Entity Detection
Extract named entities — persons, organizations, locations, dates, and events — from text or audio files.
Two pipelines cover named entity recognition (NER) — one for text files and one for audio. The audio variant first transcribes the file with Whisper, then runs NER on the transcript. Both return the same output schema.
lt-entity-detection-text
Extracts named entities directly from text and document files using an LLM.
150 credits per file · ~2 minutes
Accepted Files
| Type | MIME types |
|---|---|
| Text | text/* |
| Documents | application/pdf, Word (.docx) |
Up to 10 files per run.
Context Parameters
| Parameter | Required | Description |
|---|---|---|
entity_types | No | Comma-separated list of entity types to focus on (e.g. "PERSON,ORG,LOC"). If omitted, all supported types are detected. |
Supported entity types: PERSON, ORG, LOC, DATE, EVENT, MISC
API Example
POST /v2/lt-pipelines/project/{projectId}/run{
"pipelineSlug": "lt-entity-detection-text",
"fileIds": ["file_abc123"],
"contextData": {
"entity_types": "PERSON,ORG"
}
}lt-entity-detection-audio
Transcribes the audio file with Whisper, then extracts named entities from the transcript in a second stage. This pipeline runs two stages sequentially.
350 credits per file · ~5 minutes
Accepted Files
| Type | MIME types |
|---|---|
| Audio | audio/* (MP3, WAV, M4A, OGG, FLAC, …) |
One file per run. Maximum file size: 25 MB.
Context Parameters
None.
API Example
POST /v2/lt-pipelines/project/{projectId}/run{
"pipelineSlug": "lt-entity-detection-audio",
"fileIds": ["file_abc123"],
"contextData": {}
}Output
Both pipelines return the same output schema:
{
"pipelineSlug": "lt-entity-detection-text",
"output": {
"entities": [
{
"text": "Angela Merkel",
"type": "PERSON",
"start": 12,
"end": 25,
"confidence": 0.97
},
{
"text": "Berlin",
"type": "LOC",
"start": 42,
"end": 48,
"confidence": 0.94
}
],
"entityCount": 2
},
"rawText": null
}Output Fields
| Field | Type | Description |
|---|---|---|
entities | array | All detected entities |
entities[].text | string | The entity text as it appears in the source |
entities[].type | string | Entity type: PERSON, ORG, LOC, DATE, EVENT, or MISC |
entities[].start | number | Character offset of the entity start in the source text |
entities[].end | number | Character offset of the entity end |
entities[].confidence | number | Detection confidence score 0–1 |
entityCount | number | Total number of detected entities |
How is this guide?
Language Identification
Detect the language(s) present in text files or spoken in audio files.
API Overview
Base URL, versioning, authentication, request format, and rate limiting.