Adidas

Marketing in India

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: Adidas, Marketing in India: India has been a very speculative market for Adidas.[why?]Despite this Dave Thomas, managing director of Adidas in India is ambitious for the country's potential.The company hoped to double its revenue from Rs. 805 crores by 2020.In 2015, the company signedRanveer Singha Bollywood actor as a brand ambassador to the company's products.Singhthen was a budding actor. The company later decided to use the Indian people's passion for the game of cricket to promote their brand and launched a new cricket campaign in the country.The campaign was called FeelLoveUseHate with Indian cricketer Virat Kohli.However, in 2017, Virat Kohli was removed as the brand ambassador of the company.The cricketer later signed a major deal with Puma India.The company also sells its products online through e-commerce websites such as Myntra, Snapdeal, Jabong and Amazon. Adidas also has a website dedicated to the Indian audience that markets and sells products to its consumers in India.

[["Adidas", "has_product_line", "Cricket"], ["Adidas", "has_product_line", "Football"], ["Adidas", "has_product_line", "Athletics"], ["Adidas", "has_product_line", "Rugby"], ["Adidas", "has_product_line", "Basketball"], ["Adidas", "has_product_line", "Volleyball"], ["Adidas", "has_product_line", "Hockey"], ["Adidas", "has_product_line", "Golf"], ["Adidas", "has_product_line", "Soccer"], ["Adidas", "has_product_line", "Skateboarding"], ["Adidas", "has_product_line", "Surfing"], ["Adidas", "has_product_line", "Tennis"], ["Adidas", "has_product_line", "American_football"], ["Adidas", "has_product_line", "Baseball"], ["Adidas", "has_product_line", "Softball"], ["Adidas", "has_product_line", "Ice_hockey"], ["Adidas", "has_product_line", "Futsal"], ["Adidas", "has_product_line", "Wrestling"], ["Adidas", "has_product_line", "Beach_volleyball"], ["Adidas", "has_product_line", "Handball"], ["Adidas", "has_product_line", "Floorball"], ["Adidas", "has_product_line", "Kabaddi"], ["Adidas", "has_product_line", "Ice_skating"], ["Adidas", "has_product_line", "Judo"], ["Adidas", "has_product_line", "Aerobics"], ["Adidas", "has_product_line", "Badminton"], ["Adidas", "has_product_line", "Basketball"], ["Adidas", "has_product_line", "Squash"], ["Adidas", "has_product_line", "Ping_pong"], ["Adidas", "has_product_line", "Swimming"], ["Adidas", "has_product_line", "Taekwondo"], ["Adidas", "has_product_line", "Tennis"], ["Adidas", "has_product_line", "Track_and_field"], ["Adidas", "has_product_line", "Volleyball"], ["Adidas", "has_product_line", "Water_polo"], ["Adidas", "has_product_line", "Weightlifting"], ["Adidas", "has_product_line", "Wrestling"], ["Adidas", "has_product_line", "Football"], ["Adidas", "has_product_line", "Team_gyroscope"], ["Adidas", "has_product_line", "Batting_practice"], ["Adidas", "has_product_line", "Cricket"], ["Adidas", "has_product_line", "Golf"], ["Adidas", "has_product_line", "American_football"], ["Adidas", "has_product_line", "Softball"], ["Adidas", "has_product_line", "Ice_hockey"], ["Adidas", "has_product_line", "Futsal"], ["Adidas", "has_product_line", "Beach_volleyball"], ["Adidas", "has_product_line", "Handball"], ["Adidas", "has_product_line", "Floorball"], ["Adidas", "has_product_line", "Kabaddi"], ["Adidas", "has_product_line", "Ice_skating"], ["Adidas", "has_product_line", "Judo"], ["Adidas", "has_product_line", "Aerobics"], ["Adidas", "has_product_line", "Badminton"], ["Adidas", "has_product_line", "Squash"], ["Adidas", "has_product_line", "Ping_pong"], ["Adidas", "has_product_line", "Swimming"], ["Adidas", "has_product_line", "Taekwondo"], ["Adidas", "has_product_line", "Tennis"], ["Adidas", "has_product_line", "Track_and_field"], ["Adidas", "has_product_line", "Volleyball"], ["Adidas", "has_product_line", "Water_polo"], ["Adidas", "has_product_line", "Weightlifting"], ["Adidas", "has_product_line", "Wrestling"], ["Adidas", "has_product_line", "Football"], ["Adidas", "has_product_line", "Team_gyroscope"], ["Adidas", "has_product_line", "Batting_practice"], ["Adidas", "has_product_line", "Cricket"], ["Adidas", "has_product_line", "Golf"], ["Adidas", "has_product_line", "American_football"], ["Adidas", "has_product_line", "Softball"], ["Adidas", "has_product_line", "Ice_hockey"], ["Adidas", "has_product_line", "Futsal"], ["Adidas", "has_product_line", "Beach_volleyball"], ["Adidas", "has_product_line", "Handball"], ["Adidas", "has_product_line", "Floorball"], ["Adidas", "has_product_line", "Kabaddi"], ["Adidas", "has_product_line", "Ice_skating"], ["Adidas", "has_product_line", "Judo"], ["Adidas", "has_product_line", "Aerobics"], ["Adidas", "has_product_line", "Badminton"], ["Adidas", "has_product_line", "Squash"], ["Adidas", "has_product_line", "Ping_pong"], ["Adidas", "has_product_line", "Swimming"], ["Adidas", "has_product_line", "Taekwondo"], ["Adidas", "has_product_line", "Tennis"], ["Adidas", "has_product_line", "Track_and_field"], ["Adidas", "has_product_line", "Volleyball"], ["Adidas", "has_product_line", "Water_polo"], ["Adidas", "has_product_line", "Weightlifting"], ["Adidas", "has_product_line", "Wrestling"], ["Adidas", "has_stakeholder", "Virat_Kohli"], ["Adidas", "has_stakeholder", "Ranveer_Singh"], ["Adidas", "has_stakeholder", "Dave_Thomas"], ["Adidas", "has_stakeholder", "Puma"], ["Adidas", "has_stakeholder", "Myntra"], ["Adidas", "has_stakeholder", "Snapdeal"], ["Adidas", "has_stakeholder", "Jabong"], ["Adidas", "has_stakeholder", "Amazon_(company)"], ["Adidas", "has_stakeholder", "India"], ["Adidas", "has_stakeholder", "805.0"], ["Adidas", "has_stakeholder", "2020"], ["Adidas", "has_stakeholder", "FeelLoveUseHate"], ["Adidas", "has_stakeholder", "Cricket"]]


