Triple

T1190820
Position Surface form Disambiguated ID Type / Status
Subject Balearic Sea E25353 entity
Predicate hasMajorPort P942 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: [Balearic Sea, hasMajorPort, Barcelona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona
Context triple: [Balearic Sea, hasMajorPort, 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. Girona
    Girona is a historic city in northeastern Catalonia, Spain, known for its well-preserved medieval architecture, walled Old Quarter, and prominent cathedral.
  • E. Valencia
    Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
  • 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_69a49427d98881908646d6c63b8cea1e completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd58d8d88190b8d9c9c9de7f4e97 completed March 1, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad718af9a08190b16c7df72d1ef1d3 completed March 8, 2026, 12:54 p.m.
Created at: March 1, 2026, 7:45 p.m.