Triple

T10644833
Position Surface form Disambiguated ID Type / Status
Subject Bages E250809 entity
Predicate hasNotableTown P14082 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: [Bages, hasNotableTown, Manresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manresa
Context triple: [Bages, hasNotableTown, 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. Arenys de Munt
    Arenys de Munt is a municipality in the Maresme comarca of Catalonia, Spain, known for its Mediterranean setting and involvement in early Catalan independence referendums.
  • 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_69d6aa5a4c4881908f39be6efe5981e5 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6dfd04ca88190ac4fffd13c1f33a8 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69deb07626988190a46d8a54eda156f5 completed April 14, 2026, 9:24 p.m.
Created at: April 8, 2026, 9:05 p.m.