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Version: Current

Extract Categorized Entities

Description​

The Extract Categorized Entities activity identifies and classifies named entities within text using Azure AI Language services.

Entities are recognized and assigned categories such as people, organizations, locations, dates, quantities, events, and more.

For example, in the text:

John Smith works at Contoso in Seattle.

The activity may identify:

  • John Smith → Person
  • Contoso → Organization
  • Seattle → Location

Common settings​

Use Process Data to bind supported inputs to workflow values or upstream outputs. See Common Activity Features for shared execution, error-handling, and output-persistence settings. Availability depends on the activity.

Design-Time Configuration​

Display Text​

The label for this activity as it appears on the workflow canvas.

Connection Name​

Select one of the following:

  • An Azure Text Analytics connection configured in AI Command Center using customer-provided Azure credentials.
  • IB-X Azure Text Analytics Credits to use the IntelliBuddies Integration Gateway.

When using IB-X Azure Text Analytics Credits, requests are processed through the Integration Gateway and consume IB-X Currency based on the request volume and service usage.

Input Text​

The text from which entities should be extracted.

The value can be entered directly or supplied dynamically from Process Data.

Execution settings​

Configure Wait All Incoming when execution paths converge, and On Error for failure handling. Select Inherit where available to use the workflow-level error policy.

Outputs​

Categorized Entities​

A collection of extracted entities containing information such as:

  • Entity text
  • Category
  • Subcategory (when available)
  • Confidence score
  • Position within the source text

Raw Response​

The complete JSON response returned by Azure AI Language services.

Notes​

  • Multiple entities can be extracted from a single text input.
  • Entity categories depend on Azure AI Language service capabilities.
  • When using the Integration Gateway, IB-X Currency is consumed based on the request volume.

Example: Analyze text​

Select an Azure AI Language connection or the documented credits option, then bind Input Text to text such as “John Smith works at Contoso in Seattle.”. After execution, use Categorized Entities to inspect Text, Category, and ConfidenceScore for each entity. See the output model for property types.

For a collection output, check it contains items before iterating. Keep Raw Response for cases where the workflow needs details not exposed by the structured output. See Azure Text Analytics connection.

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