Adidas

Marketing in Russia

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: Adidas, Marketing in Russia: Adidas provided outfits for the Soviet Union's team in the1980 Summer Olympics, making them one of the first global brands to enter the territory.TheCommunist Partyprohibited the usage of the company logo on Soviet tracksuits, changing the design from three stripes to one red stripe. The shoes remained unchanged, as the three stripes resembled an "M", hinting that the Olympics were held inMoscow. The brand later became a staple of theGopniksubculture.

[["Adidas", "Company"], ["Soviet_Union", "Company"], ["1980_Summer_Olympics", "Date"], ["Gopnik_subculture", "Company"], ["Adidas", "Product"], ["\"M\"", "string"], ["Moscow", "City"], ["Soviet_Union", "Country"], ["Communist_Party_(Soviet_Union)", "Organization"]]


Adidas

Marketing in Russia

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: Adidas, Marketing in Russia: Adidas provided outfits for the Soviet Union's team in the1980 Summer Olympics, making them one of the first global brands to enter the territory.TheCommunist Partyprohibited the usage of the company logo on Soviet tracksuits, changing the design from three stripes to one red stripe. The shoes remained unchanged, as the three stripes resembled an "M", hinting that the Olympics were held inMoscow. The brand later became a staple of theGopniksubculture.

[["Adidas", "has_product_line", "Soviet_Olympic_team_tracksuits"], ["Adidas", "is_known_for", "Gopnik_subculture"], ["Adidas", "marketing", "Soviet_Union"], ["Soviet_Olympic_team_tracksuits", "has_product_line", "Adidas"], ["Soviet_Olympic_team_tracksuits", "has_trademark", "\"One red stripe\""], ["Soviet_Olympic_team_tracksuits", "has_trademark", "\"M\""]]


