2000 – 2020

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, 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.

[["Metro_AG", "subsidiary", "Galeria_Kaufhof"], ["Metro_AG", "subsidiary", "Real_(hypermarket)"], ["Metro_AG", "subsidiary", "Johann_A._Würth_GmbH_&_Co._KG"], ["Metro_AG", "subsidiary", "Makro"], ["Metro_AG", "subsidiary", "Makro_(Pakistan)"], ["Metro_AG", "subsidiary", "Adler_Fashion"], ["Metro_AG", "subsidiary", "Johann_i_Hallen_&_Bergfalk"], ["Metro_AG", "subsidiary", "Johbeco_AB"], ["Metro_AG", "subsidiary", "Media_Markt"], ["Metro_AG", "subsidiary", "Saturn_(electronics_store)"], ["Metro_AG", "subsidiary", "Praktiker"], ["Metro_AG", "subsidiary", "Metro_Cash_&_Carry"], ["Metro_AG", "subsidiary", "Real_(hypermarket)"], ["Metro_AG", "subsidiary", "SCP_Group"], ["Metro_AG", "subsidiary", "Wal-Mart"], ["Metro_AG", "subsidiary", "Divaco"], ["Metro_AG", "subsidiary", "Auchan"], ["Metro_AG", "subsidiary", "HBC_(company)"], ["Metro_AG", "subsidiary", "Johann_A._Würth_GmbH_&_Co._KG"], ["Metro_AG", "subsidiary", "Makro_(Pakistan)"], ["Metro_AG", "subsidiary", "Adler_Fashion"], ["Metro_AG", "subsidiary", "Johann_i_Hallen_&_Bergfalk"], ["Metro_AG", "subsidiary", "Johbeco_AB"], ["Metro_AG", "subsidiary", "Media_Markt"], ["Metro_AG", "subsidiary", "Saturn_(electronics_store)"], ["Metro_AG", "subsidiary", "Praktiker"], ["Metro_AG", "subsidiary", "Metro_Cash_&_Carry"], ["Metro_AG", "subsidiary", "Real_(hypermarket)"], ["Metro_AG", "subsidiary", "SCP_Group"], ["Metro_AG", "subsidiary", "Wal-Mart"], ["Metro_AG", "subsidiary", "Divaco"], ["Metro_AG", "subsidiary", "Auchan"], ["Metro_AG", "subsidiary", "HBC_(company)"], ["Metro_AG", "has_product_line", "Food"], ["Metro_AG", "has_product_line", "Building_materials"], ["Metro_AG", "has_product_line", "Household_goods"], ["Metro_AG", "has_product_line", "Clothing"], ["Metro_AG", "has_product_line", "Cosmetics"], ["Metro_AG", "has_product_line", "Toys"], ["Metro_AG", "has_product_line", "Sports_goods"], ["Metro_AG", "has_product_line", "Luggage"], ["Metro_AG", "has_product_line", "Automotive"], ["Metro_AG", "has_product_line", "Industrial_goods"], ["Metro_AG", "has_product_line", "Appliances"], ["Metro_AG", "has_product_line", "Electronic_components"], ["Metro_AG", "has_product_line", "Computers"], ["Metro_AG", "has_product_line", "Monitors"], ["Metro_AG", "has_product_line", "Printers"], ["Metro_AG", "has_product_line", "Televisions"], ["Metro_AG", "has_product_line", "Video_games"], ["Metro_AG", "has_product_line", "Home_theatre"], ["Metro_AG", "has_product_line", "Mobile_devices"], ["Metro_AG", "has_product_line", "Smartphones"], ["Metro_AG", "has_product_line", "Watches"], ["Metro_AG", "has_product_line", "Clocks"], ["Metro_AG", "has_product_line", "Music"], ["Metro_AG", "has_product_line", "Musical_instruments"], ["Metro_AG", "has_product_line", "Film"], ["Metro_AG", "has_product_line", "Television_programming"], ["Metro_AG", "has_product_line", "Video"], ["Metro_AG", "has_product_line", "Games"], ["Metro_AG", "has_product_line", "Toy"], ["Metro_AG", "has_product_line", "Food"], ["Metro_AG", "has_product_line", "Building_materials"], ["Metro_AG", "has_product_line", "Household_goods"], ["Metro_AG", "has_product_line", "Clothing"], ["Metro_AG", "has_product_line", "Cosmetics"], ["Metro_AG", "has_product_line", "Toys"], ["Metro_AG", "has_product_line", "Sports_goods"], ["Metro_AG", "has_product_line", "Luggage"], ["Metro_AG", "has_product_line", "Automotive"], {"Metro_AG", "has_stake": "Divaco"}, {"Metro_AG", "has_stake": "Siegefried_Kaske"}, {"Metro_AG", "has_stake": "1.0"}, {"Metro_AG", "has_stake": "Auchan"}, {"Metro_AG", "has_stake": "HBC_(company)"}, {"Metro_AG", "has_stake": "BluO"}, {"Metro_AG", "has_stake": "Johann_A._Würth_GmbH_&_Co._KG"}, {"Metro_AG", "has_stake": "Johann_i_Hallen_&_Bergfalk"}, {"Metro_AG", "has_stake": "Makro_(Pakistan)"}, {"Metro_AG", "has_stake": "Real_(hypermarket)"}, {"Metro_AG", "has_stake": "SCP_Group"}, {"Metro_AG", "has_stake": "Wal-Mart"}, {"Metro_AG", "has_stake": "Johbeco_AB"}, {"Metro_AG", "has_stake": "Adler_Fashion"}]


