---
title: "RelEx-PT: A Portuguese Sentence-Level Relation — Pinto 2026"
description: "Portuguese relation extraction lacks clean, controlled benchmarks."
authors: ["Tomás Pinto", "Catarina Silva", "Hugo Goncalo Oliveira"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.609/"
doi: "10.63317/473a5ncxrap4"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pinto-2026-relex-pt-portuguese-sentence-level/"
---
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# RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset

**Authors:** Tomás Pinto, Catarina Silva, Hugo Goncalo Oliveira · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.609/) · [PDF](https://aclanthology.org/2026.lrec-1.609.pdf) · [DOI 10.63317/473a5ncxrap4](https://doi.org/10.63317/473a5ncxrap4) · [OpenAlex](https://openalex.org/W7160047084)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Portuguese relation extraction lacks clean, controlled benchmarks. Existing resources tend to cover narrow domains, are skewed toward a few relations, target multi-sentence contexts, or depend on costly manual annotation. The authors address this with RelEx-PT, a natively Portuguese dataset in which each sentence has two marked entities and one relation, so every item corresponds to a single \<subject, relation, object> triple. Instances are collected automatically by matching Wikidata facts to Portuguese Wikipedia sentences (distant supervision). An NLI model then discards matches where the sentence does not actually support the relation. The authors establish baselines with both prompted and fine-tuned LLMs on three tasks: Open Information Extraction (OpenIE), the extraction of relation triples (RTE), and the classification of relations (RC). For OpenIE, freely generated relations are mapped back to the schema for scoring. Models perform strongly on RC and reasonably on structured triple generation, but open-ended extraction remains hard. The resource gives Portuguese a counterpart to English sentence-level benchmarks such as CoNLL04, and the released pipeline lets others extend it or build new datasets.

## Key points
- RelEx-PT covers 18 Wikidata relation types with perfect class balance. Examples include author (P50), place of birth (P19), instance of (P31) and headquarters location (P159).
- It contains 1,800 instances in total: 1,260 for training (70 per relation) and 540 for testing (30 per relation).
- The construction pipeline combines Wikidata–Wikipedia distant supervision with NLI-based noise filtering, a filtering idea adopted from SRED FM. Both the dataset and the pipeline are on GitHub (TomasCCPinto/RelEx-PT).
- Prior Portuguese resources differ in scope. SMiLER has 22 relations and an uneven distribution, while SRED FM has 400 relations, is cross-sentence and is highly unbalanced. DBpediaRelations-PT is sentence-level with 10 relations, and Bete is limited to the diabetes domain.
- The dataset is released under CC BY-SA 4.0, inherited from Wikipedia text; Wikidata itself is CC0.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pinto2026relex,
  title = {{RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset}},
  author = {Pinto, Tomás and Silva, Catarina and Oliveira, Hugo Goncalo},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/473a5ncxrap4},
  url = {https://doi.org/10.63317/473a5ncxrap4}
}
```

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