Social responsibility

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: Alibaba_Group, Social responsibility: In 2014, the Chinese government and Alibaba partnered to create the rural e-commerce expansion program Rural Taobao, which aimed to provide rural households with the same level of access to consumer goods that urban residents have and to facilitate the sale of agricultural goods through e-commerce.: 129As of 2017, the program covered 16,500 villages.: 130 Beginning in March 2016, Alibaba and theCommunist Youth Leaguebegan a collaboration to promote online entrepreneurship amongrural Chineseyouth.: 129Later that year, Alibaba signed other rural development agreements with theNational Development and Reform CommissionandMinistry of Agriculture.: 129 In May 2017, Alibaba signed a memorandum with theSupreme People's Procuratorate's Anti-Corruption Bureau "to create a clean, credible, rule-of-law market environment.": 77Pursuant to the memorandum, the Bureau provides Alibaba and Ant Group with access to criminal records from bribery cases and Alibaba and Ant Group query those records including during verification of Taobao sellers and in connection with anti-money laundering initiatives and financial risk controls.: 77 On 7 August 2021, Alibaba fired a manager who admitted to "indecent acts" with an inebriated employee.The case highlighted issues of sexual harassment in the workplace. Alibaba actively promotes female employment, collaborating with theAll-China Women's Federationfor community e-commerce events. The company also donates to UN Women to support female employment initiatives.Half of Alibaba's employees are women, with one-third of leadership roles, especially in departments like human resources, held by women. Alibaba collaborates with Hangzhou'ssmart cityinitiative and efforts to digitize the economy and build a cashless city.: 122–123

[["Alibaba_Group", "Company"], ["Rural_Taobao", "Product"], ["National_Development_and_Reform_Commission", "Organization"], ["Hangzhou", "City"], ["Communist_Youth_League", "Company"], ["Alibaba_Group", "Product"], ["Supreme_People's_Procuratorate", "Organization"], ["Ant_Group", "Company"], ["United_Nations_Women", "Organization"], ["Hangzhou_Smart_City", "City"], ["Sexual_harassment", "Issue"], ["Alibaba_Group", "Person"], ["All-China_Women's_Federation", "Organization"], ["2017-05-15", "Date"], ["Hangzhou", "Company"], ["Rural_Taobao", "Company"], ["16,500", "number"], ["Alibaba_Group", "City"], ["Ant_Group", "Person"], ["Sexual_harassment", "Company"], ["United_Nations_Women", "Company"], ["Alibaba_Group", "Country"], ["Hangzhou_Smart_City", "Company"], ["2014", "Year"], ["Ant_Group", "Product"], ["Alibaba_Group", "Product"], ["Hangzhou", "Country"], ["All-China_Women's_Federation", "Company"], ["Ant_Group", "Company"], ["Sexual_harassment", "Product"], ["Alibaba_Group", "Person"], ["United_Nations_Women", "Person"], ["Hangzhou_Smart_City", "Person"], ["16,500", "Market_Share"], ["Alibaba_Group", "City"], ["Ant_Group", "City"], ["Alibaba_Group", "Company"], ["Sexual_harassment", "Company"], ["Hangzhou_Smart_City", "City"], ["United_Nations_Women", "Company"], ["Ant_Group", "Person"], ["Alibaba_Group", "Person"], ["Hangzhou", "Person"], ["All-China_Women's_Federation", "Person"], ["2017-05-15", "Product"], ["Rural_Taobao", "Company"], ["Ant_Group", "Company"], ["Alibaba_Group", "Company"], ["Sexual_harassment", "Issue"], ["Hangzhou_Smart_City", "Company"], ["United_Nations_Women", "Person"], ["Alibaba_Group", "City"], ["Ant_Group", "City"], ["Hangzhou", "Company"], ["2014", "Date"], ["Alibaba_Group", "Person"], ["All-China_Women's_Federation", "Company"], ["Ant_Group", "Person"], ["Sexual_harassment", "Product"], ["Hangzhou_Smart_City", "City"], ["United_Nations_Women", "Company"], ["16,500", "number"], ["Alibaba_Group", "Company"], ["Ant_Group", "Company"], ["Rural_Taobao", "Product"], ["Hangzhou", "City"], ["Alibaba_Group", "Person"], ["Sexual_harassment", "Company"], ["Ant_Group", "Person"], ["United_Nations_Women", "Person"], ["Hangzhou_Smart_City", "Person"], ["All-China_Women's_Federation", "Person"], ["2017-05-15", "Date"], ["Alibaba_Group", "City"], ["Ant_Group", "City"], ["National_Development_and_Reform_Commission", "Company"], ["Alibaba_Group", "Product"], ["Sexual_harassment", "Issue"], ["Hangzhou", "Country"], ["Alibaba_Group", "Company"], ["Ant_Group", "Person"], ["United_Nations_Women", "Company"], ["Hangzhou_Smart_City", "Company"], {"type": "Company", "key": "Alibaba_Group", "value": "Hangzhou"}, {"type": "Company', "key": "Alibaba_Group', "value": "Ant_Group"}, {"type": "Company', "key": "Alibaba_Group', "value": "United_Nations_Women"}, {"type": "Company', "key": "Alibaba_Group', "value": "All-China_Women's_Federation"}, {"type": "Company', "key": "Alibaba_Group', "value": "Sexual_harassment"}, {"type": "Company', "key": "Alibaba_Group', "value": "Hangzhou_Smart_City"}, {"type": "Company', "key": "Alibaba_Group', "value": "National_Development_and_Reform_Commission"}]


