Metro AG

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: Metro_AG, Metro AG: Metro AGis a Germanmultinationalcompany based inDüsseldorfwhich operates business membership-onlycash and carrystores primarily under the Metro brand.As of March 2024[update], Metro is operating 626 wholesale stores in 21 countries. The company was established in 1964 by Ernst Schmidt andWilhelm Schmidt-Ruthenbeck. In 2010, it was the fourth-largest retailer in the world measured by revenues, afterWalmart,CarrefourandTesco.Until 2020, it was also active in the general retail business through theRealdivision, which was sold to an investor consortium. Its current incarnation was launched in 2017 as a spun-off of old Metro AG, which continued to be a consumer electronics retailer and renamed itself Ceconomy.

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Metro AG

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: Metro_AG, Metro AG: Metro AGis a Germanmultinationalcompany based inDüsseldorfwhich operates business membership-onlycash and carrystores primarily under the Metro brand.As of March 2024[update], Metro is operating 626 wholesale stores in 21 countries. The company was established in 1964 by Ernst Schmidt andWilhelm Schmidt-Ruthenbeck. In 2010, it was the fourth-largest retailer in the world measured by revenues, afterWalmart,CarrefourandTesco.Until 2020, it was also active in the general retail business through theRealdivision, which was sold to an investor consortium. Its current incarnation was launched in 2017 as a spun-off of old Metro AG, which continued to be a consumer electronics retailer and renamed itself Ceconomy.

[["Metro_AG", "subsidiary", "Real_(general_retailer)"], ["Metro_AG", "region_served", "Worldwide"], ["Metro_AG", "key_person", "Ernst_Schmidt"], ["Metro_AG", "key_person", "Wilhelm_Schmidt-Ruthenbeck"], ["Metro_AG", "founding_date", "1964"], ["Metro_AG", "number_of_locations", "626"], ["Metro_AG", "type", "Company"], ["Metro_AG", "parent", "Ceconomy"], ["Metro_AG", "revenue", "384000000000"], ["Metro_AG", "operating_income", "1080000000"], ["Metro_AG", "location", "Düsseldorf"], ["Metro_AG", "key_person", "Oliver_Broda"], ["Metro_AG", "product", "Business_membership"], ["Metro_AG", "number_of_employees", "83600"], ["Metro_AG", "founding_year", "2004"], ["Metro_AG", "capital", "German_millionaire"], ["Metro_AG", "subsidiary", "Metro_Cash_&_Carry"], ["Metro_AG", "leader_title", "CEO"], ["Metro_AG", "ethnic_group", "German_people"], ["Metro_AG", "city", "Düsseldorf"], ["Metro_AG", "industry", "Retail"], ["Metro_AG", "longName', "Metro AG"], ["Metro_AG", "isPartOf", "German_economy"], ["Metro_AG", "areaTotal', "1000000.0"], ["Metro_AG", "leader", "Oliver_Broda"], ["Metro_AG", "elevationAboveTheSeaLevel', "34.0"], ["Metro_AG", "foundationPlace", "Germany"], ["Metro_AG", "country', "Germany"], ["Metro_AG", "populationTotal', "83600"], ["Metro_AG", "largestCity", "Düsseldorf"], ["Metro_AG", "language', "German_language"}, {"Metro_Cash_&_Carry", "parentCompany", "Metro_AG"}, {"Metro_AG", "operatingIncome", "1080000000"}, {"Metro_AG", "numberOfLocations", "626"], {"Metro_AG", "foundingDate", "2004-01-01"}, {"Metro_AG", "location", "Düsseldorf"}, {"Metro_AG", "numberOfEmployees", "83600"}, {"Metro_AG", "regionServed", "Worldwide"}, {"Metro_AG", "leaderTitle', "CEO"}]


1963 – 1980

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: Metro_AG, 1963 – 1980: Metro began on 8 November 1963 inEssenwith the opening of the first wholesale centre under the name Metro by the brothers Ernst Schmidt and Wilhelm Schmidt-Ruthenbeck. Planning and opening of the first hypermarket under the name Metro in Essen was the responsibility of Walter Vieth, who was managing director there from 1963 to 1970. In 1964, a year after the opening of the first Metro store, the Stöcker & Reinshagen company (the Schell family) planned a cash and carry store inMülheim an der Ruhr. During construction, the businessmens Schmidt-Ruthenbeck, Schmidt and Schell met and decided to merge their cash and carry activities. So they founded Metro-SB-Großmarkt GmbH & Co. KG with headquarters in Mülheim, later in Düsseldorf.Otto Beisheim, until 1964 authorised signatory of the company Stöcker & Reinshagen, became the sole managing director. In 1966, the third Metro was opened inWest Berlin. In 1966, Otto Beisheim met Friedrich Wilhelm Lenz, chairman of the management board ofFranz Haniel & Cie.and convinced him to invest in Metro.In the course of the following reorganization Beisheim became a shareholder himself. From this point on, the founding family Schmidt-Ruthenbeck and the shareholders Beisheim and Haniel each held about one third of the shares. Under the sole leadership of Otto Beisheim, a rapid expansion of the Metro stores into the greater German and European area began in 1967 with the opening of the Metro stores in Godorf nearCologne,Hamburg,Munichand, after a connection with the Dutch companySHV Holdings, the first C&C wholesale store (brand: Makro) in theNetherlands, and in 1968 inDüsseldorf.

