Triple

T10575007
Position Surface form Disambiguated ID Type / Status
Subject Ferrocarrils de la Generalitat de Catalunya urban lines E249584 entity
Predicate hasStationIn P78960 FINISHED
Object Terrassa E188972 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: Terrassa | Statement: [Ferrocarrils de la Generalitat de Catalunya urban lines, hasStationIn, Terrassa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Terrassa
Context triple: [Ferrocarrils de la Generalitat de Catalunya urban lines, hasStationIn, Terrassa]
  • A. Terrassa chosen
    Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
  • B. Canigó
    Canigó is a prominent mountain in the eastern Pyrenees of southern France, culturally significant to Catalan identity and often celebrated in regional literature and tradition.
  • C. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • D. Gironella
    Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
  • E. Valldemossa
    Valldemossa is a picturesque mountain village on the Spanish island of Mallorca, renowned for its historic Carthusian monastery and scenic stone streets.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5280da8bc8190a2a7c90dbc17ea70 completed April 7, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b5d89748190bb398943e4a16e9b completed April 10, 2026, 7:11 p.m.
Created at: April 6, 2026, 12:38 p.m.