Triple

T10804370
Position Surface form Disambiguated ID Type / Status
Subject Province of Tarragona E254925 entity
Predicate contains P35 FINISHED
Object Reus E388590 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: Reus | Statement: [Province of Tarragona, contains, Reus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reus
Context triple: [Province of Tarragona, contains, Reus]
  • A. Reus chosen
    Reus is a city in Catalonia, Spain, known as the birthplace of architect Antoni Gaudí and for its historic center and vermouth production.
  • B. Benicàssim
    Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
  • C. Lleida
    Lleida is a historic city in western Catalonia, Spain, known for its medieval Seu Vella cathedral and role as a regional agricultural and commercial center.
  • D. Esplugues de Llobregat
    Esplugues de Llobregat is a municipality in the metropolitan area of Barcelona, Catalonia, known for its residential character and proximity to the Catalan capital.
  • E. Montmeló
    Montmeló is a municipality in Catalonia, Spain, best known for hosting the Circuit de Barcelona-Catalunya, a major venue for Formula 1 and MotoGP races.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d73370e7388190885b104fc883456e completed April 9, 2026, 5:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69f78ac420788190b12167aef7436c64 completed May 3, 2026, 5:49 p.m.
Created at: April 8, 2026, 9:18 p.m.