Fake craft beers

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: Heineken_N, Fake craft beers: InIreland, Heineken briefly marketed "Blasket Blonde" inCounty Kerryfrom March 2015 to September 2016, andBeanntraí Bruin parts ofCounty Corkin August 2016, as locally madecraft beers, from invented breweries.

[["Heineken_N", "Company"], ["County_Cork", "Place"], ["Blasket_Blonde", "Product"], ["\"March 2015 - September 2016\"", "Date"], ["Beanntraí_Bruin", "Product"], ["County_Kerry", "Place"], ["\"August 2016\"", "Date"], ["Craft_beer", "Product"], ["Heineken", "Company"]]


Fake craft beers

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: Heineken_N, Fake craft beers: InIreland, Heineken briefly marketed "Blasket Blonde" inCounty Kerryfrom March 2015 to September 2016, andBeanntraí Bruin parts ofCounty Corkin August 2016, as locally madecraft beers, from invented breweries.

[["Heineken_N", "launched_product", "\"Blasket Blonde\""], ["Heineken_N", "marketed_as", "\"Craft beer\""], ["Heineken_N", "sold_in", "Ireland"], ["Heineken_N", "product", "\"Beanntraí Bruin\""], ["Heineken_N", "production_start_date", "\"March 2015\""], ["Heineken_N", "production_end_date", "\"August 2016\""], ["Heineken_N", "location", "County_Kerry"]]


Investments 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: Heineken_N, Investments in Russia: At the end of March 2022, over a month after Russia started its war in Ukraine, Heineken announced that it was leaving Russia (including with its other brands there, likeAffligem,Amsteletc.), saying that ownership of the Russian subsidiary was no longer “durable or viable.” But despite this promise Heineken hired more than 240 new staff and launched 61 new products on the Russian market in the last year,[clarification needed]investigators fromFollow the Moneyreported, based on an overview of 2022 by Heineken Russia. The Dutch brewer's Russian subsidiary looked back on “a turbulent year, with many new growth opportunities.” One of these opportunities being the departure ofCoca-ColaandPepsifrom Russia, which Heineken "cynically" used to "enter the non-alcoholic carbonated beverage market". Heineken announced more investments for 2023, including more modern packaging and new flavors.New products launched in Russia included an Irish stout, replacingGuinness(which had been brewed and sold by Heineken, under licence), afterDiageowithdrew from Russia. In August 2023, Heineken announced the sale of its Russian subsidiary to theArnest Groupfor €1 plus a €100 million commitment to repay domestic debt.