Adidas

Sponsorship

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: Adidas, Sponsorship: Adidas has numerous major kit deals with football clubs worldwide, including their main sponsorBayern Munich.Moreover, their sponsored national teams includeGermany,Spain,Mexico,Argentina,Sweden,Japan,Hungary,Belgium,Colombia,Wales,Chile,Italy,Costa Rica,Colombia,Venezuela,Iceland,Peru. Adidas has sponsored numerous players, includingLionel Messi,Zinedine Zidane,Kaká,David Beckham,Steven Gerrard,Gareth Bale,Thomas Müller,Xavi,James Rodríguez,Iker Casillas,Arjen Robben,Paul Pogba,Luis Suárez,George Weah,Alessandro Del Piero,Son Heung-min,Mohamed Salah,Jude Bellingham,Luis Diaz,Alexander Isak,Darwin Nunez,Rio Ngumoha,Declan Rice,Raphinha,Mikel Merino,Riccardo Calafiori,Alexis Mac Allisteretc. Adidas is one of the official sponsors of theUEFA Champions League, and theAdidas Finaleis the competition's official match ball.Along with theAdidas Predatorboot, Adidas manufactures theadiPurerange of football boots. Adidas provides clothing and equipment for all teams inMajor League Soccer(MLS). In July 2014, Adidas andManchester Unitedagreed to a ten-year kit deal, beginning with the2015–16 Premier League season. This kit deal has a guaranteed minimum value of £750 million (US$1.29 billion), making it the most valuable kit deal in sports history, and replaced rivalNikeas the club's global equipment partner. In July 2019, Adidas andArsenalagreed to a five-year kit deal, beginning with the 2019–2 Premier League season. This kit deal has a guaranteed minimum value of £300 million (US$406.41 million) and replaced rivalPumaas the club's global equipment partner. In August 2025, Adidas andLiverpoolagreed to a ten-year kit deal, beginning with the2025–26 Premier Leagueseason. This kit deal has a guaranteed minimum value of £60 million (US$81.28 million) and replaced rivalNikeas the club's global equipment partner. In November 2009, World Number 4 tennis playerAndy Murraywas confirmed as Adidas's highest-paid star with a five-year contract reportedly worth US$24.5 million.InCincinnati, at theATP Tennis Tournamentin Mason, they have also sponsored theball-boy and ball-girluniforms. Adidas is also partners with Malibu Tennis Camp, Green Fitness GmbH and with Schöler & Micke Sportartikel Vertriebs GmbH. Adidas has sponsored numerous basketball players likeKareem Abdul-Jabbar(first ever NBA player signed with Adidas and first ever player overall with signature shoes),Chauncey Billups,Tim Duncan,Brandon Knight,Jeremy Lin,Tracy McGrady,Iman Shumpertand present players likeJames Harden,Damian Lillard,Donovan Mitchell,Candace Parker,Derrick Rose,John WallandTrae Young. Adidas also endorsedKobe Bryantwith the Adidas Equipment KB8 as his first signature shoe until July 2002.The company also endorsedKevin Garnettuntil he opted out of his contract in 2010.Gilbert Arenaswas an Adidas endorser until 2010.In August 2015,James Hardenleft Nike for Adidas by signing a 13-year contract reportedly worth US$200 million. Inrugby union, Adidas is the current kit supplier to theAll Blacks, theFrance national team, theItalian national rugby teamand the South AfricanStormersandWestern Provincerugby union teams among others. Adidas is also theNew Zealand Rugby Unionclothing sponsor and supplies clothing to allSuper Rugbyfranchises up till 2022,a selection of domestic teams and national referees. Adidas are also the official match ball supplier to theHeineken Cup. Adidas was theBritish and Irish Lionskit supplier from 1997 to 2013. They are the jersey manufacturers of theGold Coast TitansRugby League club in the AustralasianNational Rugby League. Dual rugby and league international and former boxerSonny Bill Williamsis a global ambassador for Adidas. Adidas has providedfield hockeyequipment and sponsors numerous players of Germany, England, Netherlands, Australia, Spain and Belgium. The company has been the kit provider of Argentinewomen'sandmen'steams for over 15 years.The company also sponsored clubsReading,BeestonandEast Grinstead. Adidas also sponsorsprofessional golfersincludingCollin Morikawa,Ludvig Åberg,Nick Dunlap,Tyrrell Hatton,Daniel BergerandNick Taylor. Since Adidas does not make golf equipment the sponsorship is more limited to clothing and shoes. Inice hockey, Adidas signed an agreement withNational Hockey League(NHL) to be the official outfitter of uniforms and licensed apparel, starting in the2017–18 season, lasting through the2023–24 season. Adidas'scricketsponsorships include cricketersLasith Malinga,Kieron Pollard,Dwayne BravoandK. L. Rahul. Adidas's volleyball sponsorships includeIvan ZaytsevandEarvin N'Gapeth, who advertises as a model and brand ambassador for Adidas.

