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

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

Q322598 - Galeria Kaufhof: former German department store chain

Q80220059 - Galeria Karstadt Kaufhof: German department store

Q118205782 - Galeria Kaufhof: building in Braunschweig, Germany

Q135289885 - Galeria Kaufhof: department store in Paderborn, Germany

Q1491834 - Galeria Kaufhof Berlin-Alexanderplatz: None

Metro_AG

Metro_AG

Metro_AG

Q704606 - Makro: Dutch brand of supermarket chains

Q62246 - polycarbonate: family of polymers

Metro_AG

Metro_AG

Metro_AG

Metro_AG

Metro_AG

Q2381223 - MediaMarkt: retail chain specializing in electronics and consumer appliances

Q108169182 - Media Markt Saturn W Polsce: company

Metro_AG

Metro_AG

Q680214 - Praktiker: German marketplace for construction and DIY enthusiasts

Q23011076 - Praktiker: hardware store chain in Greece

Q120483743 - Praktiker: hardware store chain in Hungary

Q120483788 - Praktiker: hardware store chain in Turkey

Q110399491 - Praktiker: hardware store chain in Bulgaria

Metro_AG

Q13610282 - Metro Cash & Carry: international self-service wholesaler

Q117814984 - Metro Cash & Carry: Russian company

Q33093143 - METRO Cash & Carry International Holding: holding company

Q12770687 - METRO Cash & Carry Slovakia: Slovak wholesale company

Q111850935 - Metro Cash & Carry Bulgaria: company in Bulgaria

Metro_AG

Metro_AG

Q483551 - Walmart: U.S. discount retailer based in Arkansas

Q127256141 - Wal-Mart: episode of Penn & Teller: Bullshit! (S5 E2)

Q49254226 - Wal-Mart Stores Inc.: painting by Tom Sachs

Q3282877 - Wal-Mart: The High Cost of Low Price: 2005 film by Robert Greenwald

Q7961235 - Wal-Mart v. Dukes: 2011 United States Supreme Court case

Metro_AG

Q557216 - Municipality of Divača: municipality of Slovenia

Q134643084 - Divacore International: None

Metro_AG

Q758603 - Auchan: French multinational retail company

Q110053013 - Auchan Retail Russia: chain of stores in Russia, a subsidiary of French retail

Q34248502 - Auchanitis (Pauly-Wissowa): encyclopedic article in Paulys Realencyclopädie der classischen Altertumswissenschaft (RE)

Q125868206 - Auchan: group of bus stops in Olsztyn, Poland urban area

Metro_AG

Metro_AG

Q2095 - food: any substance consumed to provide nutritional support for the body; form of energy stored in chemical

Q8195619 - human food: food eaten by Homo sapiens

Q173710 - esophagus: vertebrate organ through which food passes to the stomach

Q3007875 - Food Chemistry: peer-reviewed scientific journal

Metro_AG

Q206615 - building material: material used for construction purposes

Q12144907 - construction materials industry: production and supply of physical materials involved in constructing and maintaining buildings

Q811891 - building materials trade: an economic sector of trade

Q99318011 - building materials dealer: place where building materials are sold

Metro_AG

Q369594 - household goods: goods and products used within households

Q59679273 - household equipment: furnishings, appliances, and other equipment used in the home

Q111524239 - Household Goods: print after drawing by Johannes Rach and Odvardt Helmoldt de Lode (Nasjonalmuseet, NMK.2021.0112.038)

Q119411553 - Household Goods: painting by Prunella Clough (1919–1999), Government Art Collection

Q11455447 - Household Goods Quality Labeling Act: None

Metro_AG

Q11460 - clothing: covering worn on the body

Q3501317 - fashion designer: one who designs clothing and related items

Q1065579 - costume accessory: item carried or worn which is used to contribute to the wearer's outfit

Q56876960 - heraldic clothing: category of heraldic charges

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

Metro_AG

Q131207 - cosmetics: substances used to enhance the appearance or odor of the human body

Q12752882 - cosmetics industry: industry that manufactures and distributes cosmetic products