Social responsibility

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: Alibaba_Group, Social responsibility: In 2014, the Chinese government and Alibaba partnered to create the rural e-commerce expansion program Rural Taobao, which aimed to provide rural households with the same level of access to consumer goods that urban residents have and to facilitate the sale of agricultural goods through e-commerce.: 129As of 2017, the program covered 16,500 villages.: 130 Beginning in March 2016, Alibaba and theCommunist Youth Leaguebegan a collaboration to promote online entrepreneurship amongrural Chineseyouth.: 129Later that year, Alibaba signed other rural development agreements with theNational Development and Reform CommissionandMinistry of Agriculture.: 129 In May 2017, Alibaba signed a memorandum with theSupreme People's Procuratorate's Anti-Corruption Bureau "to create a clean, credible, rule-of-law market environment.": 77Pursuant to the memorandum, the Bureau provides Alibaba and Ant Group with access to criminal records from bribery cases and Alibaba and Ant Group query those records including during verification of Taobao sellers and in connection with anti-money laundering initiatives and financial risk controls.: 77 On 7 August 2021, Alibaba fired a manager who admitted to "indecent acts" with an inebriated employee.The case highlighted issues of sexual harassment in the workplace. Alibaba actively promotes female employment, collaborating with theAll-China Women's Federationfor community e-commerce events. The company also donates to UN Women to support female employment initiatives.Half of Alibaba's employees are women, with one-third of leadership roles, especially in departments like human resources, held by women. Alibaba collaborates with Hangzhou'ssmart cityinitiative and efforts to digitize the economy and build a cashless city.: 122–123