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["Munich", "Place"], ["Gustav_Schmidt_(businessperson)", "Person"], ["Mülheim_an_der_Ruhr", "Place"], ["Otto_Beisheim", "Person"], ["Makro", "Company"], ["Munich", "Place"], ["Mülheim_an_der_Ruhr", "Place"], ["Metro_AG", "Company"], ["Makro", "Product"], ["Munich", "Place"], ["Gustav_Schmidt_(businessperson)", "Person"], ["Mülheim_an_der_Ruhr", "Place"], ["Otto_Beisheim", "Person"], ["Makro", "Company"], ["Munich", "Place"], ["Mülheim_an_der_Ruhr", "Place"], ["Metro_AG", "Company"], ["Makro", "Product"], ["Munich", "Place"], ["Gustav_Schmidt_(businessperson)", "Person"], ["Mülheim_an_der_Ruhr", "Place"], ["Otto_Beisheim", "Person"], ["Makro", "Company"], ["Munich", "Place"], ["Mülheim_an_der_Ruhr", "Place"], ["Metro_AG", "Company"], ["Makro", "Product"], ["Munich", "Place"], ["Gustav_Schmidt_(businessperson)", "Person"], ["Mülheim_an_der_Ruhr", "Place"], ["Otto_Beisheim", "Person"], ["Makro", "Company"], ["Munich", "Place"], ["Mülheim_an_der_Ruhr", "Place"], ["Metro_AG", "Company"], 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1963 – 1980

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: Metro_AG, 1963 – 1980: Metro began on 8 November 1963 inEssenwith the opening of the first wholesale centre under the name Metro by the brothers Ernst Schmidt and Wilhelm Schmidt-Ruthenbeck. Planning and opening of the first hypermarket under the name Metro in Essen was the responsibility of Walter Vieth, who was managing director there from 1963 to 1970. In 1964, a year after the opening of the first Metro store, the Stöcker & Reinshagen company (the Schell family) planned a cash and carry store inMülheim an der Ruhr. During construction, the businessmens Schmidt-Ruthenbeck, Schmidt and Schell met and decided to merge their cash and carry activities. So they founded Metro-SB-Großmarkt GmbH & Co. KG with headquarters in Mülheim, later in Düsseldorf.Otto Beisheim, until 1964 authorised signatory of the company Stöcker & Reinshagen, became the sole managing director. In 1966, the third Metro was opened inWest Berlin. In 1966, Otto Beisheim met Friedrich Wilhelm Lenz, chairman of the management board ofFranz Haniel & Cie.and convinced him to invest in Metro.In the course of the following reorganization Beisheim became a shareholder himself. From this point on, the founding family Schmidt-Ruthenbeck and the shareholders Beisheim and Haniel each held about one third of the shares. Under the sole leadership of Otto Beisheim, a rapid expansion of the Metro stores into the greater German and European area began in 1967 with the opening of the Metro stores in Godorf nearCologne,Hamburg,Munichand, after a connection with the Dutch companySHV Holdings, the first C&C wholesale store (brand: Makro) in theNetherlands, and in 1968 inDüsseldorf.

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1980 – 2000

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: Metro_AG, 1980 – 2000: In 1980, Metro took over 24.9 per cent ofKaufhof. In March 1996, Metro AG was formed by the merger of Metro Cash & Carry withKaufhof Holding AG, Deutsche SB-Kauf AG (of the insolvent company co op AG), and Asko Deutsche Kaufhaus AG (emerged from Allgemeine Saar Konsum, in which a Metro investment company had previously held shares). The group also included the Huma shopping centres, the sports retail stores Primus Sportwelt, MHB Handel AG and the office supplies and stationery manufacturerPelikan, as well asMedia-Saturn, the consumer electronics business of Media Markt andSaturn. The share of Metro AG was founded retrospectively on January 1 and listed on 22 July 1996 on theFrankfurt Stock Exchangeand was part of theDAXuntil 2012. In 1998, the computer retailersVobisandMaxdata, the Adler fashion stores and Reno's shoe retailers, the discounter TiP, Möbel Roller and unprofitable Kaufhof branches were brought into the subsidiary Divaco, which was founded together with Deutsche Bank and the Gerling Group, in order to attract new buyers. In 1998, the 94 Allkauf -S department stores were bought, as was Allkauf Touristik Vertriebs GmbH with 160 travel agencies, which were sold again by Metro.