Q19787844 - Cosmetics Europe: organization in Brussels, Belgium

Metro_AG

Q1849238 - Toys: 1992 film directed by Barry Levinson

Q6168 - Töysä: former municipality of Finland, now part of Alavus

Q1255568 - toy goods: toys and games intended for retail sale

Q114504341 - Toys: episode of The Mr. Men Show (S2 E11)

Metro_AG

Q115753818 - sports goods: class in the Central Product Classification Version 2.1

Metro_AG

Q865054 - baggage: cases or container for storing traveler's items

Q1869423 - Luggage: war memorial and sculpture in Leiden, the Netherlands

Q10651198 - luggage: baggage a passenger brings with them, railroad term

Q110974457 - Luggage Store Gallery: Art nonprofit organization in San Francisco

Metro_AG

Q190117 - automotive industry: range of organizations associated with motor vehicles, such as automobiles, trucks and motorcycles

Q786820 - automobile manufacturer: company that makes automobiles

Q850270 - concept car: car designed to showcase prototype features

Q2090494 - automobile safety: study and practice to minimize the occurrence and consequences of motor vehicle accidents

Q124192 - automotive engineering: discipline of engineering

Metro_AG

Q1836700 - manufactured good: object produced by industry

Metro_AG

Q4781455 - Appliances Online: Australian online retailer

Q113430883 - Appliances and instruments : China: folder of the 20th Century Press Archives

Q113430889 - Appliances and instruments : Mexico: folder of the 20th Century Press Archives

Q113428613 - Appliances and instruments : Poland: folder of the 20th Century Press Archives

Q113430876 - Appliances and instruments : Argentina: folder of the 20th Century Press Archives

Metro_AG

Q5570655 - Global Standards Collaboration: organization

Q102089992 - ECSEL Joint Undertaking: public-private partnership for electronic components and systems, co-funded by the EU, member states and industry associations

Q124217536 - Electronic components and glasses: US patent 11272278

Q123209879 - Electronic Components and Accessories: SIC industry classification

Q123188381 - Electronic Components, Not Elsewhere Classified: SIC industry classification

Metro_AG

Q68 - computer: general-purpose device for performing arithmetic or logical operations

Q13475188 - Computers & Security: journal

Q2025825 - Computers in Biology and Medicine: scientific journal

Q50808407 - Computers: Academic journal published by MDPI AG , covering the subjects: Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Q15759884 - Computers Informatics Nursing: journal

Metro_AG

Q65022024 - Monitors: print in the National Gallery of Art (NGA 11105)

Q110149575 - Monitors: etching - print made by James McNeill Whistler

Q16253336 - The Monitors: 1969 film by Jack Shea

Q10331864 - Monitors: fictional race from DC Comics

Metro_AG

Q1260821 - printing ink: ink especially formulated for plate or block printing

Q3356530 - typographic ornament: page adornment used in printing

Q100319411 - Post-transcriptional regulation of effector function in T cells by RNA binding proteins: EU Funded (H2020) Research Project

Q135022498 - Printersim: 2021 video game developed by THISPOLO LTD

Q19436110 - Printersdreef: street in Maastricht, the Netherlands

Metro_AG

Q63864279 - Televisions, Ponte City, Johannesburg: sculpture in the National Gallery of Art (NGA 168522)

Q100570159 - Televisions and Gramophone Records Regulations, 1984: Irish Statutory Instrument S.I. No. 363/1984

Q100573117 - Televisions and Gramophone Records Regulations, 1979: Irish Statutory Instrument S.I. No. 61/1979

Q9085591 - Televisions Digitals Independents: None

Q8261587 - Televisions Locals Catalanes de Proximitat: None

Metro_AG

Q75115362 - Video Games: vocal track by Lana Del Rey; 2011 studio recording

Q7889 - video game: electronic game with user interface and visual feedback

Q771253 - Video Games: original song written and composed by Lana Del Rey and Justin Parker; first recorded by Lana Del Rey

Q115920686 - video games, consoles and accessories: product category including video games and their accessories

