Triple

T2428694
Position Surface form Disambiguated ID Type / Status
Subject La Rambla E52791 entity
Predicate city P40 FINISHED
Object 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: Barcelona | Statement: [La Rambla, city, Barcelona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona
Context triple: [La Rambla, city, 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. Madrid
    Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
  • C. Madrid
    Madrid is a municipality in the Cundinamarca department of Colombia, located near Bogotá and known for its floriculture and agricultural production.
  • D. Mataró
    Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
  • 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_69ab4959bcc0819083246f9fb10439e3 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc99e1b548190aca9a0ba72a9b0f7 completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef071a6588190bf45a797b4d10f8b completed March 9, 2026, 4:08 p.m.
Created at: March 6, 2026, 9:43 p.m.