_0.0"], ["Heineken", "has_product_line", "Heineken_(beer)"]]


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.

[["Heineken", "Company"], ["Holland_Heineken_House", "City"], ["Heineken", "Person"], ["NOC*NSF", "Person"], ["Olympic_Games", "Place"], ["1992", "Year"]]


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", "has_product_line", "Holland_Heineken_House"], ["Holland_Heineken_House", "has_parentCompany", "Heineken"], ["Heineken", "has_product_line", "NOC*NSF"], ["Heineken", "has_product_line", "Olympic_Games"]]


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.

[["Heineken_Experience", "company"], ["Heineken_Pilsener", "product"], ["Heineken", "person"], ["1867", "date"], ["\"Heineken Reception and Information Centre\"", "string"], ["\"Heineken Experience\"", "string"], ["2001", "date"], ["\"21.00 euro\"", "number"], ["Amsterdam", "location"], ["Heineken", "company"], ["1988", "date"], ["Heineken_Experience", "product"], ["\"two full-sized glasses and one small tasting glass\"", "number"], ["\"interactive exhibits\"", "string"], ["\"two bars\"", "number"], ["\"rides\"", "string"]]


Heineken Experience

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, 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.

[["Heineken_Experience", "is_a_type_of", "Museum"], ["Heineken_Experience", "located_in", "Amsterdam"], ["Heineken_Experience", "has_product_line", "Heineken_Pilsener"], ["Heineken_Experience", "launch_date", "\"2001\""], ["Heineken_Experience", "has_product_line", "Heineken_Beer"], ["Heineken_Experience", "entry_fee", "21.0 (euros)"], ["Heineken_Experience", "has_product_line", "Tasting_glass"], ["Heineken_Experience", "revenue", "Heineken"], ["Heineken_Experience", "has_product_line", "Two_full-sized_glasses_of_Heineken_beer"], ["Heineken_Experience", "established", "1991"], ["Heineken_Experience", "has_product_line", "Interactive_exhibit"], ["Heineken_Experience", "has_product_line", "Ride"], ["Heineken_Experience", "has_product_line", "Information_center"], ["Heineken_Experience", "has_product_line", "Beer"], ["Heineken_Experience", "has_product_line", "Food"], ["Heineken_Experience", "has_product_line", "Film"], ["Heineken_Experience", "has_product_line", "Fictional_character"], ["Heineken_Experience", "has_product_line", "Place"], ["Heineken_Experience", "has_product_line", "Demonym"], ["Heineken_Experience", "has_product_line", "Country"], ["Heineken_Experience", "has_product_line", "Language"], ["Heineken_Experience", "has_product_line", "Written_work"], ["Heineken_Experience", "has_product_line", "Award"], ["Heineken_Experience", "has_product_line", "Film_series"], ["Heineken_Experience", "has_product_line", "Channel"], ["Heineken_Experience", "has_product_line", "Organisation"], ["Heineken_Experience", "has_product_line", "City"], ["Heineken_Experience", "has_product_line", "Occupation"], ["Heineken_Experience", "has_product_line", "Ethnic_group"], ["Heineken_Experience", "has_product_line", "State"], ["Heineken_Experience", "has_product_line", "Country'], ["Heineken_Experience", "has_product_line", "Language"], ["Heineken_Experience", "has_product_line", "Food"], ["Heineken_Experience", "has_product_line", "Film"], ["Heineken_Experience", "has_product_line", "City"], ["Heineken_Experience", "has_product_line", "Background"], ["Heineken_Experience", "has_product_line", "Hospitality"], ["Heineken_Experience", "has_product_line", "Alcohol"], ["Heineken_Experience", "has_product_line", "Awards"], ["Heineken_Experience", "has_product_line", "Asthma"], ["Heineken_Experience", "has_product_line", "Asthma_trigger"], ["Heineken_Experience", "has_product_line", "Beer"], ["Heineken_Experience", "has_product_line", "Museum"], ["Heineken_Experience", "has_product_line", "Food"], ["Heineken_Experience", "has_product_line", "City"], ["Heineken_Experience", "has_product_line", "Country"], ["Heineken_Experience", "has_product_line", "Language"], ["Heineken_Experience", "has_product_line", "Written_work"], ["Heineken_Experience", "has_product_line", "Film"], ["Heineken_Experience", "has_product_line", "Award"], ["Heineken_Experience", "has_product_line", "Person"], ["Heineken_Experience", "has_product_line", "Place"], ["Heineken_Experience", "has_product_