[["Heineken", "Company"], ["Russia", "Place"], ["Affligem", "Product"], ["Amstel", "Product"], ["Heineken", "Product"], ["Diageo", "Company"], ["Russian_subdivision_of_Heineken", "Company"], ["Arnest_Group", "Company"], ["Irish_stout", "Product"], ["Coca-Cola", "Company"], ["Pepsi", "Company"], ["€100.0 million", "number"], ["Heineken", "Place"], ["Dutch_language", "Language"], ["Russia", "Company"], ["2023", "Year"], ["March_8,_2022", "Date"], ["Heineken", "Person"], ["Saint_Petersburg", "Place"], ["Euro", "Currency"], ["61", "number"], ["Heineken_Russia", "Company"], ["Dutch_people", "Country"], ["Heineken", "Product"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["240", "number"], ["Affligem", "Company"], ["Amstel", "Company"], ["Saint_Petersburg", "Place"], ["Russian_subdivision_of_Heineken", "Company"], ["Arnest_Group", "Company"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["Diageo", "Company"], ["Irish_stout", "Product"], ["Heineken", "Company"], ["Saint_Petersburg", "Place"], ["Euro", "Currency"], ["Russia", "Place"], ["61", "number"], ["Heineken", "Person"], ["Dutch_language", "Language"], ["Heineken_Russia", "Company"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["240", "number"], ["Affligem", "Product"], ["Amstel", "Product"], ["Heineken", "Product"], ["Diageo", "Company"], ["Irish_stout", "Product"], ["Saint_Petersburg", "Place"], ["Arnest_Group", "Company"], ["Heineken", "Company"], ["Russian_subdivision_of_Heineken", "Company"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["€100.0 million", "number"], ["Heineken_Russia", "Company"], ["Heineken", "Person"], ["Saint_Petersburg", "Place"], ["Dutch_language", "Language"], ["Affligem", "Company"], ["Amstel", "Company"], ["Coca-Cola", "Company"], ["Pepsi", "Company"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["Diageo", "Company"], ["Irish_stout", "Product"], ["Heineken", "Product"], ["Saint_Petersburg", "Place"], ["Arnest_Group", "Company"], ["Heineken_Russia", "Company"], ["Heineken", "Person"], ["Saint_Petersburg", "Place"], ["Dutch_language", "Language"], ["Affligem", "Product"], ["Amstel", "Product"], ["Coca-Cola", "Company"], ["Pepsi", "Company"], ["Diageo", "Company"], ["Irish_stout", "Product"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["Heineken", "Company"], ["Russian_subdivision_of_Heineken", "Company"], ["Arnest_Group", "Company"], ["Saint_Petersburg", "Place"], ["Heineken_Russia", "Company"], ["€1", "number"], ["Heineken", "Person"], ["Saint_Petersburg", "Place"], ["Dutch_language", "Language"], ["Russian_subdivision_of_Heineken", "Company"], ["Arnest_Group", "Company"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["Heineken_Russia", "Company"], ["Heineken", "Product"], ["Affligem", "Product"], ["Amstel", "Product"], ["Coca-Cola", "Company"], ["Pepsi", "Company"], ["Diageo", "Company"], ["Irish_stout", "Product"], ["Saint_Petersburg", "Place"], ["Russian_language", "Language"], ["Heineken", "Company"], ["Russian_subdivision_of_Heineken", "Company"], ["Arnest_Group", "Company"], ["Saint_Petersburg", "Place"], ["Heineken_Russia", "Company"], ["Heineken", "Person"], ["Dutch_language", "Language"], ["Affligem", "Product"], ["Amstel", "Product"], ["Coca-Cola", "Company"], ["Pepsi", "Company"], {"type": "Company", "text": "Heineken", "revenue": "€22,400,000,000"}, {"type": "Company", "text": "Heineken", "netIncome': "€1,800,000,000"}, {"type": "Company', "text": "Heineken', "type': "FoodIndustry"}, {"type": "Company', "text": "Heineken', "parentCompany': "Grolsch"}, {"type': 'Company', "text": 'Heineken', "product': 'Beer"}, {"type': 'Company', 'text': 'Heineken', 'operatingIncome': '€2,700,000,000"}, {"type': 'Company', 'text': 'Heineken', 'numberOfLocations': '8448"}, {"type': 'Company', 'text': 'Heineken', 'foundingDate': 'Heineken', 'location': 'Amsterdam', 'numberOfEmployees': '23800"}, {"type': 'Company', 'text': 'Heineken', 'regionServed': 'Worldwide"}, {"type': 'Company', 'text': 'Heineken', 'subsidiary': 'Mexican_Subdivision_of_Heineken"}, {"type': 'Company', 'text': 'Heineken', 'leaderTitle': 'CEO"}, {"type': 'Company', 'text': 'Heineken', 'ethnicGroup': 'Indigenous_people_of_the_Caribbean'}, {"type': 'Company', 'text': 'Heineken', 'city': 'Amsterdam"}, {"type': 'Company', 'text': 'Heineken', 'industry': 'FoodIndustry'}, {"type': 'Company', 'text': 'Heineken', 'longName': 'Heineken N.V.', 'isPartOf': 'Grolsch"}, {"type': 'Company', 'text': 'Heineken', 'areaTotal': '1049000000.0'}, {"type': 'Company', 'text': 'Heineken', 'leader': 'Dagmar Reim', 'elevationAboveTheSeaLevel': '0.0'}]


Investments 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: Heineken_N, Investments in Russia: At the end of March 2022, over a month after Russia started its war in Ukraine, Heineken announced that it was leaving Russia (including with its other brands there, likeAffligem,Amsteletc.), saying that ownership of the Russian subsidiary was no longer “durable or viable.” But despite this promise Heineken hired more than 240 new staff and launched 61 new products on the Russian market in the last year,[clarification needed]investigators fromFollow the Moneyreported, based on an overview of 2022 by Heineken Russia. The Dutch brewer's Russian subsidiary looked back on “a turbulent year, with many new growth opportunities.” One of these opportunities being the departure ofCoca-ColaandPepsifrom Russia, which Heineken "cynically" used to "enter the non-alcoholic carbonated beverage market". Heineken announced more investments for 2023, including more modern packaging and new flavors.New products launched in Russia included an Irish stout, replacingGuinness(which had been brewed and sold by Heineken, under licence), afterDiageowithdrew from Russia. In August 2023, Heineken announced the sale of its Russian subsidiary to theArnest Groupfor €1 plus a €100 million commitment to repay domestic debt.