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"Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", 'Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas", "Athlete"], ["Adidas", "Club"], ["Adidas


Adidas

Sponsorship

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: Adidas, Sponsorship: Adidas has numerous major kit deals with football clubs worldwide, including their main sponsorBayern Munich.Moreover, their sponsored national teams includeGermany,Spain,Mexico,Argentina,Sweden,Japan,Hungary,Belgium,Colombia,Wales,Chile,Italy,Costa Rica,Colombia,Venezuela,Iceland,Peru. Adidas has sponsored numerous players, includingLionel Messi,Zinedine Zidane,Kaká,David Beckham,Steven Gerrard,Gareth Bale,Thomas Müller,Xavi,James Rodríguez,Iker Casillas,Arjen Robben,Paul Pogba,Luis Suárez,George Weah,Alessandro Del Piero,Son Heung-min,Mohamed Salah,Jude Bellingham,Luis Diaz,Alexander Isak,Darwin Nunez,Rio Ngumoha,Declan Rice,Raphinha,Mikel Merino,Riccardo Calafiori,Alexis Mac Allisteretc. Adidas is one of the official sponsors of theUEFA Champions League, and theAdidas Finaleis the competition's official match ball.Along with theAdidas Predatorboot, Adidas manufactures theadiPurerange of football boots. Adidas provides clothing and equipment for all teams inMajor League Soccer(MLS). In July 2014, Adidas andManchester Unitedagreed to a ten-year kit deal, beginning with the2015–16 Premier League season. This kit deal has a guaranteed minimum value of £750 million (US$1.29 billion), making it the most valuable kit deal in sports history, and replaced rivalNikeas the club's global equipment partner. In July 2019, Adidas andArsenalagreed to a five-year kit deal, beginning with the 2019–2 Premier League season. This kit deal has a guaranteed minimum value of £300 million (US$406.41 million) and replaced rivalPumaas the club's global equipment partner. In August 2025, Adidas andLiverpoolagreed to a ten-year kit deal, beginning with the2025–26 Premier Leagueseason. This kit deal has a guaranteed minimum value of £60 million (US$81.28 million) and replaced rivalNikeas the club's global equipment partner. In November 2009, World Number 4 tennis playerAndy Murraywas confirmed as Adidas's highest-paid star with a five-year contract reportedly worth US$24.5 million.InCincinnati, at theATP Tennis Tournamentin Mason, they have also sponsored theball-boy and ball-girluniforms. Adidas is also partners with Malibu Tennis Camp, Green Fitness GmbH and with Schöler & Micke Sportartikel Vertriebs GmbH. Adidas has sponsored numerous basketball players likeKareem Abdul-Jabbar(first ever NBA player signed with Adidas and first ever player overall with signature shoes),Chauncey Billups,Tim Duncan,Brandon Knight,Jeremy Lin,Tracy McGrady,Iman Shumpertand present players likeJames Harden,Damian Lillard,Donovan Mitchell,Candace Parker,Derrick Rose,John WallandTrae Young. Adidas also endorsedKobe Bryantwith the Adidas Equipment KB8 as his first signature shoe until July 2002.The company also endorsedKevin Garnettuntil he opted out of his contract in 2010.Gilbert Arenaswas an Adidas endorser until 2010.In August 2015,James Hardenleft Nike for Adidas by signing a 13-year contract reportedly worth US$200 million. Inrugby union, Adidas is the current kit supplier to theAll Blacks, theFrance national team, theItalian national rugby teamand the South AfricanStormersandWestern Provincerugby union teams among others. Adidas is also theNew Zealand Rugby Unionclothing sponsor and supplies clothing to allSuper Rugbyfranchises up till 2022,a selection of domestic teams and national referees. Adidas are also the official match ball supplier to theHeineken Cup. Adidas was theBritish and Irish Lionskit supplier from 1997 to 2013. They are the jersey manufacturers of theGold Coast TitansRugby League club in the AustralasianNational Rugby League. Dual rugby and league international and former boxerSonny Bill Williamsis a global ambassador for Adidas. Adidas has providedfield hockeyequipment and sponsors numerous players of Germany, England, Netherlands, Australia, Spain and Belgium. The company has been the kit provider of Argentinewomen'sandmen'steams for over 15 years.The company also sponsored clubsReading,BeestonandEast Grinstead. Adidas also sponsorsprofessional golfersincludingCollin Morikawa,Ludvig Åberg,Nick Dunlap,Tyrrell Hatton,Daniel BergerandNick Taylor. Since Adidas does not make golf equipment the sponsorship is more limited to clothing and shoes. Inice hockey, Adidas signed an agreement withNational Hockey League(NHL) to be the official outfitter of uniforms and licensed apparel, starting in the2017–18 season, lasting through the2023–24 season. Adidas'scricketsponsorships include cricketersLasith Malinga,Kieron Pollard,Dwayne BravoandK. L. Rahul. Adidas's volleyball sponsorships includeIvan ZaytsevandEarvin N'Gapeth, who advertises as a model and brand ambassador for Adidas.

