> For the complete documentation index, see [llms.txt](https://docs.amigo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amigo.ai/developer-guide/platform-api/data-world-model/entity-resolve.md).

# Entity Resolve

Entity resolve looks up entities by phone number, email, canonical identifier, external-system identifier, entity UUID, or a combination of identifiers. It powers inbound caller identification, webhook matching, and other workflows that need to turn an external identifier into a workspace entity.

## Resolve an Entity

{% openapi src="<https://api.platform.amigo.ai/v1/openapi.json>" path="/v1/{workspace\_id}/world/entities/resolve" method="post" %}
<https://api.platform.amigo.ai/v1/openapi.json>
{% endopenapi %}

At least one identifier is required. An external-system identifier is system-qualified: resolution checks canonical keys for supported resource types and the bare value used by producers that minted it as a canonical identifier. An external ID that was never minted into the canonical map is not resolvable through this operation.

## Ranking

Candidates are ranked by confidence and then by the number of identifiers that matched. A direct UUID match has confidence `1.0`; a row matching both phone and email ranks above one matching only phone. See [Confidence as Trust Architecture](/developer-guide/platform-api/data-world-model.md#confidence-as-trust-architecture) for the platform confidence scale.

## Example

```json
{
  "entity_type": "person",
  "phone": "+15551234567",
  "email": "jane@example.com",
  "limit": 5
}
```

Requests without an identifier return `400`; malformed values return `422`.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.amigo.ai/developer-guide/platform-api/data-world-model/entity-resolve.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.