Q47510340 - Video Games: painting by Todd Schorr

Metro_AG

Q42941076 - Home Theatre: former movie theater in Blair, Nebraska, United States

Q43339818 - Home Theatre: former movie theater in New Lisbon, Wisconsin, United States

Q43337253 - Gem Theatre: former movie theater in Milwaukee, Wisconsin, United States

Q42538628 - Radio Theatre: former movie theater in Chicago, Illinois, United States

Q42606591 - Home Theatre: former movie theater in Davenport, Iowa, United States

Metro_AG

Q93704307 - Mobile Devices to Detect Early Pneumonitis in Stage III NSCLC Patients on Durvalumab.: clinical trial

Q125321191 - Mobile devices supporting international students to overcome language and cultural difficulties during study abroad: doctoral thesis

Q63403846 - Mobile Devices as Educational Skin History Tools: clinical trial

Q119175796 - Mobile devices, methods, and computer program products for prioritizing data sets: US patent 11132668

Q112883689 - Mobile devices: iPhone risks and Forensic Tool capability: 2010 master's thesis by Benjamin Andrew Knight at Auckland University of Technology

Metro_AG

Q22645 - smartphone: handheld mobile telephone/computer

Q18163867 - SmartPhones: 2014 single by Trey Songz

Q24901351 - smartphones and pedestrian safety: pedestrian focused on their smartphone

Q114566384 - Smartphones as steady companions: device use in everyday life and the economics of attention: doctoral thesis by Maximilian Heitmayer

Q14121150 - Smartphones by Nokia: Wikimedia list article

Metro_AG

Q178794 - watch: personal timepiece, typically worn on the wrist or carried in a pocket

Q114771092 - number of viewers/listeners: number of viewers of a television or broadcasting program; web traffic on websites

Q18615458 - Watches: 1420th strip of the webcomic xkcd

Q2887818 - ESPN3: online streaming service owned by ESPN Inc.

Metro_AG

Q615063 - Clocks: 2002 single by Coldplay

Q2410332 - The Clocks: 2009 film by Charles Palmer

Q114521996 - Clocks: episode of The Mr. Men Show (S2 E30)

Q114526375 - Clocks: episode of Modern Marvels (S6 E14)

Metro_AG

Q14005 - MusicBrainz: online music metadata database

Q639669 - musician: person who composes, conducts or performs music

Q482994 - album: grouping of album releases by an artist usually released at the same time with the same title and tracks but in different formats for consumption (digital, CD, LP)

Metro_AG

Q67429143 - Musical Instruments: curatorial department of The Metropolitan Museum of Art in New York, USA

Q28532886 - Musical Instruments: painting by Ernest Leopold Sichel

Q18689723 - Musical Instruments: painting by Fernand Léger

Q20539519 - Musical Instruments: painting by Evaristo Baschenis

Q110014648 - Musical Instruments: drawing - attributed to Gangaram Chintaman Tambat

Metro_AG

Q11424 - film: sequence of images that give the impression of movement, stored on film stock

Q1137203 - thin film: a layer of material ranging from fractions of a nanometer to several micrometers in thickness

Q2526255 - film director: person who controls the artistic and dramatic aspects of a film production

Q41253 - movie theater: venue, usually a building, for viewing films

Q93204 - documentary film: nonfictional motion picture

Metro_AG

Q13821074 - television programming: practice of organizing television programs in to a schedule

Q2155186 - program block: group of similar broadcast programs aired in sequence

Q12376699 - television programming: sequence of programs planned to be broadcasted on a TV channel

Q29584758 - television programming and broadcasting activities: economic activity

Q112122773 - television programming and broadcasting: None

Metro_AG

Q34508 - video recording: technique of recording, copying and broadcasting of moving visual images

Q7889 - video game: electronic game with user interface and visual feedback

Q723685 - video on demand: systems which allow users to select and watch to video content on demand

Q98069877 - video: specific video with defining traits like framerate

Metro_AG

Q8567 - 2008 Summer Olympics: Games of the XXIX Olympiad, in Beijing, China

Q8577 - 2012 Summer Olympics: Games of the XXX Olympiad, in London, United Kingdom

Q8558 - 2004 Summer Olympics: Games of the XXVIII Olympiad, in Athens, Greece

Q8544 - 2000 Summer Olympics: Games of the XXVII Olympiad, in Sydney, Australia

Metro_AG

Q11422 - toy: object intended to be played with

Q53268 - Toyota: Japanese multinational automotive manufacturer

Q1150311 - Toy's Factory: Japanese record label

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

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Output: None of the candidates


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.