[["Adidas", "has_sponsored", "Lionel_Messi"], ["Adidas", "has_sponsored", "Zinedine_Zidane"], ["Adidas", "has_sponsored", "Kaká"], ["Adidas", "has_sponsored", "James_Rodríguez"], ["Adidas", "has_sponsored", "Alexis_Mac_Allister"], ["Adidas", "has_sponsored", "Gareth_Bale"], ["Adidas", "has_sponsored", "Steven_Gerrard"], ["Adidas", "has_sponsored", "Luis_Suárez"], ["Adidas", "has_sponsored", "Iker_Casillas"], ["Adidas", "has_sponsored", "Arjen_Robben"], ["Adidas", "has_sponsored", "Paul_Pogba"], ["Adidas", "has_sponsored", "George_Weah"], ["Adidas", "has_sponsored", "Alexandr_Del_Piero"], ["Adidas", "has_sponsored", "Son_Heung-min"], ["Adidas", "has_sponsored", "Mohamed_Salah"], ["Adidas", "has_sponsored", "Jude_Bellingham"], ["Adidas", "has_sponsored", "Luis_Díaz_(footballer, born 1997)"], ["Adidas", "has_sponsored", "Alexander_Isak"], ["Adidas", "has_sponsored", "Darwin_Núñez"], ["Adidas", "has_sponsored", "Rio_Ngumoha"], ["Adidas", "has_sponsored", "Declan_Rice"], ["Adidas", "has_sponsored", "Raphinha"], ["Adidas", "has_sponsored", "Mikel_Merino"], ["Adidas", "has_sponsored", "Riccardo_Calafiori"], ["Adidas", "has_sponsored", "Andy_Murray"], ["Adidas", "has_sponsored", "Kareem_Abdul-Jabbar"], ["Adidas", "has_sponsored", "Chauncey_Billups"], ["Adidas", "has_sponsored", "Tim_Duncan"], ["Adidas", "has_sponsored", "Brandon_Knight"], ["Adidas", "has_sponsored", "Jeremy_Lin"], ["Adidas", "has_sponsored", "Tracy_McGrady"], ["Adidas", "has_sponsored", "Iman_Shumpert"], ["Adidas", "has_sponsored", "James_Harden"], ["Adidas", "has_sponsored", "Damian_Lillard"], ["Adidas", "has_sponsored", "Donovan_Mitchell"], ["Adidas", "has_sponsored", "Candace_Parker"], ["Adidas", "has_sponsored", "Derrick_Rose"], ["Adidas", "has_sponsored", "Gilbert_Arenas"], ["Adidas", "has_sponsored", "Kevin_Garnett"], ["Adidas", "has_sponsored", "John_Wall"], ["Adidas", "has_sponsored", "Kobe_Bryant"], ["Adidas", "has_sponsored", "New_Zealand_Rugby_Union"], ["Adidas", "has_sponsored", "All_Blacks"], ["Adidas", "has_sponsored", "Italy_national_rugby_union_team"], ["Adidas", "has_sponsored", "France_national_rugby_union_team"], ["Adidas", "has_sponsored", "South_Africa_national_rugby_union_team"], ["Adidas", "has_sponsored", "Gold_Coast_Titans"], ["Adidas", "has_sponsored", "Western_Province_rugby_union"], ["Adidas", "has_sponsored", "Malibu_Tennis_Camp"], ["Adidas", "has_sponsored", "Green_Fitness_GmbH"], ["Adidas", "has_sponsored", "Schöler_&_Micke_Sportartikel_Vertriebs_GmbH"], ["Adidas", "has_sponsored", "England_national_field_hockey_team"], ["Adidas", "has_sponsored", "Germany_national_field_hockey_team"], ["Adidas", "has_sponsored", "Spain_national_field_hockey_team"], ["Adidas", "has_sponsored", "Australasia_national_field_hockey_team"], ["Adidas", "has_sponsored", "Belgium_national_field_hockey_team"], ["Adidas", "has_sponsored", "Super_Rugby"], ["Adidas", "has_sponsored", "Heineken_Cup"], ["Adidas", "has_sponsored", "British_and_Irish_Lions"], ["Adidas", "has_sponsored", "Reading_F.C."], ["Adidas", "has_sponsored", "Beeston_F.C."], ["Adidas", "has_sponsored", "East_Grinstead_F.C."], ["Adidas", "has_sponsored", "Argentina_national_football_team"], ["Adidas", "has_sponsored", "Adidas_Finale"], ["Adidas", "has_sponsored", "Adidas_Predator"], ["Adidas", "has_sponsored", "Adidas_Pure"], ["Adidas", "has_sponsored", "Major_League_Soccer"], ["Adidas", "has_sponsored", "Manchester_United_F.C."], ["Adidas", "has_sponsored", "Arsenal_F.C."], ["Adidas", "has_sponsored", "Liverpool_F.C."], ["Adidas", "has_sponsored", "ATP_Tennis_Tournament_in_Cincinnati"], ["Adidas", "has_sponsored", "Sonny_Bill_Williams"], ["Adidas", "has_sponsored", "Australasia_national_rugby_union_team"], ["Adidas", "has_sponsored", "National_Hockey_League"], ["Adidas", "has_sponsored", "Lasith_Malinga"], ["Adidas", "has_sponsored", "Kieron_Pollard"], ["Adidas", "has_sponsored", "Dwayne_Bravo"], ["Adidas", "has_sponsored", "Ivan_Zaytsev"], ["Adidas", "has_sponsored", "Earvin_N'Gapeth"], ["Adidas", "has_sponsored", "K.L._Rahul"]]


