Adidas

Accessories

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, Accessories: Adidas also designs and makesslide-style sandals, mobile accessories,watches,eyewear, bags,baseball caps, andsocks. As well, Adidas has a branded range of male and femaledeodorants,perfumes,aftershaveandlotions. Adidas announced they would be launching a new $199 Fit Smart wristband in mid-August 2014. The wristband will pair with Adidas's miCoach app, which acts as a personal trainer. Adilettewas the first ever pair ofsandalsmade by Adidas, originally developed in 1963.Adidas claims that a group of athletes approachedAdi Dasslerrequesting a shoe be made for the locker room. To this day, the resulting sandals are a best-seller.Since the original navy blue and white Adilette sandals were created nearly fifty years ago, more varieties have been created in different colours (black, red, green, grey, orange, brown, yellow, pink, golden, silver). Most recently, Adidas has introduced a colour scheme that goes along with its Predator and adizero line; the scheme is dubbed warning (orange) and purple. Usually, the three stripes appear in the contrasting colour on the strap of the classic models. The most common Adilette livery is in navy blue or black, mixed with white colours. Also the Woodilette and Trefoil models follow a similar design but without stripes on the strap. The model provides a contoured orthopedic rubber sole with synthetic upper, and was designed as an after sportslide sandal, but the Adilette were quickly adopted outside of the sporting world. Adissageis also a slide sandal. Available in black, navy, light blue, black with pink, and other assorted colors, the sandal has the trademarked three stripes on avelcrostrap toward the front of the shoe. On the side of the shoe, toward the heel on either side, the manufacturers name appears, as well as on a round emblem in the actual heel of the footbed. Notably, there are tiny black massage nubs throughout the foot-bed for the purpose of massaging foot aches after sport, although popular as a casual sandal amongst non-athletes as well. TheSantiossageis aslide-style sandal. The sandal has the trademarked three stripes on a velcro strap toward the front of the shoe.Santiossagecomes in black, navy, or red. On the side of the shoe, toward the heel on either side, the manufacturer's name appears, as well as on a round emblem in the actual heel of the foot-bed. Like the Adissage there are tiny clear massage nubs throughout the foot-bed for the purpose of massaging after-sport footaches, although the sandals are worn casually among non-athletes. Seen through these clear nubs are Adidas's three stripes.

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Adidas

Accessories

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, Accessories: Adidas also designs and makesslide-style sandals, mobile accessories,watches,eyewear, bags,baseball caps, andsocks. As well, Adidas has a branded range of male and femaledeodorants,perfumes,aftershaveandlotions. Adidas announced they would be launching a new $199 Fit Smart wristband in mid-August 2014. The wristband will pair with Adidas's miCoach app, which acts as a personal trainer. Adilettewas the first ever pair ofsandalsmade by Adidas, originally developed in 1963.Adidas claims that a group of athletes approachedAdi Dasslerrequesting a shoe be made for the locker room. To this day, the resulting sandals are a best-seller.Since the original navy blue and white Adilette sandals were created nearly fifty years ago, more varieties have been created in different colours (black, red, green, grey, orange, brown, yellow, pink, golden, silver). Most recently, Adidas has introduced a colour scheme that goes along with its Predator and adizero line; the scheme is dubbed warning (orange) and purple. Usually, the three stripes appear in the contrasting colour on the strap of the classic models. The most common Adilette livery is in navy blue or black, mixed with white colours. Also the Woodilette and Trefoil models follow a similar design but without stripes on the strap. The model provides a contoured orthopedic rubber sole with synthetic upper, and was designed as an after sportslide sandal, but the Adilette were quickly adopted outside of the sporting world. Adissageis also a slide sandal. Available in black, navy, light blue, black with pink, and other assorted colors, the sandal has the trademarked three stripes on avelcrostrap toward the front of the shoe. On the side of the shoe, toward the heel on either side, the manufacturers name appears, as well as on a round emblem in the actual heel of the footbed. Notably, there are tiny black massage nubs throughout the foot-bed for the purpose of massaging foot aches after sport, although popular as a casual sandal amongst non-athletes as well. TheSantiossageis aslide-style sandal. The sandal has the trademarked three stripes on a velcro strap toward the front of the shoe.Santiossagecomes in black, navy, or red. On the side of the shoe, toward the heel on either side, the manufacturer's name appears, as well as on a round emblem in the actual heel of the foot-bed. Like the Adissage there are tiny clear massage nubs throughout the foot-bed for the purpose of massaging after-sport footaches, although the sandals are worn casually among non-athletes. Seen through these clear nubs are Adidas's three stripes.