2020 – present

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, 2020 – present: In April 2025, Metro applied fordelistingfrom the Frankfurt Stock Exchange. With the revocation of its listing, its inclusion in the GermanSDAXwas terminated.

[["Metro_AG", "Company"], ["Frankfurt_Stock_Exchange", "Place"], ["German_Sdax", "Index"], ["2020", "Date"], ["\"April 2025\"", "Date"], ["Delisting", "Action"]]


2020 – present

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, 2020 – present: In April 2025, Metro applied fordelistingfrom the Frankfurt Stock Exchange. With the revocation of its listing, its inclusion in the GermanSDAXwas terminated.

[["Metro_AG", "in_year", "2020"], ["Metro_AG", "has_product_line", "\"2025-04-27\""], ["Metro_AG", "in_year", "\"2025-04-27\""], ["Metro_AG", "is_publicly_traded", "Frankfurt_Stock_Exchange"], ["Metro_AG", "in_year", "2025"], ["Metro_AG", "has_product_line", "German_Sdax"]]


Controversies

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

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

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

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Metro_AG, Controversies: Following the2022 Russian invasion of Ukrainewhich began on 24 February, many international, particularly Western companies pulled out of Russia. Unlike most of its Western competitors, Metro AG announced its intention to keep its business in Russia open, drawing criticism.Ukrainian government officials have called for a global boycott of the company.The Ukrainian office of MetroAG has allegedly been threatened by the company's headquarters in Germany due to the Ukrainian branch openly calling for sanctions on Metro's Russian branch. According to Ukrainian ex-ministerDmytro Dubiletthe Ukrainian branch has received warnings to be disconnected from the centralized supply.In February 2023, the National Agency for the Prevention of Corruption of Ukraine included the company in the list ofInternational Sponsors of War.

[["Metro_AG", "Company"], ["2022_Russian_invasion_of_Ukraine", "Event"], ["Ukraine", "Company"], ["National_Agency_for_the_Prevention_of_Corruption_(Ukraine)", "Organization"], ["\"Ukrainian branch of Metro AG has been threatened by the company's headquarters in Germany\"", "string"], ["Dmytro_Dubilet", "Person"], ["\"Ukrainian branch of Metro AG called for sanctions on Metro's Russian branch\"", "string"], ["\"Ukrainian branch of Metro AG will be disconnected from the centralized supply\"", "string"], ["International_Sponsors_of_War", "Award"], ["2023-02-28", "Date"]]


Controversies

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

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

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

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

Analyze the text and targets carefully, identify relevant information.

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

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Metro_AG, Controversies: Following the2022 Russian invasion of Ukrainewhich began on 24 February, many international, particularly Western companies pulled out of Russia. Unlike most of its Western competitors, Metro AG announced its intention to keep its business in Russia open, drawing criticism.Ukrainian government officials have called for a global boycott of the company.The Ukrainian office of MetroAG has allegedly been threatened by the company's headquarters in Germany due to the Ukrainian branch openly calling for sanctions on Metro's Russian branch. According to Ukrainian ex-ministerDmytro Dubiletthe Ukrainian branch has received warnings to be disconnected from the centralized supply.In February 2023, the National Agency for the Prevention of Corruption of Ukraine included the company in the list ofInternational Sponsors of War.

[["Metro_AG", "has_branch", "Ukraine"], ["Metro_AG", "has_branch", "Russia"], ["Metro_AG", "controversy", "2022_Russian_invasion_of_Ukraine"], ["Metro_AG", "controversy", "National_Agency_for_the_Prevention_of_Corruption_(Ukraine)"], ["Metro_AG", "controversy", "\"Ukrainian office of Metro AG has allegedly been threatened by the company's headquarters in Germany due to the Ukrainian branch openly calling for sanctions on Metro's Russian branch.\""], ["Metro_AG", "controversy", "\"In February 2023, the National Agency for the Prevention of Corruption of Ukraine included the company in the list of International Sponsors of War.\""], ["Metro_AG", "controversy", "\"Ukrainian ex-minister Dmytro Dubilet said that the Ukrainian branch has received warnings to be disconnected from the centralized supply.\""], ["Metro_AG", "controversy", "\"Following the 2022 Russian invasion of Ukraine, which began on 24 February 2022, many international, particularly Western companies pulled out of Russia. Unlike most of its Western competitors, Metro AG announced its intention to keep its business in Russia open, drawing criticism.\""]]


