Datasets:

Despina
/

gids

annotations_creators:
  - other
language:
  - en
language_creators:
  - found
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100k
source_datasets:
  - extended|other
tags:
  - relation extraction
  - information-extraction
dataset_info:
  features:
    - name: text
      dtype: string
    - name: entity1
      dtype: string
    - name: entity2
      dtype: string
    - name: relation
      dtype: string
    - name: prompt_0_shot
      dtype: string
    - name: prompt_2_shot
      dtype: string
    - name: prompt_5_shot
      dtype: string
  splits:
    - name: train
      num_bytes: 67454455
      num_examples: 11297
    - name: validation
      num_bytes: 11182197
      num_examples: 1864
    - name: test
      num_bytes: 33938174
      num_examples: 5663
  download_size: 57694394
  dataset_size: 112574826
task_categories:
  - text-classification
  - text-generation
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

GIDS (Relation Extraction)

GIDS (Google-IISc Distant Supervision) is a general-domain sentence-level relation extraction (RE) dataset. Each example pairs a sentence (mentioning a head and tail entity) with the relation that holds between the two entities. It was built by extending the human-judged Google Relation Extraction corpus with additional distantly-supervised sentences, and is designed so that every entity pair has at least one sentence that expresses the target relation (reducing the noise typical of distant supervision).

This copy is packaged for the paper "Sub-Billion, Super-Frontier: Fine-Tuned Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction" (Christou & Tsoumakas, 2026) arXiv:2606.22606. Rows are pre-formatted as instruction prompts so the dataset can be used directly for prompt-conditioned fine-tuning and evaluation.

Dataset structure

Splits: train, validation, test.

Column	Description
`prompt_0_shot`	Zero-shot instruction prompt (task instructions + the input sentence).
`prompt_2_shot`	Same prompt with 2 in-context demonstrations prepended.
`prompt_5_shot`	Same prompt with 5 in-context demonstrations prepended.
`relation`	Gold relation label (the target/completion).

The relation column holds a small set of Freebase-style relation types (e.g. birth/death place and education-related relations) plus a no-relation / NA label; inspect the column for the exact set. The three prompt_* columns are alternative renderings of the same example at different shot counts, so pick one shot setting per experiment rather than concatenating them.

An example of 'train' looks as follows:

{
  "text": "War as appropriate. Private Alfred James_Smurthwaite Sample. 26614. 2nd Battalion Yorkshire Regiment. Son of Edward James Sample, of North_Ormesby , Yorks. Died 2 April 1917. Aged 29. Born Ormesby, Enlisted Middlesbrough. Buried BUCQUOY ROAD CEMETERY, FICHEUX. Not listed on the Middlesbrough War Memorial Private Frederick Scott. 46449. 4th Battalion Yorkshire Regiment. Son of William and Maria Scott, of 25, Aspinall St., Heywood, Lancs. Born at West Hartlepool. Died 27 May 1918. Aged 24.", 
  "entity1": "Alfred", 
  "entity2": "Yorkshire", 
  "entity1Type": "PERSON", 
  "entity2Type": "LOCATION", 
  "relation": 'fight_at'
}

Data Splits

	Train	Dev	Test
GIDS	11297	1864	5663

Usage

from datasets import load_dataset

ds = load_dataset("Despina/gids")
print(ds["test"][0]["prompt_2_shot"])  # formatted input
print(ds["test"][0]["relation"])       # gold label

Source and licensing

GIDS is derived from the Google Relation Extraction corpus (distantly extended over Wikipedia text) and was introduced by Jat, Khandelwal & Talukdar (2018). It is available for research use; please respect the terms of the underlying Google Relation Extraction corpus and cite the original GIDS resource alongside the paper below.

Citation

If you use this dataset, please cite our paper:

@article{christou2026subbillion,
  title        = {Sub-Billion, Super-Frontier: Small Language Models Rival
                  Zero-Shot Frontier LLMs on General and Literary Relation Extraction},
  author       = {Christou, Despina and Tsoumakas, Grigorios},
  journal      = {arXiv preprint arXiv:2606.22606},
  year         = {2026},
  url          = {https://arxiv.org/abs/2606.22606}
}