[
    "Q169167 - METRO AG: German wholesale company",
    "Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela",
    "Q8191806 - Agrícola Oriental: metro station in Mexico City",
    "Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article"
]

[
    "Q151139 - Frankfurt Stock Exchange: stock exchange located in Frankfurt, Germany",
    "Q5616081 - Template:Frankfurt Stock Exchange: Wikimedia template"
]

[
    "Q25337 - 2020: year",
    "Q5978933 - 2020: natural number",
    "Q107442757 - 2020 Philippine census: census of the Philippines as of 1 May 2020",
    "Q23766566 - 2020 United States Census: 24th national census of the United States, taken on April 1, 2020",
    "Q99659280 - Second Nagorno-Karabakh War: armed conflict in South Caucasus"
]

[
    "Q73545549 - delisting: formal removal from an official list (Wikibase reason for deprecated rank)",
    "Q1184312 - delisting: None",
    "Q116658061 - Delisting: removal of a product from the list of those sold by a particular retailer",
    "Q122089382 - Delisting: proces ukončení veřejné obchodovatelnosti účastnických cenných papírů: book edition published in 2021",
    "Q40741790 - Delisting of liver transplant candidates with chronic hepatitis C after viral eradication: A European study: scientific article published on 17 May 2016"
]


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

Q169167 - METRO AG: German wholesale company

Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela

Q8191806 - Agrícola Oriental: metro station in Mexico City

Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article

Q107442757 - 2020 Philippine census: census of the Philippines as of 1 May 2020

Q23766566 - 2020 United States Census: 24th national census of the United States, taken on April 1, 2020

Q99659280 - Second Nagorno-Karabakh War: armed conflict in South Caucasus

Metro_AG

Q151139 - Frankfurt Stock Exchange: stock exchange located in Frankfurt, Germany

Q5616081 - Template:Frankfurt Stock Exchange: Wikimedia template

Metro_AG

Q135079650 - 2025: video game developed by Big Bros

Q3598057 - 20252 Eyjafjallajökull: asteroid

Metro_AG


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.

[
    "Q169167 - METRO AG: German wholesale company",
    "Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela",
    "Q8191806 - Agrícola Oriental: metro station in Mexico City",
    "Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article"
]

[
    "Q110999040 - Russian invasion of Ukraine: ongoing military conflict in Eastern Europe since 2022",
    "Q111012213 - timeline of the Russian invasion of Ukraine: Wikimedia timeline",
    "Q113703507 - 2022 Russian Invasion of Ukraine and Change of the Finnish Foreign and Security Policy Line: A Paradigm Shift and the Birth of a New Political Consensus?: academic article published on 30 June 2022"
]

[
    "Q212 - Ukraine: country in Eastern Europe",
    "Q110999040 - Russian invasion of Ukraine: ongoing military conflict in Eastern Europe since 2022",
    "Q170403 - Ukraine men's national football team: men's national association football team representing Ukraine",
    "Q2296961 - Ukraina Stadium: multi-purpose stadium in Lviv, Ukraine",
    "Q4220180 - Kievskaia starina: ethographic and literary chronicle"
]

[
    "Q47107801 - Dmytro Dubilet: Ukrainian banker, businessman and politician"
]

[
    "Q121868041 - International Sponsors of War: Ukrainian list of Russian-aligned companies and individuals"
]

[
    "Q69306723 - February 28, 2023: date in Gregorian calendar"
]


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

P452 - industry: specific industry of company or organization

P241 - military branch: branch to which this military unit, award, office, or person belongs, e.g. Royal Navy