Metro on Russian market

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 on Russian market: Metro AG is Russia's fourth-biggest retailer behind X5 PJPq.L, Magnit MGNTq.L and French chain Auchan.The company entered the market in 2001 with the opening of its first wholesale store in Moscow.Over the years, Metro AG has expanded its presence in the country, opening stores in several other cities, including Saint Petersburg, Nizhny Novgorod and Rostov-on-Don. There are 97 stores in 65 cities across the country.In 2022, the company's revenue amounted to 224 billion rubles. On 22 March 2022, in response to the ongoing war in Ukraine, Metro AG published an official statement expressing solidarity with Ukraine and condemning the war.The company also announced its support for the food and water deliveries of the UN World Food Programme to Ukraine. Despite that, Metro AG made a decision not to leave the Russian market, citing its responsibility to the 10,000 colleagues employed there and the many customers who rely on its stores. The company stated that it is closely monitoring further developments and is committed to supporting “decision-makers in business and politics who are seeking a peaceful solution to the war in Ukraine”. Currently, Metro AG is developing the integration of new projects in Russia, such as the establishment of the grocery stores chain "Fasol"and the acceptance of payments by "Mir" cards,a project initiated by "Sberbank" in response to sanctions. Furthermore, the investigation by the National Agency on Corruption Prevention (NACP) revealed that the key shareholder of Metro Cash & Carry, Daniel Kretynski, controls EP Infrastructure (EPIF), which owns 49% of Eustream,the company that operates a gas transportation system from Russia to Central and Eastern Europe. This fact, combined with the company's actions, led to Metro Cash & Carry being added to the NACP's list of international sponsors of war. Metro AG's latest quarterly financial results report,which was released on 8 February 2023, indicates a decrease in sales at the chain's stores in Russia. The document states that the sales in local currency decreased by 14.1% in the first quarter of fiscal year 2022–2023; adjusted EBITDA in Russia decreased to €60 million from €81 million in Q1 2021/2022.

[["Metro_AG", "Company"], ["Russia", "Place"], ["\"Fasol\"", "Product"], ["\"Mir\" (payment system)", "Product"], ["224000000000.0", "number"], ["\"Saint Petersburg\"", "Place"], ["\"Nizhny Novgorod\"", "Place"], ["\"Rostov-on-Don\"", "Place"], ["\"Metro AG entered the Russian market in 2001\"", "Date"], ["97", "number"], ["10,000", "number"], ["\"Daniel Kretzynski\"", "Person"], ["EP_Infrastructure", "Company"], ["Eustream", "Company"], ["\"National Agency on Corruption Prevention\"", "Company"], ["\"Metro AG supports the UN World Food Programme's efforts to deliver food and water to Ukraine\"", "Action"], ["\"Metro AG is committed to supporting decision-makers in business and politics who are seeking a peaceful solution to the war in Ukraine\"", "Action"], ["\"Saint Petersburg\"", "City"], ["\"Moscow\"", "City"], ["\"14.1%\"", "number"], ["\"60.0 million (EUR)\"", "number"], ["\"81.0 million (EUR)\"", "number"], ["\"2022-03-22\"", "Date"], ["\"Metro AG's revenue in Russia was 224.0 billion rubles\"", "number"], ["\"Moscow\"", "Place"], ["\"Fiscal year 2022–2023\"", "number"], ["\"Q1 2021–2022\"", "TimePeriod"], ["\"Q1 2022–2023\"", "TimePeriod"]]