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'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 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'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 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'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': 'Adilette', 'range': 'Athlete'}}, {'domain': '


Adidas

Marketing

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: During the mid to late 1990s, Adidas divided the brand into three main groups with each a separate focus:Adidas Performancewas designed to maintain their devotion to the athlete;Adidas Originalswas designed to focus on the brand's earlier designs which remained a popular life-style icon; andStyle Essentials, which dealt with the fashion market; the main group within this beingY-3(which is a collaboration between Adidas and renowned Japanese fashion designerYohji Yamamoto- the Y representing Yamamoto and the 3 representing the three stripes of Adidas). Launched in 2004,"Impossible is Nothing"is one of the company's most memorable campaigns.The campaign was developed by 180/TBWA based in Amsterdam, but significant work was also done byTBWAChiatDayin San Francisco.A few years later, Adidas launched a basketball specific campaign -- "Believe in 5ive"—for the 2006-2007 NBA season. In 2011,"Adidas is all in"became the global marketing strategy slogan for Adidas. The slogan aimed to tie all brands and labels together, presenting a unified image to consumers interested in sports, fashion, street, music and pop culture. There appears to be connection with the phrase "all-in" meaning "exhausted" in some English speaking nations. In 2015, Adidas launched "Creating the New" as its strategic business plan until 2020.

[["Adidas", "Company"], ["Impossible_is_Nothing", "Marketing"], ["180/TBWA", "Company"], ["Adidas", "Product"], ["Y-3", "Product"], ["Yohji_Yamamoto", "Person"], ["Believe_in_5ive", "Marketing"], ["Creating_the_New", "Marketing"], ["Adidas_Originals", "Product"], ["\"All in\"", "Marketing"], ["Adidas", "Product"], ["\"All in\"", "string"], ["2004", "Year"], ["San_Francisco", "City"], ["Adidas", "Company"], ["\"Creating the New\"", "string"], ["2015", "Year"], ["Adidas", "Company"], ["Adidas_Performance", "Product"], ["\"Impossible is Nothing\"", "string"], ["2006–2007_NBA_season", "date"], ["Adidas", "Company"], ["\"Adidas is all in\"", "string"]]


Adidas

Marketing

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: During the mid to late 1990s, Adidas divided the brand into three main groups with each a separate focus:Adidas Performancewas designed to maintain their devotion to the athlete;Adidas Originalswas designed to focus on the brand's earlier designs which remained a popular life-style icon; andStyle Essentials, which dealt with the fashion market; the main group within this beingY-3(which is a collaboration between Adidas and renowned Japanese fashion designerYohji Yamamoto- the Y representing Yamamoto and the 3 representing the three stripes of Adidas). Launched in 2004,"Impossible is Nothing"is one of the company's most memorable campaigns.The campaign was developed by 180/TBWA based in Amsterdam, but significant work was also done byTBWAChiatDayin San Francisco.A few years later, Adidas launched a basketball specific campaign -- "Believe in 5ive"—for the 2006-2007 NBA season. In 2011,"Adidas is all in"became the global marketing strategy slogan for Adidas. The slogan aimed to tie all brands and labels together, presenting a unified image to consumers interested in sports, fashion, street, music and pop culture. There appears to be connection with the phrase "all-in" meaning "exhausted" in some English speaking nations. In 2015, Adidas launched "Creating the New" as its strategic business plan until 2020.

[["Adidas", "marketing", "\"Creating the New\""], ["Adidas", "marketing", "\"Adidas is all in\""], ["Adidas", "marketing", "\"Impossible is Nothing\""], ["Adidas", "marketing", "\"Believe in 5ive\""], ["Adidas", "marketing", "\"Adidas Performance\", \"Adidas Originals\", \"Y-3\""], ["Adidas", "marketing", "\"180/TBWA, Amsterdam\""], ["Adidas", "marketing", "\"TBWA Chiat Day, San Francisco\""]]


Adidas

Collaborations

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, Collaborations: Adidas has done several collaborations with well known designers, includingAlexander Wang,Jeremy Scott,Raf Simons, andStella McCartney. They have also reached out to several celebrities, such asBeyoncé,Jonah Hill,Karlie Kloss,Ninja,Bad Bunny, andPharrell Williamsto create some of the company's most notable and coveted pieces.In 2025, they collaborated withbritpopbandOasisfor a collection called 'Original Forever' which commemorated the band's legacy with the brand since the 90s.