Adidas

IP protection

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: Adidas, IP protection: In 2016, Adidas filed lawsuits againstSkechersfor making a duplicateStan Smithdesign and for Adidas replicas such as "Springblade".

[["Adidas", "Company"], ["2016", "Date"], ["Skechers", "Company"], ["Stan_Smith", "Product"], ["\"Springblade\"", "Product"]]


Adidas

IP protection

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: Adidas, IP protection: In 2016, Adidas filed lawsuits againstSkechersfor making a duplicateStan Smithdesign and for Adidas replicas such as "Springblade".

[["Adidas", "acquired", "Stan_St._Smith"], ["Adidas", "in_year", "2016"], ["Adidas", "acquired", "Adidas_replicas"], ["Adidas", "acquired", "Springblade"], ["Adidas", "acquired", "Skechers"]]


Adidas

Criticism

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: Adidas, Criticism: Adidas's business practises/ethicsand commitment to worker welfare have been scrutinised and often criticised.

[["Adidas", "Company"], ["Criticism", "string"], ["\"Adidas's business practises/ethics and commitment to worker welfare have been scrutinised and often criticised\"", "Criticism"]]


Adidas

Criticism

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: Adidas, Criticism: Adidas's business practises/ethicsand commitment to worker welfare have been scrutinised and often criticised.

[["Adidas", "criticism", "\"Adidas's business practises/ethics and commitment to worker welfare have been scrutinised and often criticised.\""]]


Adidas

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: Adidas, 2011 All Blacks replica rugby jersey pricing: Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand-basedAll Blacksfans asked for price cuts and began purchasing the jersey from overseas vendors after it was revealed that the local price of NZ$220 was more than twice the price offered on some websites. Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents. It has been labelled a public relations disaster by leading New Zealand PR firms[which?]and Consumer advocate groups. The largest New Zealand sportswear retailerRebel Sporthas stated[when?]it is angry and is considering selling the All Blacks Jerseys to the general public below cost.