Metro on Russian market

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 on Russian market: Metro AG is Russia's fourth-biggest retailer behind X5 PJPq.L, Magnit MGNTq.L and French chain Auchan.The company entered the market in 2001 with the opening of its first wholesale store in Moscow.Over the years, Metro AG has expanded its presence in the country, opening stores in several other cities, including Saint Petersburg, Nizhny Novgorod and Rostov-on-Don. There are 97 stores in 65 cities across the country.In 2022, the company's revenue amounted to 224 billion rubles. On 22 March 2022, in response to the ongoing war in Ukraine, Metro AG published an official statement expressing solidarity with Ukraine and condemning the war.The company also announced its support for the food and water deliveries of the UN World Food Programme to Ukraine. Despite that, Metro AG made a decision not to leave the Russian market, citing its responsibility to the 10,000 colleagues employed there and the many customers who rely on its stores. The company stated that it is closely monitoring further developments and is committed to supporting “decision-makers in business and politics who are seeking a peaceful solution to the war in Ukraine”. Currently, Metro AG is developing the integration of new projects in Russia, such as the establishment of the grocery stores chain "Fasol"and the acceptance of payments by "Mir" cards,a project initiated by "Sberbank" in response to sanctions. Furthermore, the investigation by the National Agency on Corruption Prevention (NACP) revealed that the key shareholder of Metro Cash & Carry, Daniel Kretynski, controls EP Infrastructure (EPIF), which owns 49% of Eustream,the company that operates a gas transportation system from Russia to Central and Eastern Europe. This fact, combined with the company's actions, led to Metro Cash & Carry being added to the NACP's list of international sponsors of war. Metro AG's latest quarterly financial results report,which was released on 8 February 2023, indicates a decrease in sales at the chain's stores in Russia. The document states that the sales in local currency decreased by 14.1% in the first quarter of fiscal year 2022–2023; adjusted EBITDA in Russia decreased to €60 million from €81 million in Q1 2021/2022.

[["Metro_AG", "has_product_line", "Metro_Cash_&_Carry"], ["Metro_AG", "revenue", "224000000000.0"], ["Metro_AG", "subsidiary", "Metro_Cash_&_Carry"], ["Metro_Cash_&_Carry", "key_person", "Daniel_Kretynski"], ["Metro_Cash_&_Carry", "shares", "EP_Infrastructure"], ["Metro_Cash_&_Carry", "shares", "Eustream"], ["Metro_AG", "subsidiary", "Fasol"], ["Metro_AG", "revenue", "€60.0 million"], ["Metro_AG", "subsidiary", "Sberbank"], ["Metro_AG", "revenue", "€81.0 million"], ["Metro_AG", "subsidiary", "Mir_(payment_system)"], ["Metro_AG", "region", "Russia"], ["Metro_AG", "number", "97"], ["Metro_AG", "number", "65"], ["Metro_AG", "revenue", "14.1%"], ["Metro_AG", "subsidiary", "Metro_Russia"], ["Metro_AG", "statement", "\"Metro AG is committed to supporting decision-makers in business and politics who are seeking a peaceful solution to the war in Ukraine.\""], ["Metro_AG", "subsidiary", "Auchan"], ["Metro_AG", "subsidiary", "Magnit"], ["Metro_AG", "subsidiary", "X5_Pizza_Hut"], ["Metro_AG", "subsidiary", "Moscow"], ["Metro_AG", "subsidiary", "Saint_Petersburg"], ["Metro_AG", "subsidiary", "Nizhny_Novgorod"], ["Metro_AG", "subsidiary", "Rostov-on-Don"], ["Metro_AG", "subsidiary", "United_Nations_World_Food_Programme"], ["Metro_AG", "subsidiary", "Ukraine"], ["Metro_AG", "subsidiary", "War_in_Ukraine"], ["Metro_AG", "subsidiary", "Sberbank"], ["Metro_AG", "subsidiary", "EP_Infrastructure"], ["Metro_AG", "subsidiary", "Eustream"], ["Metro_AG", "subsidiary", "Metro_Cash_&_Carry"], ["Metro_AG", "subsidiary", "Fasol"], ["Metro_AG", "subsidiary", "Mir_(payment_system)"]]


Former operations

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, Former operations: Metro sold in 2010, the Makro subsidiary inMorocco. In 2012, Metro exited theUnited Kingdommarket through the sale of its 30 Makro stores toBooker Group. In 2014, Metro divested itsGreeceoperations by selling 9 Makro stores to Sklavenitis. In 2014, Metro exitedVietnamby signing an agreement to sell its local subsidiary, consisting of 19 stores, to the Thai group Berli Jucker for €665 million. In December 2014, Metro exited theDenmarkmarket. They had pioneered in-store delivery services nationally since opening their first store in 1971. Egypthad a brief market foray in 2010 with two stores opening, but these were closed between 2013 and 2015. In April 2020, Metro completed the sale of a majority stake in its Chinese joint venture toWumei Technology Groupfor more than €1.5 billion. Metro retained a 20% ownership stake in the venture, which operates 97 wholesale centers acrossChina. In August 2021, Metro ceased operations inJapan, resulting in the closure of all its 10 stores within the country. In September 2021, Metro announced it would end its supply business inMyanmarby the end of October 2021. In 2022, Metro exited fromBelgiumthrough thebankruptcyof its local subsidiary, which operated stores under both the Metro and Makro brands.Notably, Belgium was the only country where Metro had stores under both brands, and it was also unique in allowing general access to its Makro network, typically reserved for business customers. Only Metro wholesale network stores that avoided bankruptcy were subsequently sold and reopened under the Sligro-M brand. In 2023, their operations inIndiawere sold toReliance Retail.

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