Triple

T10769169
Position Surface form Disambiguated ID Type / Status
Subject Vallès Oriental E254029 entity
Predicate contains P35 FINISHED
Object Cardedeu E909737 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: Cardedeu | Statement: [Vallès Oriental, contains, Cardedeu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cardedeu
Context triple: [Vallès Oriental, contains, Cardedeu]
  • A. Cardedeu chosen
    Cardedeu is a municipality in the province of Barcelona, Catalonia, known for its modernist architecture and location near the Montseny Natural Park.
  • B. Segarra
    Segarra is a historical inland comarca in Catalonia, Spain, known for its rolling cereal plains, medieval castles, and the town of Cervera as its capital.
  • C. Berguedà
    Berguedà is a mountainous comarca in central Catalonia, Spain, known for its Pyrenean landscapes, rural villages, and natural parks.
  • D. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • E. 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.
  • 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_69e5d2d717888190b7e455d006d01033 completed April 20, 2026, 7:16 a.m.
Created at: April 8, 2026, 9:16 p.m.