line", "Ethnic_group"], ["Heineken_Experience", "has_product_line", "State"], ["Heineken_Experience", "has_product_line", "Occupation"], ["Heineken_Experience", "has_product_line", "Organisation"], ["Heineken_Experience", "has_product_line", "Hospitality"], ["Heineken_Experience", "has_product_line", "Alcohol"], ["Heineken_Experience", "has_product_line", "Asthma"], ["Heineken_Experience", "has_product_line", "Asthma_trigger"], ["Heineken_Experience", "established", "1863"], ["Heineken_Experience", "has_product_line", "Tasting_glass"], ["Heineken_Experience", "has_product_line", "Two_full-sized_glasses_of_Heineken_beer"], ["Heineken_Experience", "revenue", "Heineken"], ["Heineken_Experience", "has_product_line", "Beer"], ["Heineken_Experience", "has_product_line", "Food"], ["Heineken_Experience", "has_product_line", "Film"], ["Heineken_Experience", "has_product_line", "City"], ["Heineken_Experience", "has_product_line", "Country"], ["Heineken_Experience", "has_product_line", "Language"], ["Heineken_Experience", "has_product_line", "Written_work"], ["Heineken_Experience", "has_product_line", "Award"], ["Heineken_Experience", "has_product_line", "Person"], ["Heineken_Experience", "has_product_line", "Place"], ["Heineken_Experience", "has_product_line", "Ethnic_group"], {"domain": "Heineken_Experience", "range": "Food"}], "hasToItsSoutheast": {"domain": "Heineken_Experience", "range": "Place"}}, {"hasToItsWest": {"domain": "Heineken_Experience", "range": "Place"}}, {"hasToItsNorth": {"domain": "Heineken_Experience", "range": "Place"}}, {"hasToItsSouthwest": {"domain": "Heineken_Experience", "range": "Place"}}, {"numberOfPages": {"domain": "WrittenWork', "range": "number"}}, {"genre': {"domain": "WrittenWork', "range": "Genre'}}, {"capital': {"domain": "Country', "range': "City'}}, {"parentCompany': {"domain': "Company', 'range': 'Company'}}, {"residence': {"domain': "Person', 'range': 'City'}}, {'releaseDate': {"domain': 'WrittenWork', 'range': 'Date'}}, {'headquarter': {'domain': 'Company', 'range': 'City'}}, {'birthPlace': {'domain': 'Person', 'range': 'Place'}}, {'impactFactor': {'domain': "WrittenWork', 'range': 'number'}}, {'issnNumber': {'domain': 'WrittenWork', 'range': 'number'}}, {'isbnNumber': {'domain': 'WrittenWork', 'range': 'number'}}, {"nationality': {'domain': 'Person', 'range': 'Country'}}, {'abbreviation': {'domain': 'WrittenWork', 'range': 'string'}}, {'regionServed': {'domain': 'WrittenWork', 'range': 'Place'}}, {'affiliation': {'domain': 'Person', 'range': 'Organisation'}}, {'oclcNumber': {'domain': 'WrittenWork', 'range': 'number'}}, {'literaryGenre': {'domain': 'WrittenWork', 'range': 'Genre'}}, {'libraryofCongressClassification': {'domain': 'WrittenWork', 'range': 'string'}}, {'leaderTitle': {'domain': 'Country', 'range': 'string'}}, {'academicDiscipline': {'domain': 'WrittenWork', 'range': 'AcademicDiscipline'}}, {'ethnicGroup': {'domain': 'Country', 'range': 'EthnicGroup'}}, {'firstPublicationYear': {'domain': 'WrittenWork', 'range': 'Year'}}, {'author': {'domain': 'WrittenWork', 'range': 'Person'}}, {'leader': {'domain': 'Country', 'range': 'Person'}}, {'city': {'domain': 'Company', 'range': 'City'}}, {'notableWork': {'domain': 'Person', 'range': 'WrittenWork'}}, {'followedBy': {'domain': 'WrittenWork', 'range': 'WrittenWork'}}, {'president': {'domain': 'Country', 'range': 'Person'}}, {'precededBy': {'domain': 'WrittenWork', 'range': 'WrittenWork'}}, {'doctoralAdvisor': {'domain': 'Person', 'range': 'Person'}}, {'frequency': {'domain': 'WrittenWork', 'range': 'number'}}, {'spokenIn': {'domain': 'Language', 'range': 'Country'}}, {'almaMater': {'domain': 'Person', 'range': 'School'}}, {'country': {'domain': 'WrittenWork', 'range': 'Country'}}, {'publisher': {'domain': 'WrittenWork', 'range': 'Company'}}, {'editor': {'domain': 'WrittenWork', 'range': 'Person'}}, {'LCCN_number': {'domain': 'WrittenWork', 'range': 'number'}}, {'language': {'domain': 'WrittenWork', 'range': 'Language'}}, {'codenCode': {'domain': 'WrittenWork', 'range': 'string'}}, {'state': {'domain': 'City', 'range': 'State'}}, {'birthDate': {'domain': 'Person', 'range': 'Date'}}, {'founder': {'domain': 'Company', 'range': 'Person'}}, {'influencedBy': {'domain': 'Person', 'range': 'Person'}}]


