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.
Sign in to Power Apps or Power Automate.
On the left pane, select ... More > AI hub.
Under Discover an AI capability, select AI models.
(Optional) To keep AI models permanently on the menu for easy access, select the pin icon.
Select Entity Extraction - Extract key elements from text, and classifies them into predefined categories.
Select predefined text samples to analyze, or add your own text, select Analyze text 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. For more information, see Use Power Fx in AI Builder models in Power Apps (preview).
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 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 |