name : batch-detect-entities.rst **To detect entities from multiple input texts** The following ``batch-detect-entities`` example analyzes multiple input texts and returns the named entities of each. The pre-trained model's confidence score is also output for each prediction. :: aws comprehend batch-detect-entities \ --language-code en \ --text-list "Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st." "Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at AnySpa@example.com." Output:: { "ResultList": [ { "Index": 0, "Entities": [ { "Score": 0.9985517859458923, "Type": "PERSON", "Text": "Jane", "BeginOffset": 5, "EndOffset": 9 }, { "Score": 0.9767839312553406, "Type": "ORGANIZATION", "Text": "AnyCompany Financial Services, LLC", "BeginOffset": 16, "EndOffset": 50 }, { "Score": 0.9856694936752319, "Type": "OTHER", "Text": "1111-XXXX-1111-XXXX", "BeginOffset": 71, "EndOffset": 90 }, { "Score": 0.9652159810066223, "Type": "QUANTITY", "Text": ".53", "BeginOffset": 116, "EndOffset": 119 }, { "Score": 0.9986667037010193, "Type": "DATE", "Text": "July 31st", "BeginOffset": 135, "EndOffset": 144 } ] }, { "Index": 1, "Entities": [ { "Score": 0.720084547996521, "Type": "ORGANIZATION", "Text": "Sunshine Spa", "BeginOffset": 33, "EndOffset": 45 }, { "Score": 0.9865870475769043, "Type": "LOCATION", "Text": "123 Main St", "BeginOffset": 47, "EndOffset": 58 }, { "Score": 0.5895616412162781, "Type": "LOCATION", "Text": "Anywhere", "BeginOffset": 60, "EndOffset": 68 }, { "Score": 0.6809214353561401, "Type": "PERSON", "Text": "Alice", "BeginOffset": 75, "EndOffset": 80 }, { "Score": 0.9979087114334106, "Type": "OTHER", "Text": "AnySpa@example.com", "BeginOffset": 84, "EndOffset": 99 } ] } ], "ErrorList": [] } For more information, see `Entities <https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html>`__ in the *Amazon Comprehend Developer Guide*.