Cookbook: Extract relations between entities

Relation extraction turns sentences into facts you can store in a graph or a table. You define the entity labels and the relation types, and GLiNER returns each relation as a head entity, a tail entity, and the relation type.

Request

curl -X POST https://api.fastino.ai/v1/chat/completions \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "fastino/gliner2.5-multi-v1",
  "messages": [
    {
      "role": "user",
      "content": "Lena Vogel works for Siemens in Munich. Siemens acquired Altair Engineering in 2025."
    }
  ],
  "schema": {
    "entities": [
      {
        "name": "person",
        "description": "full name of a person"
      },
      {
        "name": "organization",
        "description": "company or institution"
      },
      {
        "name": "location",
        "description": "city or country"
      }
    ],
    "relations": {
      "works_for": {
        "description": "a person is employed by an organization"
      },
      "located_in": {
        "description": "an organization or person is based in a place"
      },
      "acquired": {
        "description": "one organization bought another"
      }
    }
  },
  "threshold": 0.5,
  "store": false
}'

Request schema

Field	Type	In this recipe
`model`	string	`fastino/gliner2.5-multi-v1`.
`messages`	array	One `user` message holding the text to read.
`schema.entities`	array	The entity labels relations connect.
`schema.relations`	object	One key per relation type. `description` says what the relation means, including its direction.
`threshold`	number	Minimum confidence, from 0 to 1. Default `0.5`.
`store`	boolean	Defaults to `true`, which saves the inference. `false` opts out.

Response

GLiNER returns the result as a JSON string in choices[0].message.content. Parsed, it looks like this. Measured against https://api.fastino.ai; confidence values vary slightly between calls.

{
  "entities": {
    "person": [
      {
        "text": "Lena Vogel",
        "confidence": 1.0,
        "start": 0,
        "end": 10
      }
    ],
    "organization": [
      {
        "text": "Siemens",
        "confidence": 0.996,
        "start": 21,
        "end": 28
      },
      {
        "text": "Siemens",
        "confidence": 0.992,
        "start": 40,
        "end": 47
      },
      {
        "text": "Altair Engineering",
        "confidence": 0.973,
        "start": 57,
        "end": 75
      }
    ],
    "location": [
      {
        "text": "Munich",
        "confidence": 1.0,
        "start": 32,
        "end": 38
      }
    ]
  },
  "relation_extraction": {
    "works_for": [
      {
        "head": {
          "text": "Lena Vogel",
          "start": 0,
          "end": 10,
          "confidence": 0.988
        },
        "tail": {
          "text": "Siemens",
          "start": 21,
          "end": 28,
          "confidence": 0.988
        }
      }
    ],
    "located_in": [
      {
        "head": {
          "text": "Siemens",
          "start": 40,
          "end": 47,
          "confidence": 0.977
        },
        "tail": {
          "text": "Munich",
          "start": 32,
          "end": 38,
          "confidence": 0.977
        }
      }
    ],
    "acquired": [
      {
        "head": {
          "text": "Siemens",
          "start": 40,
          "end": 47,
          "confidence": 0.938
        },
        "tail": {
          "text": "Altair Engineering",
          "start": 57,
          "end": 75,
          "confidence": 0.938
        }
      }
    ]
  }
}

Response schema

Field	Type	Meaning
`relation_extraction`	object	One key per relation type you defined.
`relation_extraction.<type>[].head`	object	The entity the relation starts from, with `text`, `confidence`, `start`, and `end`.
`relation_extraction.<type>[].tail`	object	The entity the relation points to, with the same fields.

Relations come back under relation_extraction, not under relations.

Act on the answer

import json

result = json.loads(response.json()["choices"][0]["message"]["content"])

facts = [
    (pair["head"]["text"], relation, pair["tail"]["text"])
    for relation, pairs in result["relation_extraction"].items()
    for pair in pairs
    if min(pair["head"]["confidence"], pair["tail"]["confidence"]) >= 0.5
]
for head, relation, tail in facts:
    graph.add_edge(head, tail, type=relation)

Adapt it

State the direction in each description, such as “a person is employed by an organization”, so heads and tails come out the right way round.
Define entity labels for every kind of thing your relations connect, so the entities are typed in your results.
Lower threshold while tuning to see near misses, then filter in code on the head and tail confidence.
Store start and end with each fact, so you can trace it back to its source sentence.

Call GLiNER

Every GLiNER schema type and how to read its result.

Chat Completions API reference

Every field, limit, and error for POST /v1/chat/completions.

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Cookbook: Extract relations between entities

Request

Request schema

Response

Response schema

Act on the answer

Adapt it

Call GLiNER

Chat Completions API reference

​Request

​Request schema

​Response

​Response schema

​Act on the answer

​Adapt it

Call GLiNER

Chat Completions API reference

Request

Request schema

Response

Response schema

Act on the answer

Adapt it