Triple

T2280145
Position Surface form Disambiguated ID Type / Status
Subject Province of Barcelona E51261 entity
Predicate hasMunicipality P847 FINISHED
Object Manresa E186358 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: Manresa | Statement: [Province of Barcelona, hasMunicipality, Manresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manresa
Context triple: [Province of Barcelona, hasMunicipality, Manresa]
  • A. Manresa chosen
    Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
  • 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. Cadaqués
    Cadaqués is a picturesque coastal town on Spain’s Costa Brava, renowned for its whitewashed houses, rocky coves, and association with artist Salvador Dalí.
  • D. Ripoll
    Ripoll is a Spanish surname of Catalan origin, notably borne by Colombian singer Shakira.
  • E. Santanyí
    Santanyí is a picturesque coastal town in southeastern Mallorca, Spain, known for its traditional stone architecture, weekly markets, and nearby sandy coves.
  • 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_69a88b08e4308190bdac9aebcca1c91a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc21ac3d48190abef254e1c3f45e8 completed March 7, 2026, 6:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f11ea248190be3092985edcb29b completed March 9, 2026, 8:04 a.m.
Created at: March 4, 2026, 7:48 p.m.