P361 - part of: object of which the subject is a part (if this subject is already part of object A which is a part of object B, then please only make the subject part of object A), inverse property of "has part" (P527, see also "has parts of the class" (P2670))

P807 - separated from: subject was founded or started by separating from identified object

P8368 - number of branches: number of branches or stores affiliated with a chain, brand or company

Q212 - Ukraine: country in Eastern Europe

Q110999040 - Russian invasion of Ukraine: ongoing military conflict in Eastern Europe since 2022

Q170403 - Ukraine men's national football team: men's national association football team representing Ukraine

Q2296961 - Ukraina Stadium: multi-purpose stadium in Lviv, Ukraine

Q4220180 - Kievskaia starina: ethographic and literary chronicle

Metro_AG

Q159 - Russia: country in Eastern Europe and Northern Asia

Q34266 - Russian Empire: former empire in Eurasia and North America (1721–1917)

Q206855 - Russian Wikipedia: Russian-language edition of Wikipedia

Metro_AG

P1310 - statement disputed by: entity that disputes a given statement

Q111012213 - timeline of the Russian invasion of Ukraine: Wikimedia timeline

Q113703507 - 2022 Russian Invasion of Ukraine and Change of the Finnish Foreign and Security Policy Line: A Paradigm Shift and the Birth of a New Political Consensus?: academic article published on 30 June 2022

Metro_AG

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


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.

[
    "Q169167 - METRO AG: German wholesale company",
    "Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela",
    "Q8191806 - Agrícola Oriental: metro station in Mexico City",
    "Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article"
]

[
    "Q159 - Russia: country in Eastern Europe and Northern Asia",
    "Q34266 - Russian Empire: former empire in Eurasia and North America (1721–1917)",
    "Q23890440 - Russia: genus of Ostracoda",
    "Q7737 - Russian: East Slavic language",
    "Q206855 - Russian Wikipedia: Russian-language edition of Wikipedia"
]

[
    "Q673849 - 97: natural number",
    "Q17012 - Guadeloupe: overseas region and department of France, and archipelago in the Caribbean",
    "Q135915087 - 97: song written and composed by Thomas Stenström, Isak Friberg and Johannes Runemark; originally recorded by Thomas Stenström and released in 2023",
    "Q135915086 - 97: vocal track by Thomas Stenström; 2023 studio recording",
    "Q30960 - 97: year"
]

[
    "Q427923 - 10,000: natural number",
    "Q163892 - 10,000 metres: common long distance running event",
    "Q60388564 - 10,000 Dresses: 2008 edition of the work 10,000 Dresses by Marcus Ewert",
    "Q507 - googol: large integer number defined as ten raised to the power of 100",
    "Q163819 - 10,000 Maniacs: United States-based alternative rock band"
]

[
    "Q27449963 - EP Infrastructure: company in the Czech Republic"
]

[
    "Q13408692 - Eustream: Slovak company"
]


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

Q13610282 - Metro Cash & Carry: international self-service wholesaler

Q117814984 - Metro Cash & Carry: Russian company

Q33093143 - METRO Cash & Carry International Holding: holding company

Q12770687 - METRO Cash & Carry Slovakia: Slovak wholesale company

Q111850935 - Metro Cash & Carry Bulgaria: company in Bulgaria

Metro_AG

P2139 - total revenue: income gained by an organization during a given time frame. Not to be confused with fiscal revenue

P2770 - source of income: source of income of an organization or person

Metro_AG

Metro_Cash_&_Carry

Q117814984 - Metro Cash & Carry: Russian company

Q111850935 - Metro Cash & Carry Bulgaria: company in Bulgaria

P3342 - significant person: person linked to the item in any possible way

Metro_Cash_&_Carry

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

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

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

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

Q27449963 - EP Infrastructure: company in the Czech Republic

Metro_Cash_&_Carry

Metro_AG

Q3766446 - Giovanni Antonio Fasolo: Italian painter (1530-1572)

Q42339 - Phaseolus vulgaris: species of plant

Metro_AG

Metro_AG

Q205012 - Sberbank: Russian banking and financial services company

Q112049927 - Sberbank Magyarország: Hungarian bank (2012-2022)

