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Entity Linking for Faroese Using Large Language Models with Web Search

Paper · 2026 · Annika Simonsen, Iben Nyholm Debess and Hafsteinn Einarsson

Summary

This work tackles entity linking (EL) for Faroese, a North Germanic language with roughly 70,000 speakers that had no prior EL research or dedicated candidate-generation and disambiguation tools. Instead of a classic multi-stage pipeline, the authors use GPT-5 with built-in web search, accessed through OpenRouter, in a zero-shot setting. The model receives a news text and its already-identified named entities, then returns a Wikipedia page for each one or an empty value. Lookup proceeds in a cascade of three separate calls: Faroese Wikipedia first, then English Wikipedia, then any language edition. A link is accepted only if its page has a Wikidata ID. Mentions come from the existing Faroese NER dataset built on Sosialurin news. Two human annotators checked a 1,010-example subset, and the system reached about 87% precision and recall, well above a plain Wikipedia API search. This sets the first Faroese EL benchmark. It also suggests that LLMs with search can stand in for missing language-specific EL components in other low-resource languages.

Key points

  • Data: 3,000 examples were drawn from the Faroese named-entity recognition corpus released by Snæbjarnarson et al. (2023), yielding 5,584 entity mentions. Of these, 1,010 examples with 1,647 predictions (Person, Location, Organization, Miscellaneous) were annotated by two people. This subset and the code are released as a gold-standard dataset on GitHub (haffi112/faroese-entity-linking).
  • Overall result: Precision is 87.5% and recall 87.3%. Locations perform best (93–95% precision, 92–95% recall). Persons are hardest, with recall of only 72–83%.
  • Knowledge base use: 76.5% of produced links point to Faroese Wikipedia, even though that edition has only about 14,196 articles.
  • Baseline: A Wikipedia API search without an LLM scores F1 = 0.57–0.60.
  • Cross-model check: GPT-5, Gemini 3 Flash and GPT-5.4 Mini reach F1 between 0.74 and 0.87, so the method is not tied to one provider.

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

 Institutions

🎓University of IcelandReykjavik · Iceland5 works · 41 citations🎓University of the Faroe IslandsTórshavn · Faroe Islands2 works · 2 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 473 worksNamed entity recognition · 200 works

Cite

Cite as Annika Simonsen, Iben Nyholm Debess and Hafsteinn Einarsson. 2026. Entity Linking for Faroese Using Large Language Models with Web Search. Proceedings of the Fourth Workshop on the Role of Resources in the Age of Large Language Models (RESOURCEFUL 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2myt23gboqw3

@inproceedings{simonsen2026entity,
  title = {{Entity Linking for Faroese Using Large Language Models with Web Search}},
  author = {Annika Simonsen, Iben Nyholm Debess and Hafsteinn Einarsson},
  booktitle = {Proceedings of the Fourth Workshop on the Role of Resources in the Age of Large Language Models (RESOURCEFUL 2026)},
  year = {2026},
  doi = {10.63317/2myt23gboqw3},
  url = {https://doi.org/10.63317/2myt23gboqw3}
}

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