Despina/nyt_11

NYT11 (Relation Extraction)

NYT11 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 is derived from the New York Times corpus, with entity pairs distantly aligned to Freebase relations; the test set is manually annotated.

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 the label set (a small set of Freebase-style relation types plus a no-relation / None 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.

Usage

from datasets import load_dataset

ds = load_dataset("Despina/nyt_11")
print(ds["test"][0]["prompt_2_shot"])  # formatted input
print(ds["test"][0]["relation"])       # gold label

Source and licensing

NYT11 is derived from the New York Times Annotated Corpus (LDC2008T19), which is distributed under a Linguistic Data Consortium license. Use of this data is subject to the terms of the underlying NYT/LDC corpus. Please ensure you have the appropriate rights to the source corpus before using this dataset, and cite the original NYT11 / NYT Annotated Corpus resources 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}
}