Q117705654 - Sberbank: Sochi stop bus, Krasnodar Krai, Russia

Q4077351 - Sberbank of the USSR: Soviet bank (1987-1991)

Q18193621 - Sberbank CZ: former bank in the Czech Republic

Metro_AG

Metro_AG

Metro_AG

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

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

P518 - applies to part: part, aspect, or form of the item to which the claim applies

P1071 - location of creation: place where the item was conceived or made; where applicable, location of final assembly

P2541 - operating area: geographic area or jurisdiction an organisation or industry operates in, serves, or has responsibility for

Q159 - Russia: country in Eastern Europe and Northern Asia

Q34266 - Russian Empire: former empire in Eurasia and North America (1721–1917)

Q206855 - Russian Wikipedia: Russian-language edition of Wikipedia

Metro_AG

P1545 - series ordinal: position of an item in its parent series (most frequently a 1-based index), generally to be used as a qualifier (different from "rank" defined as a class, and from "ranking" defined as a property for evaluating a quality)

P433 - issue: issue of a newspaper, a scientific journal or magazine for reference purpose

P1114 - quantity: number of instances of this subject in the universe of the subject (the actual number of instances in Wikidata may be lower or higher)

P478 - volume: volume of a book or music release in a collection/series or a published collection of journal issues in a serial publication

P1104 - number of pages: number of pages in an edition of a written work; see allowed units constraint for valid values to use for units in conjunction with a number

Q17012 - Guadeloupe: overseas region and department of France, and archipelago in the Caribbean

Q135915087 - 97: song written and composed by Thomas Stenström, Isak Friberg and Johannes Runemark; originally recorded by Thomas Stenström and released in 2023

Q135915086 - 97: vocal track by Thomas Stenström; 2023 studio recording

Metro_AG

Q1838 - terbium: element with the atomic number of 65

Q254285 - 65daysofstatic: English experimental rock band

Q28270 - Ham: chimpanzee launched into outer space

Metro_AG

Metro_AG

P355 - child organization/unit: child organization/unit or subsidiary of a company or organization/unit; generally a fully owned separate corporation. Compare with "business division" (P199). Opposite of parent organization/unit (P749).

P749 - parent organization: parent organization of an organization, opposite of subsidiaries (P355)

Metro_AG

Q758603 - Auchan: French multinational retail company

Q110053013 - Auchan Retail Russia: chain of stores in Russia, a subsidiary of French retail

Q34248502 - Auchanitis (Pauly-Wissowa): encyclopedic article in Paulys Realencyclopädie der classischen Altertumswissenschaft (RE)

Q125868206 - Auchan: group of bus stops in Olsztyn, Poland urban area

Metro_AG

Q95449 - Magnitogorsk: city in Chelyabinsk Oblast, Russia

Q2091629 - magnitude: mathematical concept related to comparison and ordering

Q120812 - absolute value: nonnegative number with the same magnitude as a given real number

Q2333469 - Magnitogorsk State Technical University: University in Magnitogorsk

Metro_AG

Metro_AG

Q649 - Moscow: capital and most populous city of Russia

Q6760 - UTC+03:00: identifier for a time offset from UTC of +3

Q215539 - Moscow Conservatory: higher musical education institution in Moscow

Q13164 - Moscow State University: public research university in Moscow, Russia

Metro_AG

Q656 - Saint Petersburg: federal city and former capital of Russia

Q49236 - St. Petersburg: city in Pinellas County, Florida, United States

Q27621 - Saint Petersburg State University: Russian federal state-owned higher education institution

Q628254 - Imperial Academy of Arts: art school in Saint Petersburg, Russia

Q178416 - Saint Petersburg Conservatory: music school in Saint Petersburg

Metro_AG

Q891 - Nizhny Novgorod: capital of the Nizhny Novgorod Oblast and the Volga Federal District in central Russia

Q2246 - Nizhny Novgorod Oblast: federal subject of Russia

Q536319 - Nizhny Novgorod Governorate: governorate of the Russian Empire

Q4318652 - Nizhny Novgorod State Technical University: technical university in Nizhny Novgorod, Russia

