Triple

T6325093
Position Surface form Disambiguated ID Type / Status
Subject Iberia Líneas Aéreas de España E141843 entity
Predicate focusCity P164 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: [Iberia Líneas Aéreas de España, focusCity, Barcelona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona
Context triple: [Iberia Líneas Aéreas de España, focusCity, 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. Madrid
    Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
  • D. Madrid
    Madrid is a municipality in the Cundinamarca department of Colombia, located near Bogotá and known for its floriculture and agricultural production.
  • E. Madri
    Madri is a princess from the Mahabharata epic, known as the second wife of King Pandu and the mother of the twins Nakula and Sahadeva.
  • 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_69c008d201748190917e69c41ba3f978 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c064e701ec81908e5eaa0660d01a4b completed March 22, 2026, 9:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e3ba7fb88190b4156fe5c60eef50 completed March 27, 2026, 1:56 a.m.
Created at: March 22, 2026, 4:29 p.m.