Triple

T10769166
Position Surface form Disambiguated ID Type / Status
Subject Vallès Oriental E254029 entity
Predicate contains P35 FINISHED
Object Granollers E957442 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: Granollers | Statement: [Vallès Oriental, contains, Granollers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Granollers
Context triple: [Vallès Oriental, contains, Granollers]
  • A. Granollers chosen
    Granollers is a town and municipality in Catalonia, Spain, known as an important commercial and industrial center and the capital of the comarca of Vallès Oriental.
  • B. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • C. Gandria
    Gandria is a picturesque lakeside village in southern Switzerland known for its historic stone houses, narrow alleyways, and scenic setting on the shores of Lake Lugano.
  • D. Guardiola de Berguedà
    Guardiola de Berguedà is a small municipality in the Berguedà comarca of Catalonia, Spain, known for its mountainous surroundings and proximity to the Pyrenees.
  • E. Sant Celoni
    Sant Celoni is a town in Catalonia, Spain, located northeast of Barcelona in the Vallès Oriental comarca, known as a local commercial and transport hub between the Montseny and Montnegre natural areas.
  • 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_69d6aa5f54f4819082d0bbcb6f8797e6 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d7322f9968819098b0ad54b913bfe4 completed April 9, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7304bc684819083ca999f283b0cb3 completed May 3, 2026, 11:23 a.m.
Created at: April 8, 2026, 9:16 p.m.