Triple

T10530913
Position Surface form Disambiguated ID Type / Status
Subject mainland Spain E248437 entity
Predicate contains P35 FINISHED
Object city of Barcelona E9407 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: city of Barcelona | Statement: [mainland Spain, contains, city of Barcelona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Barcelona
Context triple: [mainland Spain, contains, city of Barcelona]
  • A. Barcelona chosen
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • B. Barcelonès
    Barcelonès is a highly urbanized comarca in Catalonia that includes the city of Barcelona and serves as one of the most densely populated areas in Spain.
  • C. municipality of Barcelona
    The municipality of Barcelona is a major coastal city in northeastern Spain, serving as the capital of Catalonia and a leading European center for culture, tourism, and commerce.
  • D. Buñol
    Buñol is a small town in Spain’s Valencia region best known for hosting the annual La Tomatina tomato-throwing festival.
  • E. Girona
    Girona is a historic city in northeastern Catalonia, Spain, known for its well-preserved medieval architecture, walled Old Quarter, and prominent cathedral.
  • 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_69d381c5c7448190bec34bee7ec72bac completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50a16915c8190ac4f3fd5e43c5fed completed April 7, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b1b0de8819089e39ec76e6bdf59 completed April 10, 2026, 7:10 p.m.
Created at: April 6, 2026, 12:30 p.m.