Metro_AG

Q908 - Rostov-on-Don: city and administrative center of Rostov Oblast in southern Russia

Q16534881 - Platov International Airport: airport in Russia

Q1432638 - Rostov-on-Don Airport: Defunct airport in Russia

Q99655665 - Rostov-on-Don Wiki-Conference 2010: None

Metro_AG

Q204344 - World Food Programme: United Nations branch related to food-assistance

Q7888965 - United Nations World Food Programme in Myanmar: None

Metro_AG

Q212 - Ukraine: country in Eastern Europe

Q110999040 - Russian invasion of Ukraine: ongoing military conflict in Eastern Europe since 2022

Q170403 - Ukraine men's national football team: men's national association football team representing Ukraine

Q2296961 - Ukraina Stadium: multi-purpose stadium in Lviv, Ukraine

Q4220180 - Kievskaia starina: ethographic and literary chronicle

Metro_AG

Q114449712 - War in Ukraine Disrupts Ships Around the Globe: Wall Street Journal article (March 1, 2022)

Q115253268 - War in Ukraine: Making Sense of a Senseless Conflict: book by Benjamin and Davies

Q113849991 - War in Ukraine: scientific article published in 2022

Q116852592 - War in Ukraine poses stark choices for scientists: scientific article published on 4 March 2022

Metro_AG

Q27449963 - EP Infrastructure: company in the Czech Republic

Metro_AG

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates

Output: None of the candidates


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.

[
    "Q169167 - METRO AG: German wholesale company",
    "Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela",
    "Q8191806 - Agrícola Oriental: metro station in Mexico City",
    "Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article"
]

[
    "Q35 - Denmark: country in Northern Europe and North America",
    "Q756617 - Kingdom of Denmark: transcontinental sovereign state and constitutional monarchy",
    "Q37044634 - Denmark: family name",
    "Q106500037 - Denmark: given name",
    "Q671384 - Statens Museum for Kunst: art museum in Copenhagen, Denmark"
]

[
    "Q79 - Egypt: country in Northeast Africa and Southwest Asia",
    "Q132659 - Egyptian hieroglyphs: formal writing system used by the ancient Egyptians",
    "Q29919 - Egyptian Arabic: Arabic dialect spoken in Egypt",
    "Q11768 - Ancient Egypt: Egyptian civilization from the 31st century BC to the 1st century BC",
    "Q194445 - Cairo University: public university with its main campus in Giza, Egypt"
]

[
    "Q1995 - 2010: calendar year",
    "Q851662 - 2010: natural number",
    "Q523716 - 2010 United States Census: national census of the United States taken in 2010",
    "Q92953148 - 2010 United States Census surname index: table of surnames with 100 or more occurrences in the 2010 US census",
    "Q107569809 - 2010 U.S. Gazetteer Files: geographical census data for regions in the United States"
]

[
    "Q704606 - Makro: Dutch brand of supermarket chains",
    "Q3842164 - Makrobiusz: male given name",
    "Q18024604 - makro: None",
    "Q10323264 - Makro: None",
    "Q62246 - polycarbonate: family of polymers"
]

[
    "Q31 - Belgium: country in western Europe",
    "Q106879226 - Belgium: family name",
    "Q166776 - Belgium men's national football team: men's national association football team representing Belgium",
    "Q2025327 - Belgium: village in Ozaukee County, Wisconsin",
    "Q2281631 - Belgium: village in Illinois, United States"
]

[
    "Q881 - Vietnam: country in Southeast Asia",
    "Q9199 - Vietnamese: Austroasiatic language originating in Vietnam",
    "Q200180 - Vietnamese Wikipedia: Vietnamese-language edition of Wikipedia",
    "Q8740 - Vietnam War: armed conflict in Vietnam, Laos, and Cambodia between North Vietnam and South Vietnam, from 1955 to 1975",
    "Q430309 - Dai Viet: Vietnamese monarchy (10th–19th century)"
]