Possible ties to the slave plantations

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, Possible ties to the slave plantations: On 15 February 1864, Gerard Adriaan Heineken bought De Hooiberg (the Haystack) brewery in Amsterdam.It remains unclear whether the funds for the purchase of the Haystack came from his father, a cheese trader, or his mother, whose estate included proceeds from her previous husband’s family’s historical investments in West Indies slave plantations. In a letter to his mother 18 June 1863, Gerard discussed the potential Haystack purchase and his plans for the future. Gerard’s mother, Anna Geertruida van de Paauw, came to own shares in slave plantations inBerbice(modern day Guyana) and Suriname through her first marriage in 1829 to Pieter Jacob Schumacher van Oudorp (1804–1833)who died in 1833. The Schumacher family owned several plantations in Berbice and Suriname, according to records held at the UK’s National Archive.After Pieter Schumacher died, Anna was remarried to Cornelis Heineken and had four children, one of which was Gerard Heineken. Anna died in 1881.

[["Heineken_N._B.V.", "Company"], ["Gerard_Adriaan_Heineken", "Person"], ["De_Hooiberg", "Company"], ["1864-02-15", "Date"], ["Anna_Geertruida_van_de_Paauw", "Person"], ["Pieter_Jacob_Schumacher_van_Oudorp", "Person"], ["Berbice", "Place"], ["Suriname", "Place"], ["Cornelis_Heineken", "Person"], ["Gustave_Heineken", "Person"], ["Guyana", "Place"], ["Heineken_N._B.V.", "Product"], ["De_Hooiberg", "Place"], ["1863-06-18", "Date"], ["Anna_Geertruida_van_de_Paauw", "Company"], ["Cheese_trader", "Industry"], ["Heineken_N._B.V.", "Place"], ["Suriname", "Company"], ["Berbice", "Company"], ["Gustave_Heineken", "Founder"], ["Guyana", "Company"]]


Possible ties to the slave plantations

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, Possible ties to the slave plantations: On 15 February 1864, Gerard Adriaan Heineken bought De Hooiberg (the Haystack) brewery in Amsterdam.It remains unclear whether the funds for the purchase of the Haystack came from his father, a cheese trader, or his mother, whose estate included proceeds from her previous husband’s family’s historical investments in West Indies slave plantations. In a letter to his mother 18 June 1863, Gerard discussed the potential Haystack purchase and his plans for the future. Gerard’s mother, Anna Geertruida van de Paauw, came to own shares in slave plantations inBerbice(modern day Guyana) and Suriname through her first marriage in 1829 to Pieter Jacob Schumacher van Oudorp (1804–1833)who died in 1833. The Schumacher family owned several plantations in Berbice and Suriname, according to records held at the UK’s National Archive.After Pieter Schumacher died, Anna was remarried to Cornelis Heineken and had four children, one of which was Gerard Heineken. Anna died in 1881.

[["Gerard_Adriaan_Heineken", "acquired", "De_Hooiberg"], ["Gerard_Adriaan_Heineken", "possible_source_of_funds", "\"His father, a cheese trader, or his mother, whose estate included proceeds from her previous husband's family's historical investments in West Indies slave plantations.\""], ["Gerard_Adriaan_Heineken", "date_of_acquisition", "\"1864-02-15\""], ["Gerard_Adriaan_Heineken", "correspondence", "\"Letter to his mother, 18 June 1863, discussing the potential purchase of the Haystack and his plans for the future.\""], ["Anna_Geertruida_van_de_Paauw", "deathDate", "1881-01-01"], ["Anna_Geertruida_van_de_Paauw", "spouse", "Cornelis_Heineken"], ["Anna_Geertruida_van_de_Paauw", "child", "Gerard_Adriaan_Heineken"], ["Anna_Geertruida_van_de_Paauw", "spouse", "Pieter_Jacob_Schumacher_van_Oudorp"], ["Pieter_Jacob_Schumacher_van_Oudorp", "deathDate", "1833-01-01"], ["Pieter_Jacob_Schumacher_van_Oudorp", "spouse", "Anna_Geertruida_van_de_Paauw"], ["Pieter_Jacob_Schumacher_van_Oudorp", "shares", "Plantations_in_Berbice"], ["Pieter_Jacob_Schumacher_van_Oudorp", "shares", "Plantations_in_Suriname"], ["Pieter_Jacob_Schumacher_van_Oudorp", "shares", "West_Indies"], ["Gerard_Adriaan_Heineken", "possible_source_of_funds", "\"Anna Geertruida van de Paauw, whose estate included proceeds from her previous husband's family's historical investments in West Indies slave plantations.\""]]