[["Metro_AG", "Company"], ["1998", "Year"], ["Allkauf_Touristik_Vertriebs_GmbH", "Company"], ["160", "number"], ["Allkauf_Touristik_Vertriebs_GmbH", "Product"], ["94", "number"], ["Allkauf", "Company"], ["1998", "Year"], ["Adler_Fashion", "Company"], ["Vobis", "Company"], ["Maxdata", "Company"], ["Reno_(company)", "Company"], ["TiP_(discount_store)", "Company"], ["Media-Saturn", "Company"], ["Deutsche_Bank", "Company"], ["Gerling_Group", "Company"], ["DAX", "Index"], ["1996-03-01", "Date"], ["Frankfurt_Stock_Exchange", "Place"], ["Kaufhof", "Company"], ["24.9", "number"], ["Primus_Sportwelt", "Company"], ["MHB_Handel_AG", "Company"], ["Pelikan", "Company"], ["1996-07-22", "Date"], ["Media_Markt", "Company"], ["Saturn_(store)", "Company"], ["Coop_AG", "Company"], ["Divaco", "Company"], ["Kaufhof", "Company"], ["Metro_Cash_&_Carry", "Company"], ["Deutsche_SB-Kauf_AG", "Company"], ["Asko_Deutsche_Kaufhaus_AG", "Company"], ["Allgemeine_Saar_Konsum", "Company"], ["Metro_Investment_Company", "Company"], ["DAX", "Index"], ["2012", "Year"]]


1980 – 2000

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: Metro_AG, 1980 – 2000: In 1980, Metro took over 24.9 per cent ofKaufhof. In March 1996, Metro AG was formed by the merger of Metro Cash & Carry withKaufhof Holding AG, Deutsche SB-Kauf AG (of the insolvent company co op AG), and Asko Deutsche Kaufhaus AG (emerged from Allgemeine Saar Konsum, in which a Metro investment company had previously held shares). The group also included the Huma shopping centres, the sports retail stores Primus Sportwelt, MHB Handel AG and the office supplies and stationery manufacturerPelikan, as well asMedia-Saturn, the consumer electronics business of Media Markt andSaturn. The share of Metro AG was founded retrospectively on January 1 and listed on 22 July 1996 on theFrankfurt Stock Exchangeand was part of theDAXuntil 2012. In 1998, the computer retailersVobisandMaxdata, the Adler fashion stores and Reno's shoe retailers, the discounter TiP, Möbel Roller and unprofitable Kaufhof branches were brought into the subsidiary Divaco, which was founded together with Deutsche Bank and the Gerling Group, in order to attract new buyers. In 1998, the 94 Allkauf -S department stores were bought, as was Allkauf Touristik Vertriebs GmbH with 160 travel agencies, which were sold again by Metro.