[
    "Q704606 - Makro: Dutch brand of supermarket chains",
    "Q3842164 - Makrobiusz: male given name",
    "Q18024604 - makro: None",
    "Q10323264 - Makro: None",
    "Q62246 - polycarbonate: family of polymers"
]

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

[
    "Q7311028 - Reliance Retail: Indian retail company"
]

[
    "Q7536037 - Sklavenitis: Greek supermarket chain"
]

[
    "Q17 - Japan: island country in East Asia",
    "Q13989 - Honshu: largest island of Japan",
    "Q5287 - Japanese: language spoken in East Asia",
    "Q188712 - Empire of Japan: Empire in the Asia-Pacific region from 1868 to 1947",
    "Q205662 - Tokugawa shogunate: 1603–1868 Japanese military government"
]


Former operations

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, 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.

Metro_AG

Q704606 - Makro: Dutch brand of supermarket chains

Q62246 - polycarbonate: family of polymers

Metro_AG

Q28170134 - Metro India: newspaper in India

Metro_AG

Metro_AG

Metro_AG

Metro_AG

Metro_AG

Metro_AG

Q3307433 - Metro: British free daily newspaper

Metro_AG

Metro_AG

Metro_AG

Q7536037 - Sklavenitis: Greek supermarket chain

Metro_AG

Metro_AG

Q7311028 - Reliance Retail: Indian retail company

Metro_AG

Output: None of the candidates


Metro name

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 name: In 2012, in the face of an impending brand conflict,Microsoftrenamed its Metro user interface of theMicrosoft Windows 8computer operating system (toMicrosoft Design Language, or MDL).

[
    "Q2283 - Microsoft: American multinational technology corporation",
    "Q1406 - Microsoft Windows: family of computer operating systems developed by Microsoft",
    "Q135288 - Microsoft Store: digital distribution platform operated by Microsoft",
    "Q132020 - Xbox: video game console by Microsoft",
    "Q11215 - Windows 7: personal computer operating system by Microsoft that was released in 2009"
]

[
    "Q169167 - METRO AG: German wholesale company",
    "Q11283062 - Metro Agua Salud: metro station in Caracas, Venezuela",
    "Q8191806 - Agrícola Oriental: metro station in Mexico City",
    "Q128289364 - Metro AG — Konzernabschluss zum 30.9.2018: scholarly article"
]

[
    "Q5046 - Windows 8: personal computer operating system by Microsoft that was released in 2012",
    "Q3569290 - Windows 8.1: personal computer operating system by Microsoft that was released in 2013"
]

[
    "Q1990 - 2012: year",
    "Q1489579 - 2012: natural number",
    "Q8577 - 2012 Summer Olympics: Games of the XXX Olympiad, in London, United Kingdom",
    "Q29033603 - 2012: 2016 EP by Chixdiggit",
    "Q184605 - 2012: 2009 film directed by Roland Emmerich"
]

[
    "Q1148750 - Metro: internal code name of a typography-based design language",
    "Q61159562 - Microsoft Design Language 2: successor of Microsoft Design Language"
]


Metro name

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 name: In 2012, in the face of an impending brand conflict,Microsoftrenamed its Metro user interface of theMicrosoft Windows 8computer operating system (toMicrosoft Design Language, or MDL).

Metro_(design_language)

Q8577 - 2012 Summer Olympics: Games of the XXX Olympiad, in London, United Kingdom

Q184605 - 2012: 2009 film directed by Roland Emmerich

Metro_(design_language)

P1476 - title: published name of a work, such as a newspaper article, a literary work, piece of music, a website, or a performance work

P793 - significant event: significant or notable events associated with the subject

P2561 - name: name the subject is known by; preferably use a more specific subproperty if available

P800 - notable work: notable scientific, artistic or literary work, or other work of significance among subject's works

P1317 - floruit: date when the person was known to be active or alive, when birth or death not documented

Q5046 - Windows 8: personal computer operating system by Microsoft that was released in 2012

Q3569290 - Windows 8.1: personal computer operating system by Microsoft that was released in 2013

Output: None of the candidates