Triple

T2414491
Position Surface form Disambiguated ID Type / Status
Subject Polytechnic University of Catalonia E52270 entity
Predicate hasCampusIn P4623 FINISHED
Object Terrassa E188972 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: Terrassa | Statement: [Polytechnic University of Catalonia, hasCampusIn, Terrassa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terrassa
Context triple: [Polytechnic University of Catalonia, hasCampusIn, Terrassa]
  • A. Terrassa chosen
    Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
  • B. Valldemossa
    Valldemossa is a picturesque mountain village on the Spanish island of Mallorca, renowned for its historic Carthusian monastery and scenic stone streets.
  • C. Manresa
    Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
  • D. Es Trenc
    Es Trenc is a famous natural beach on the southern coast of Mallorca, known for its long stretch of white sand and clear turquoise waters.
  • E. Santpedor
    Santpedor is a small town in Catalonia, Spain, best known internationally as the birthplace of football manager Pep Guardiola.
  • 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_69ab495622948190bc6bc6e4cddaf645 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc94bd7ec81909f5b4a16a406165b completed March 7, 2026, 6:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf4748d0819088aec3bc14dc5519 completed March 9, 2026, 12:38 p.m.
Created at: March 6, 2026, 9:41 p.m.