[["Heineken", "subsidiary", "Heineken_N"], ["Heineken", "has_product_line", "Affligem"], ["Heineken", "has_product_line", "Amstel"], ["Heineken", "subsidiary", "Heineken_Russia"], ["Heineken_Russia", "has_product_line", "Irish_stout"], ["Heineken_Russia", "has_product_line", "Guinness"], ["Heineken_Russia", "has_product_line", "Non-alcoholic_beverage"], ["Heineken_Russia", "has_product_line", "Coca-Cola"], ["Heineken_Russia", "has_product_line", "Pepsi"], ["Heineken_Russia", "has_product_line", "Beer"], ["Heineken_Russia", "has_product_line", "Pale_ale"], ["Heineken_Russia", "has_product_line", "Stout_(beer)"], ["Heineken_Russia", "has_product_line", "Ale"], ["Heineken_Russia", "has_product_line", "Wheat_bier"], ["Heineken_Russia", "has_product_line", "Bock"], ["Heineken_Russia", "has_product_line", "Lager"], ["Heineken_Russia", "has_product_line", "Pilsner"], ["Heineken_Russia", "has_product_line", "Bière_de_Ménage"], ["Heineken_Russia", "has_product_line", "Saison"], ["Heineken_Russia", "has_product_line", "India_ale"], ["Heineken_Russia", "has_product_line", "English_bitter"], ["Heineken_Russia", "has_product_line", "Fruit_beam"], ["Heineken_Russia", "has_product_line", "Maibock"], ["Heineken_Russia", "has_product_line", "Milk_stout"], ["Heineken_Russia", "has_product_line", "Oatmeal_stout"], ["Heineken_Russia", "has_product_line", "Pale_ale"], ["Heineken_Russia", "has_product_line", "Porter"], ["Heineken_Russia", "has_product_line", "Wheat_beer"], ["Heineken_Russia", "has_product_line", "Ale"], ["Heineken_Russia", "has_product_line", "Bière_de_Ménage"], ["Heineken_Russia", "has_product_line", "Saison"], ["Heineken_Russia", "has_product_line", "India_ale"], ["Heineken_Russia", "has_product_line", "English_bitter"], ["Heineken_Russia", "has_product_line", "Fruit_beam"], ["Heineken_Russia", "has_product_line", "Maibock"], ["Heineken_Russia", "has_product_line", "Milk_stout"], ["Heineken_Russia", "has_product_line", "Oatmeal_stout"], ["Heineken_Russia", "has_product_line", "Pale_ale"], ["Heineken_Russia", "has_product_line", "Porter"], ["Heineken_Russia", "has_product_line", "Wheat_beer"], ["Heineken_Russia", "has_product_line", "Irish_stout"], ["Heineken_Russia", "has_product_line", "Guinness"], ["Heineken_Russia", "has_product_line", "Non-alcoholic_beverage"], ["Heineken_Russia", "has_product_line", "Coca-Cola"], ["Heineken_Russia", "has_product_line", "Pepsi"], ["Heineken_Russia", "has_product_line", "Beer"], ["Heineken_Russia", "has_product_line", "Pale_ale"], ["Heineken_Russia", "has_product_line", "Stout_(beer)"], ["Heineken_Russia", "has_product_line", "Ale"], ["Heineken_Russia", "has_product_line", "Wheat_bier"], ["Heineken_Russia", "has_product_line", "Bock"], ["Heineken_Russia", "has_product_line", "Lager"], ["Heineken_Russia", "has_product_line", "Pilsner"], ["Heineken_Russia", "has_product_line", "Bière_de_Ménage"], ["Heineken_Russia", "has_product_line", "Saison"], ["Heineken_Russia", "has_product_line", "India_ale"], ["Heineken_Russia", "has_product_line", "English_bitter"], ["Heineken_Russia", "has_product_line", "Fruit_beam"], ["Heineken_Russia", "has_product_line", "Maibock"], ["Heineken_Russia", "has_product_line", "Milk_stout"], {"Heineken_Russia", "has_product_line", "Oatmeal_stout"}, {"Heineken_Russia", "has_product_line", "Pale_ale"}, {"Heineken_Russia", "has_product_line", "Porter"}, {"Heineken_Russia", "has_product_line", "Wheat_beer"}]