Price fixing

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, Price fixing: On 18 April 2007 theEuropean Commissionfined Heineken  €219.3m,Grolsch€31.65m andBavaria€22.85m for operating aprice fixing cartelin the Netherlands, totalling €273.7m.InBev, (formerlyInterbrew), escaped without a penalty because it provided "decisive information" about the cartel which operated between 1996 and 1999 and others in the EU market. The brewers controlled 95% of the Dutch market, with Heineken claiming a half and the three others 15% each. Neelie Kroessaid she was "very disappointed" that the collusion took place at the very highest (boardroom) level. She added, Heineken, Grolsch, Bavaria and InBev tried to cover their tracks by using code names and abbreviations for secret meetings to carve up the market for beer sold to supermarkets,hotels,restaurantsandcafes. Theprice fixingextended to cheaperown-brandlabels andrebatesfor bars. In 2004 Heineken andKronenbourg(then part ofScottish and Newcastle), the two dominant brewers in France, were fined €2.5m – with the penalty reduced for co-operating. This is simply unacceptable: that major beer suppliers colluded to up prices and to carve up markets among themselves — Neelie Kroes, EU Competition Commissioner

[["Heineken", "Company"], ["Grolsch", "Company"], ["Bavaria", "Company"], ["2007-04-18", "Date"], ["European_Commission", "Organization"], ["InBev", "Company"], ["\"Heineken, Grolsch, Bavaria and InBev tried to cover their tracks by using code names and abbreviations for secret meetings to carve up the market for beer sold to supermarkets, hotels, restaurants and cafes\"", "string"], ["Neelie_Kroes", "Person"], ["\"InBev (formerly known as Interbrew) escaped without a penalty because it provided decisive information\"", "string"], ["Heineken", "Product"], ["Grolsch", "Product"], ["Bavaria", "Product"], ["95.0", "number"], ["2004", "Date"], ["Kronenbourg", "Company"], ["Scottish_and_Newcastle", "Company"], ["France", "Place"], ["€219300000", "number"], ["€31650000", "number"], ["€22850000", "number"], ["€273700000", "number"], ["\"Neelie Kroes said she was 'very disappointed' that the collusion took place at the very highest (boardroom) level\"", "string"], ["Heineken", "Company"], ["Kronenbourg", "Company"], ["€2500000", "number"], ["French_language", "Language"]]


Price fixing

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, Price fixing: On 18 April 2007 theEuropean Commissionfined Heineken  €219.3m,Grolsch€31.65m andBavaria€22.85m for operating aprice fixing cartelin the Netherlands, totalling €273.7m.InBev, (formerlyInterbrew), escaped without a penalty because it provided "decisive information" about the cartel which operated between 1996 and 1999 and others in the EU market. The brewers controlled 95% of the Dutch market, with Heineken claiming a half and the three others 15% each. Neelie Kroessaid she was "very disappointed" that the collusion took place at the very highest (boardroom) level. She added, Heineken, Grolsch, Bavaria and InBev tried to cover their tracks by using code names and abbreviations for secret meetings to carve up the market for beer sold to supermarkets,hotels,restaurantsandcafes. Theprice fixingextended to cheaperown-brandlabels andrebatesfor bars. In 2004 Heineken andKronenbourg(then part ofScottish and Newcastle), the two dominant brewers in France, were fined €2.5m – with the penalty reduced for co-operating. This is simply unacceptable: that major beer suppliers colluded to up prices and to carve up markets among themselves — Neelie Kroes, EU Competition Commissioner

[["Heineken", "price_fixing", "2007-04-18"], ["Heineken", "price_fixing", "Grolsch"], ["Heineken", "price_fixing", "Bavaria"], ["Heineken", "price_fixing", "InBev"], ["Heineken", "price_fixing", "\"InBev, formerly known as Interbrew, escaped without a penalty because it provided decisive information about the cartel.\""], ["Heineken", "price_fixing", "\"The brewers controlled 95% of the Dutch market, with Heineken claiming a half and the three others 15% each.\""], ["Heineken", "price_fixing", "\"Neelie Kroes said she was 'very disappointed' that the collusion took place at the very highest (boardroom) level.\""], ["Heineken", "price_fixing", "\"The price fixing extended to cheaper own-brand labels and rebates for bars.\""], ["Heineken", "price_fixing", "\"The two dominant brewers in France, Heineken and Kronenbourg (then part of Scottish and Newcastle), were fined €2.500.000 in 2004. The penalty was reduced for co-operating.\""], ["Heineken", "price_fixing", "\"This is simply unacceptable: that major beer suppliers colluded to up prices and to carve up markets among themselves.\""]]