@inproceedings{simonsen-etal-2026-entity,
    title = "Entity Linking for {F}aroese Using Large Language Models with Web Search",
    author = "Simonsen, Annika  and
      Debess, Iben Nyholm  and
      Einarsson, Hafsteinn",
    editor = "Morger, Felix  and
      Ilinykh, Nikolai  and
      Scalvini, Barbara  and
      Dobnik, Simon  and
      Dann{\'e}lls, Dana",
    booktitle = "Proceedings of the Fourth Workshop on the Role of Resources in the Age of Large Language Models ({RESOURCEFUL} 2026)",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.resourceful-4.4/",
    doi = "10.63317/2myt23gboqw3",
    pages = "32--43",
    abstract = "Entity linking connects text mentions to knowledge bases. For low-resource languages, entity linking has typically not been a research priority, as named entity recognition and knowledge base creation must first be addressed. We present the first study of entity linking for Faroese, a North Germanic language with approximately 70,000 speakers. Unlike traditional systems that rely on separate candidate retrieval and ranking components, we employ an end-to-end approach using GPT-5 with integrated web search. Our method prompts the model to directly identify and link named entities to Wikipedia pages through a three-tier fallback strategy: Faroese Wikipedia, English Wikipedia, and finally any available Wikipedia. We evaluate our approach on 1,010 manually annotated examples from a Faroese NER dataset, analyzing entity mentions across Person, Location, Organization, and Miscellaneous types. Human evaluation shows our system achieves 87.5{\%} precision and 87.3{\%} recall, with particularly strong performance on locations (93-95{\%} precision, 92-95{\%} recall). Persons are more challenging (86-88{\%} precision, 72-83{\%} recall). The majority of links (76.5{\%}) point to Faroese Wikipedia, demonstrating the model{'}s ability to leverage language-specific knowledge bases. A Wikipedia API search baseline without any LLM achieves F1 = 0.57{--}0.60 on the same evaluation data, confirming that the LLM{'}s contextual reasoning provides substantial gains over simple search. We validate our approach across three models (GPT-5, Gemini{~}3 Flash, GPT-5.4{~}Mini), achieving F1 scores of 0.74{--}0.87 and confirming that the method generalizes across providers. This work establishes initial performance benchmarks for Faroese entity linking and demonstrates the viability of LLM-based approaches for low-resource languages."
}