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    <abstract>We introduce RelEx-PT, a new sentence-level Relation Extraction dataset for Portuguese. Addressing the scarcity of high-quality, controlled resources for the language, RelEx-PT provides a balanced benchmark comprising 18 Wikidata-derived relation types across diverse domains. The dataset is built through a distant supervision pipeline that links Wikidata triples with Portuguese Wikipedia sentences and enhanced by a Natural Language Inference (NLI)-based filtering process, combining scalability with quality assurance. Additionally, we conduct baseline experiments to evaluate the dataset’s applicability across diverse extraction settings, including Relation Classification (RC), Relation Triple Extraction, and Open Information Extraction. These experiments leverage both prompting and fine-tuning strategies using Large Language Models. The results show that RelEx-PT effectively supports a range of extraction paradigms, yielding high performance in RC and competitive results in structured triple generation, while also highlighting key challenges in open-ended extraction.</abstract>
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%0 Conference Proceedings
%T RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset
%A Pinto, Tomás
%A Silva, Catarina
%A Goncalo Oliveira, Hugo
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F pinto-etal-2026-relex
%X We introduce RelEx-PT, a new sentence-level Relation Extraction dataset for Portuguese. Addressing the scarcity of high-quality, controlled resources for the language, RelEx-PT provides a balanced benchmark comprising 18 Wikidata-derived relation types across diverse domains. The dataset is built through a distant supervision pipeline that links Wikidata triples with Portuguese Wikipedia sentences and enhanced by a Natural Language Inference (NLI)-based filtering process, combining scalability with quality assurance. Additionally, we conduct baseline experiments to evaluate the dataset’s applicability across diverse extraction settings, including Relation Classification (RC), Relation Triple Extraction, and Open Information Extraction. These experiments leverage both prompting and fine-tuning strategies using Large Language Models. The results show that RelEx-PT effectively supports a range of extraction paradigms, yielding high performance in RC and competitive results in structured triple generation, while also highlighting key challenges in open-ended extraction.
%R 10.63317/473a5ncxrap4
%U https://aclanthology.org/2026.lrec-1.609/
%U https://doi.org/10.63317/473a5ncxrap4
%P 7681-7691

Markdown (Informal)

[RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset](https://aclanthology.org/2026.lrec-1.609/) (Pinto et al., LREC 2026)

• RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset (Pinto et al., LREC 2026)

ACL

• Tomás Pinto, Catarina Silva, and Hugo Goncalo Oliveira. 2026. RelEx-PT: A Portuguese Sentence-Level Relation Extraction Dataset. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7681–7691, Palma de Mallorca, Spain. ELRA Language Resource Association.