Triple

T9910658
Position Surface form Disambiguated ID Type / Status
Subject Santpedor E185131 entity
Predicate locatedNear P294 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: [Santpedor, locatedNear, Manresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manresa
Context triple: [Santpedor, locatedNear, 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. Begur
    Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
  • C. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • D. Empuriabrava
    Empuriabrava is a large seaside resort on Spain’s Costa Brava, famous for its extensive network of navigable canals and marina-style residential development.
  • E. Cerdanya
    Cerdanya is a historic region in the eastern Pyrenees, now divided between France and Spain, known for its mountainous landscapes and Catalan cultural heritage.
  • 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_69ca8296165881908ca4750701af1f29 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb512a26881908eb72a21ffb1efef completed April 2, 2026, 12:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71c3fa4b88190be4ce7b64335a99f completed April 9, 2026, 3:25 a.m.
Created at: March 30, 2026, 8:41 p.m.