Triple

T15567533
Position Surface form Disambiguated ID Type / Status
Subject Belmonte (Portugal) E374153 entity
Predicate nearbyCity P350 FINISHED
Object Covilhã E337094 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: Covilhã | Statement: [Belmonte (Portugal), nearbyCity, Covilhã]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Covilhã
Context triple: [Belmonte (Portugal), nearbyCity, Covilhã]
  • A. Covilhã chosen
    Covilhã is a city in central Portugal, historically known for its textile industry and as a gateway to the Serra da Estrela mountain range.
  • B. 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.
  • C. Lamego
    Lamego is a historic city in northern Portugal known for its baroque Sanctuary of Our Lady of Remedies and its location in the Douro wine region.
  • D. Lourinhã
    Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
  • E. Sernancelhe
    Sernancelhe is a municipality in northern Portugal known for its historic granite architecture, religious heritage, and scenic 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dde90b081908284d9258d4462e3 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a001799fbac8190b75a48a8c63e3381 completed May 10, 2026, 5:28 a.m.
Created at: April 10, 2026, 4:10 a.m.