Triple

T10645081
Position Surface form Disambiguated ID Type / Status
Subject Metropolitan Area of Barcelona E250814 entity
Predicate containsMunicipality P852 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: [Metropolitan Area of Barcelona, containsMunicipality, Manresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manresa
Context triple: [Metropolitan Area of Barcelona, containsMunicipality, 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. Vilassar de Mar
    Vilassar de Mar is a coastal town and municipality on the Mediterranean in the Maresme comarca of Catalonia, Spain, known for its beaches and residential character.
  • D. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • E. 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.
  • 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_69d6dfe120908190ab91c38d57133739 completed April 8, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69e46262a8f88190869c2161c3b10a19 completed April 19, 2026, 5:04 a.m.
Created at: April 8, 2026, 9:05 p.m.