[["Adidas", "Company"], ["2011_All_Blacks_replica_rugby_jersey", "Product"], ["New_Zealand", "Place"], ["220.0", "number"], ["Adidas", "Person"], ["\"Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand based All Blacks fans asked for price cuts and began purchasing the jersey from overseas vendors after it was revealed that the local price of NZ$220 was more than twice the price offered on some websites. Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents. It has been labelled a public relations disaster by leading New Zealand PR firms and Consumer advocate groups. The largest New Zealand sportswear retailer Rebel Sport has stated it is angry and is considering selling the All Blacks Jerseys to the general public below cost.\"", "string"]]


Adidas

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: Adidas, 2011 All Blacks replica rugby jersey pricing: Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand-basedAll Blacksfans asked for price cuts and began purchasing the jersey from overseas vendors after it was revealed that the local price of NZ$220 was more than twice the price offered on some websites. Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents. It has been labelled a public relations disaster by leading New Zealand PR firms[which?]and Consumer advocate groups. The largest New Zealand sportswear retailerRebel Sporthas stated[when?]it is angry and is considering selling the All Blacks Jerseys to the general public below cost.

[["Adidas", "has_product_line", "All_Blacks"], ["Adidas", "has_revenue_of", "220.0"], ["Adidas", "has_revenue_of", "\"Unhappy with the local price of the Adidas replica All Blacks jersey, New Zealand based All Black fans asked for price cuts and began purchasing the jersey from overseas vendors.\""], ["Adidas", "has_revenue_of", "\"Adidas has responded by enforcing cross-border agreements to stop overseas retailers from selling to New Zealand residents.\""], ["Adidas", "has_revenue_of", "\"It has been labelled a public relations disaster by leading New Zealand PR firms and consumer advocate groups.\""], ["Adidas", "has_revenue_of", "\"Rebel Sport, the largest New Zealand sportswear retailer, has stated it is angry and is considering selling the All Blacks Jerseys to the general public below cost.\""]]


Adidas

2012 "shackle" sneakers

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: Adidas, 2012 "shackle" sneakers: On 14 June 2012, Adidas posted on their Facebook page a picture of a pair ofJeremy Scott-designed shoes containing shackles. The picture was of a planned shoe line that Adidas intended to release in July. The photo caused controversy.Jesse Jacksonwas quoted as saying: "The attempt to commercialize and make popular more than 200 years of human degradation, where blacks were considered three-fifths human by our Constitution is offensive, appalling and insensitive".Jackson threatened a boycott, and NBA commissionerDavid Sternwas at one point reportedly contacted in hopes that he would intervene.Shortly after the outcry, the company cancelled the product.

[["Adidas", "Company"], ["Jeremy_Scott", "Person"], ["Shackle", "Product"], ["2012-06-14", "Date"], ["Adidas", "Person"], ["NBA", "Organization"], ["Jesse_Jackson", "Person"], ["200", "number"], ["\"The attempt to commercialize and make popular more than 200 years of human degradation, where blacks were considered three-fifths human by our Constitution is offensive, appalling and insensitive\"", "string"], ["2012-07-01", "Date"], ["Adidas", "Product"], ["Shackle", "Company"]]


Adidas

2012 "shackle" sneakers

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: Adidas, 2012 "shackle" sneakers: On 14 June 2012, Adidas posted on their Facebook page a picture of a pair ofJeremy Scott-designed shoes containing shackles. The picture was of a planned shoe line that Adidas intended to release in July. The photo caused controversy.Jesse Jacksonwas quoted as saying: "The attempt to commercialize and make popular more than 200 years of human degradation, where blacks were considered three-fifths human by our Constitution is offensive, appalling and insensitive".Jackson threatened a boycott, and NBA commissionerDavid Sternwas at one point reportedly contacted in hopes that he would intervene.Shortly after the outcry, the company cancelled the product.

[["Adidas", "launched_campaign", "\"Shackle\" sneakers"], ["Adidas", "occurred_on_date", "\"2012-06-14\""], ["Adidas", "had_revenue_of", "2012"], ["Adidas", "followed_by", "\"Shackle\" sneakers controversy"], ["Adidas", "designer", "Jeremy_Scott"], ["Adidas", "resulted_in", "Adidas_shuttlecock_sneakers"], ["Adidas", "was_a_response_to", "Jesse_Jackson"], ["Adidas", "occurred_in_year", "2012"], ["Adidas", "has_product_line", "Adidas_shuttlecock_sneakers"], ["Adidas", "shares", "Facebook"]]