Triple

T17087727
Position Surface form Disambiguated ID Type / Status
Subject Lousã E414642 entity
Predicate hasMunicipalSeat P1474 FINISHED
Object Lousã (town) E414642 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lousã (town) | Statement: [Lousã, hasMunicipalSeat, Lousã (town)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lousã (town)
Context triple: [Lousã, hasMunicipalSeat, Lousã (town)]
  • A. Lousã chosen
    Lousã is a town and municipality in central Portugal known for its surrounding mountains, schist villages, and outdoor activities such as hiking and mountain biking.
  • B. Lousã Municipality
    Lousã Municipality is a Portuguese local administrative region in central Portugal, known for its historic castle, schist villages, and location within the Coimbra District.
  • C. Alcobaça
    Alcobaça is a historic Portuguese city best known for its UNESCO-listed Cistercian monastery, one of the country’s most important medieval monuments.
  • D. Proença-a-Nova
    Proença-a-Nova is a small municipality and town in central Portugal known for its rural landscapes, forested hills, and traditional villages.
  • E. Montemor-o-Novo
    Montemor-o-Novo is a historic town and municipality in Portugal’s Alentejo region, known for its medieval castle ruins and rural landscapes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d886cef44c8190ba56c44b4e863e64 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbe92e488190b947287a968086d5 completed April 18, 2026, 7:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012ee81fd08190a7e1f5958fbe3b97 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:35 a.m.