Create a simple entity
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- UpdatedJan 30, 2025
- 3 minutes to read
- Yokohama
- AI Experiences
Create one or more simple entities from words in your utterance examples. An entity is an object of, or context for, an action.
Before you begin
- Make sure that the NLU Workbench plugin, NLU Workbench - Core plugin, NLU Common Model plugin, and Predictive Intelligence plugin are all installed and activated on your instance.
- Create or use an existing NLU model for Virtual Agent or AI Search.
- Create or use an existing intent.
- Role required: nlu_editor, nlu_admin, or admin. The nlu_editor must be assigned to the model.
About this task
Simple entities are words or phrases whose value can be extracted by your model. Simple entities are identified based on the context in which the entity is used in an utterance.
For the following example procedure, you've already created an intent that's titled SubmitAccessRequest and you're creating a simple entity for the type of access the user is requesting.
Procedure
What to do next
Your utterances can reference a vocabulary source by using the @ handle. If you have a list of values that are defined in a vocabulary source, you can annotate the @ handle as a simple entity to extract it rather than repeating the utterance for all of the values. The referenced vocabulary source can be a table or a list. For example, the following image shows how you invoke a vocabulary source that lists various conference room names.

For more information, see NLU vocabulary.
Related Content
- Create a mapped entity
Create an entity mapped to a vocabulary source, or to a list of values you manually create for the entity. Mapped entities can help provide multiple values the model can use as context when interpreting utterances.
- Create a pattern entity
Create a pattern entity from a word or phrase with repeatable patterns, such as email addresses and phone numbers. These patterns help the system to recognize similar utterances based on the patterns.
- Create a system-derived entity
Create a custom entity that's derived from a default system entity such as date, time, duration, or location.
- Create an open-ended entity
Use an open-ended entity when you want to improve intent prediction accuracy. Open-ended entities help your model focus on the context of the utterances.
- Import entities
Reuse entities that you have created across your other Natural Language Understanding (NLU) models. Importing entities saves time and helps improve the intents in your model.
- Using regular expressions in entities
Learn how to use regular expressions in your NLU entities to establish patterns that help the system locate, match, and manage text.