[["Alibaba_Group", "has_product_line", "Rural_Taobao"], ["Alibaba_Group", "has_product_line", "Ant_Group"], ["Alibaba_Group", "has_product_line", "Taobao"], ["Alibaba_Group", "has_product_line", "Smart_City"], ["Alibaba_Group", "has_product_line", "Community_e-commerce"], ["Alibaba_Group", "has_product_line", "Hangzhou"], ["Alibaba_Group", "has_product_line", "Agriculture"], ["Alibaba_Group", "has_product_line", "City"], ["Alibaba_Group", "has_product_line", "E-commerce"], ["Alibaba_Group", "has_product_line", "Women"], ["Alibaba_Group", "has_product_line", "UN_Women"], ["Alibaba_Group", "has_product_line", "Party_youth_league"], ["Alibaba_Group", "has_product_line", "National_Development_and_Reform_Commission"], ["Alibaba_Group", "has_product_line", "All-China_Women's_Federation"], ["Alibaba_Group", "has_product_line", "Supreme_People's_Procuratorate"], ["Alibaba_Group", "has_product_line", "Sexual_harassment"], ["Alibaba_Group", "has_product_line", "Cashless_society"], ["Alibaba_Group", "has_product_line", "Agritainment"], ["Alibaba_Group", "has_product_line", "Digitization"], ["Alibaba_Group", "has_product_line", "Bribery"], ["Alibaba_Group", "has_product_line", "Economic_development"], ["Alibaba_Group", "has_product_line", "Financial_services"], ["Alibaba_Group", "has_product_line", "Government"], ["Alibaba_Group", "has_product_line", "Social_responsibility"], ["Alibaba_Group", "has_product_line", "Election"], ["Alibaba_Group", "has_product_line", "Party"], ["Alibaba_Group", "has_product_line", "Politician"], ["Alibaba_Group", "has_product_line", "Corruption"], ["Alibaba_Group", "has_product_line", "Money"], ["Alibaba_Group", "has_product_line", "Business"], ["Alibaba_Group", "has_product_line", "Professional_sports_team"], ["Alibaba_Group", "has_product_line", "Sports"], ["Alibaba_Group", "has_product_line", "Athlete"], ["Alibaba_Group", "has_product_line", "Entertainment"], ["Alibaba_Group", "has_product_line", "Film"], ["Alibaba_Group", "has_product_line", "Channel"], ["Alibaba_Group", "has_product_line", "Cityscape"], ["Alibaba_Group", "has_product_line", "Place"], ["Alibaba_Group", "has_product_line", "Ethnic_group"], ["Alibaba_Group", "has_product_line", "Company"], ["Alibaba_Group", "has_product_line", "Organisation"], ["Alibaba_Group", "has_product_line", "Country"], ["Alibaba_Group", "has_product_line", "City"], ["Alibaba_Group", "has_product_line", "Language"], ["Alibaba_Group", "has_product_line", "State"], ["Alibaba_Group", "has_product_line", "Station"], ["Alibaba_Group", "has_product_line", "Occupation"], ["Alibaba_Group", "has_product_line", "Industry"], ["Alibaba_Group", "has_product_line", "Person"], ["Alibaba_Group", "has_product_line", "University"], ["Alibaba_Group", "has_product_line", "Mean_of_transportation"], ["Alibaba_Group", "has_product_line", "Airport"], ["Alibaba_Group", "has_product_line", "School"], ["Alibaba_Group", "has_product_line", "Software"], ["Alibaba_Group", "has_product_line", "Hotel"], ["Alibaba_Group", "has_product_line", "Food"], ["Alibaba_Group", "has_product_line", "Airport"], ["Alibaba_Group", "has_product_line", "Flight"], ["Alibaba_Group", "has_product_line", "Organisation"], ["Alibaba_Group", "has_product_line", "City"], ["Alibaba_Group", "has_product_line", "Agritourism"], ["Alibaba_Group", "has_product_line", "Tourism"], ["Alibaba_Group", "has_product_line", "Retailer"], ["Alibaba_Group", "has_product_line", "Consumer"], ["Alibaba_Group", "has_product_line", "Lawsuit"], ["Alibaba_Group", "has_product_line", "Court"], ["Alibaba_Group", "has_product_line", "Judicial_case"], ["Alibaba_Group", "has_product_line", "Country"], ["Alibaba_Group", "has_product_line", "City"], ["Alibaba_Group", "has_product_line", "Company"], ["Alibaba_Group", "has_product_line", "Person"], ["Alibaba_Group", "has_product_line", "Place"], ["Alibaba_Group", "has_product_line", "Ethnic_group"], ["Alibaba_Group", "has_product_line", "Organisation"], ["Alibaba_Group", "has_product_line", "Airport"], ["Alibaba_Group", "has_product_line", "Flight"], ["Alibaba_Group", "has_product_line", "Aircraft"], ["Alibaba_Group", "has_product_line", "Demonym"], ["Alibaba_Group", "has_product_line", "Place"], ["Alibaba_Group", "has_product_line", "Ethnic_group"], {"Alibaba_Group", "has_product_line", "University"}, {"Alibaba_Group", "has_product_line", "School"}, {"Alibaba_Group", "has_product_line", "City"}, {"Alibaba_Group", "has_product_line", "Country"}, {"Alibaba_Group", "has_product_line", "Airport"}, {"Alibaba_Group", "has_product_line", "Aircraft"}, {"Alibaba_Group", "has_product_line", "Organisation"}, {"Alibaba_Group", "has_product_line", "Flight"}, {"Alibaba_Group", "has_product_line", "Politician"}, {"Alibaba_Group", "has_product_line", "Party"}, {"Alibaba_Group", "has_product_line", "Mean_of_transportation"}, {"Alibaba_Group", "has_product_line", "Place"}, {"Alibaba_Group", "has_product_line", "Ethnic_group"}, {"Alibaba_Group", "has_product_line", "Country'}, {"Alibaba_Group", "has_product_line", "City"}, {"Alibaba_Group", "has_product_line", "Airport'}, {"Alibaba_Group", "has_product_line", "Aircraft'}, {"Alibaba_Group", "has_product_line", "Organisation"}, {"Alibaba_Group", "has_product_line", "Flight"}, {"Alibaba_Group", "has_product_line", "Politician"}, {"Alibaba_Group", "has_product_line", "Party"}, {"Alibaba_Group", "has_product_line", "Mean_of_transportation"}]


External links

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: Alibaba_Group, External links: 30°11′31″N120°11′07″E / 30.19194°N 120.18528°E /30.19194; 120.18528


External links

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: Alibaba_Group, External links: 30°11′31″N120°11′07″E / 30.19194°N 120.18528°E /30.19194; 120.18528