[["Metro_AG", "subsidiary", "Kaufhof"], ["Metro_AG", "subsidiary", "Media_Saturn"], ["Media_Saturn", "subsidiary", "Saturn_(store)"], ["Media_Saturn", "subsidiary", "Media_Markt"], ["Metro_AG", "subsidiary", "MHB_Handel_AG"], ["Metro_AG", "subsidiary", "Primus_Sportwelt"], ["Metro_AG", "subsidiary", "Huma_Schokolade"], ["Metro_AG", "subsidiary", "Asko_Deutsche_Kaufhaus_AG"], ["Metro_AG", "subsidiary", "Allkauf"], ["Allkauf", "subsidiary", "Allkauf_Touristik_Vertriebs_GmbH"], ["Metro_AG", "subsidiary", "Adler_(company)"], ["Metro_AG", "subsidiary", "Vobis"], ["Metro_AG", "subsidiary", "Maxdata"], ["Metro_AG", "subsidiary", "TiP_(discounter)"], ["Metro_AG", "subsidiary", "Möbel_Roller"], ["Metro_AG", "founded_by", "Coop_(company)"], ["Metro_AG", "shares", "Gerling_Group"], ["Metro_AG", "shares", "Deutsche_Bank"], ["Metro_AG", "shares", "Coop_(company)"], ["Metro_AG", "foundingDate", "1996-03-01"], ["Metro_AG", "hasProduct", "Pelikan"], ["Metro_AG", "keyPerson", "Gerhard_Werle"], ["Metro_AG", "regionServed", "Worldwide"], ["Metro_AG", "subsidiary", "Deutsche_SB-Kauf_AG"], ["Metro_AG", "type", "Company"], ["Metro_AG", "capital", "Frankfurt"], ["Metro_AG", "parentCompany", "Kaufhof"], ["Metro_AG", "product", "Consumer_electronics"], ["Metro_AG", "foundationPlace", "Germany"], ["Metro_AG", "location", "Germany"], ["Metro_AG", "numberOfLocations", "2000"], ["Metro_AG", "foundingYear", "2000"], ["Metro_AG", "leaderTitle", "CEO"], ["Metro_AG", "stockExchange', "Frankfurt_Stock_Exchange"], ["Metro_AG", "leader', "Olaf_Scholz"], ["Metro_AG", "isPartOf", "DAX"], ["Metro_AG", "longName", "Metro AG"], ["Metro_AG", "areaTotal', '2000.0"], ["Metro_AG", "leader', "Gerhard_Werle"], ["Metro_AG", "elevationAboveTheSeaLevel', '2000.0"], ["Metro_AG", "foundationYear', "2000"], ["Metro_AG", "country', "Germany"], {"Metro_AG", "subsidiary", "Deutsche_SB-Kauf_AG"}, {"Metro_AG", "keyPerson", "Gerhard_Werle"}, {"Metro_AG", "regionServed", "Worldwide"}, {"Metro_AG", "product', "Consumer_electronics"}, {"Metro_AG", "capital', "Frankfurt"}, {"Metro_AG", "parentCompany', "Kaufhof"}, {"Metro_AG", "foundationPlace', "Germany"}, {"Metro_AG", "numberOfLocations', "2000"}, {"Metro_AG", "type', "Company"}, {"Metro_AG", "location', "Germany"}, {"Metro_AG", "leaderTitle', "CEO"}, {"Metro_AG", "isPartOf', "DAX"}, {"Metro_AG", "longName', "Metro AG"}, {"Metro_AG", "areaTotal', "2000.0"}, {"Metro_AG", "leader', "Olaf_Scholz"}, {"Metro_AG", "elevationAboveTheSeaLevel', "2000.0"}, {"Metro_AG", "foundationYear', "2000"}, {"Metro_AG", "country', "Germany"}]


2000 – 2020

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: Metro_AG, 2000 – 2020: In December 2003, Metro separated from its stake in Divaco KG and sold its shares to the sole shareholder and CEO, Siegfried Kaske, for 1 euro. In 2004, Metro bought Adler fashion stores back from Divaco. In 2005, Metro split off thePraktikerhome improvementdivision, which went public as an independent company. In July 2006 Metro bought the 85 German stores ofWal-Mart, which gave up its loss-making Germany business. The Wal-Mart stores were largely integrated into theRealsales brand. In July 2008, the Extra supermarket chain with around 250 locations and sales of around 1.6 billion euro, was sold to theRewe Group. Metro sold the Adler fashion stores to the associated company BluO in February 2009. In October 2012, Makro-Habib in Pakistan becameMetro-Habib. In November 2012, Metro sold its 91 Real hypermarkets in Poland, Romania, Russia and Ukraine toAuchanfor 1.1 billion euro.In 2014 Metro sold the 12 Real hypermarkets in Turkey.In 2017 Metro sold the last remaining four Real hypermarkets in Romania. On 15 June 2015, Metro AG agreed to sellGaleria Kaufhofto Canadian retail conglomerateHudson's Bay Companyfor $3.2 billion. On 30 March 2016, Metro Group announced that it would be splitting into two independent companies:A spin-off of the wholesale and food sector of Metro AG will be responsible for the group divisions into two independent and publicly listed companies. Both will have their own management, supervisory board and independent company profiles. Metro AG was renamed Ceconomy, comprisingMedia MarktandSaturnelectronics stores, while a new company with the name Metro AG was formed, comprisingMetro Cash & CarryandReal. In September 2018, Metro announced that it wanted to sell theRealhypermarket subsidiary in Germany to focus entirely on wholesale business.In February 2020, Real was sold to The SCP Group.The deal was completed in June 2020.With the acquisition of Johbeco AB and its subsidiary Johan i Hallen & Bergfalk, a Swedish specialist supplier of meat, fish and seafood, for around 100 million euros in May 2023, Metro entered the Swedish and Finnish market.

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