System Entities

June 12, 2019
|By Lukas Volz

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System entities are predefined entities that are used in an AI agent platform and extract frequently recurring data types from natural language. Typical examples include date, time, location, currency, phone number, or number. While custom entities are created individually for a specific use case, system entities cover universal concepts and reduce training effort. 

 

How System Entities Work

When a user formulates a query, the NLU model first analyzes the intent and simultaneously searches for relevant parameters in the utterance. This is exactly where system entities come into play: they identify standardized data types regardless of the specific wording and return the extracted values in a normalized format.

If a caller says on the phone, “I’d like to schedule an appointment for Tuesday at 10 a.m.,” the AI agent identifies the next Tuesday using a date entity and returns it in ISO format. A time entity simultaneously returns the value 10:00. This normalization is crucial because downstream systems such as calendars, CRM, or ticketing systems require unambiguous values and cannot interpret free-text data.

 

Types of System Entities

Platforms typically distinguish between three categories that differ in their return values. They are closely related to the concept of entity and extend it with ready-to-use standard types.

 

  • System Mapping: Provides a normalized reference value, such as a date in ISO 8601 format or a currency as a numeric value with a code.
  • System Enum: Returns the recognized term exactly as it was spoken, without mapping it to a standard form, such as color names.
  • System Composite: Combines multiple entities into a single object, such as a monetary amount consisting of a value and a currency.

 

Typical predefined entities in modern platforms include date, time, duration, number, ordinal number, monetary amount, quantity, address, city, country, language, person's name, email address, and phone number.

 

Frequently Asked Questions (FAQ)

System entities are predefined entities that come standard with an AI agent platform. They recognize universal data types such as date, time, location, number, or currency in natural language and return the values in a standardized format. This means that these standard cases do not need to be trained separately.

System entities cover general concepts that are common to many use cases. Custom entities are defined specifically for a project, such as product categories, contract types, or internal designations. In practice, both are combined to reliably capture both universal and domain-specific information.

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