[["Adidas", "Company"], ["Oasis_(band)", "Person"], ["Adidas_Oasis_Original_Forever", "Product"], ["Alexander_Wang", "Person"], ["Jeremy_Scott", "Person"], ["Raf_Simons", "Person"], ["Stella_McCartney", "Person"], ["Beyoncé", "Person"], ["Jonah_Hill", "Person"], ["Karlie_Kloss", "Person"], ["Ninja_(rapper)", "Person"], ["Bad_Bunny", "Person"], ["Pharrell_Williams", "Person"]]


Adidas

Collaborations

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, Collaborations: Adidas has done several collaborations with well known designers, includingAlexander Wang,Jeremy Scott,Raf Simons, andStella McCartney. They have also reached out to several celebrities, such asBeyoncé,Jonah Hill,Karlie Kloss,Ninja,Bad Bunny, andPharrell Williamsto create some of the company's most notable and coveted pieces.In 2025, they collaborated withbritpopbandOasisfor a collection called 'Original Forever' which commemorated the band's legacy with the brand since the 90s.

[["Adidas", "collaborated_with", "Oasis_(band)"], ["Adidas", "collaborated_with", "Alexander_Wang"], ["Adidas", "collaborated_with", "Jeremy_Scott"], ["Adidas", "collaborated_with", "Raf_Simons"], ["Adidas", "collaborated_with", "Stella_McCartney"], ["Adidas", "collaborated_with", "Beyoncé"], ["Adidas", "collaborated_with", "Jonah_Hill"], ["Adidas", "collaborated_with", "Karlie_Kloss"], ["Adidas", "collaborated_with", "Ninja_(rapper)"], ["Adidas", "collaborated_with", "Bad_Bunny"], ["Adidas", "collaborated_with", "Pharrell_Williams"], ["Adidas", "collaborated_with", "\"Original Forever\""], ["Adidas", "collaborated_with", "Oasis_(band)", "in_year": "2025"]]


Adidas

Game advertisement

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, Game advertisement: The brand is featured in several games, includingDaley Thompson's Olympic Challenge(Commodore Amiga),Adidas power soccer(Sony PlayStation) andAdidas Championship Football(Commodore 64,ZX spectrum,Amstrad CPC).

[["Adidas", "Company"], ["Daley_Thompson's_Olympic_Challenge", "Game"], ["Adidas_Power_Soccer", "Game"], ["Adidas_Championship_Football", "Game"], ["Adidas_Championship_Football", "Company"], ["Commodore_64", "Platform"], ["Sony_PlayStation", "Platform"], ["Amstrad_CPC", "Platform"]]


Adidas

Game advertisement

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, Game advertisement: The brand is featured in several games, includingDaley Thompson's Olympic Challenge(Commodore Amiga),Adidas power soccer(Sony PlayStation) andAdidas Championship Football(Commodore 64,ZX spectrum,Amstrad CPC).

[["Adidas", "is_featured_in", "Daley_Thompson's_Olympic_Challenge"], ["Daley_Thompson's_Olympic_Challenge", "platform", "Commodore_Amiga"], ["Adidas", "is_featured_in", "Adidas_Power_Soccer"], ["Adidas_Power_Soccer", "platform", "Sony_PlayStation"], ["Adidas", "is_featured_in", "Adidas_Championship_Football"], ["Adidas_Championship_Football", "platform", "Commodore_64"], ["Adidas_Championship_Football", "platform", "ZX_Spectrum"], ["Adidas_Championship_Football", "platform", "Amstrad_CPC"]]


Adidas

Marketing in India

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 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", "Company"], ["India", "Place"], ["Myntra", "Company"], ["Virat_Kohli", "Person"], ["\"Feel Love Use Hate\"", "string"], ["Dave_Thomas", "Person"], ["805.0 (crore rupees)", "number"], ["2015", "Year"], ["Myntra", "Product"], ["Snapdeal", "Company"], ["Jabong", "Company"], ["Amazon_(company)", "Company"], ["India", "Company"], ["\"India is a very speculative market\"", "string"], ["2017", "Year"], ["Virat_Kohli", "Person"], ["Puma_(company)", "Company"]]