Entity extraction prebuilt model

The prebuilt entity extraction model recognizes specific data from text that's of interest to your business. The model identifies key elements from text, and then classifies them into predefined categories. This can help to transform unstructured data into structured data that's machine-readable. You can then apply processing to retrieve information, extract facts, and answer questions.

The prebuilt model is ready to use out of the box. For information about customizing your entity extraction to suit your specific needs, see Overview of the entity extraction custom model.

Use in Power Apps

Explore entity extraction

You can try out the entity extraction model before you import it into your flow by using the "try it out" feature.

  1. Sign in to Power Apps.
  2. In the left pane, select AI Builder > Build.
  3. Under Get straight to productivity, select Entity Extraction.
  4. In the Entity Extraction window, select Try it out.
  5. Select predefined text samples to analyze, or add your own text in the Add your own here box to see how the model analyzes your text.

Use the formula bar

You can integrate your AI Builder entity extraction model in Power Apps Studio by using the formula bar. More information: Use formulas for text AI models

Use in Power Automate

If you want to use this prebuilt model in Power Automate, you can find more information in Use the entity extraction prebuilt model in Power Automate.

Supported data format and languages

  • Documents can't exceed 5,000 characters.
  • Supported languages:
    • English
    • Chinese-Simplified
    • French
    • German
    • Portuguese
    • Italian
    • Spanish

Supported entity types

Entity Description
Age Age of a person, place, or thing, extracted as a number
Boolean Positive or negative responses, extracted as a Boolean
City City names, extracted as a string
Color Primary colors and hues on the color spectrum, extracted as a string
Continent Continent names, extracted as a string
Country or region Country and region names, extracted as a string
Date and time Dates, times, days of the week, and months relative to a point in time, extracted as a string
Duration Lengths of time, extracted as a string in standard TimeSpan format
Email Email addresses, extracted as a string
Event Event names, extracted as a string
Language Language names, extracted as a string
Money Monetary amounts, extracted as a number
Number Cardinal numbers in numeric or text form, extracted as a number
Ordinal Ordinal numbers in numeric or text form, extracted as a number
Organization Names of organizations, associations, and corporations, extracted as a string
Percentage Percentages in numeric or text form, extracted as a number
Person name A person's partial or full name, extracted as a string
Phone number Phone numbers in the standard US format, extracted as strings
Speed Speed, extracted as a number
State Names and abbreviations for states in the United States, extracted as a string
Street address Numbered addresses, streets or roads, city, state, ZIP or postal code in the standard US format, extracted as a string
Temperature Temperature, extracted as a number
URL Website URLs and links, extracted as a string
Weight Weight, extracted as a number
Zip code ZIP codes in the standard US format, extracted as a string

Model output

The model output shows the identified entities and their entity types. For example:

Input text: "Utility costs have increased by 7% at our Boston office"

Model output entities:

Entity Entity type
7% Percentage
Boston City

Next step

Use the entity extraction prebuilt model in Power Automate