Triple

T21013956
Position Surface form Disambiguated ID Type / Status
Subject Spanish AVE E517620 entity
Predicate connectsCityPair P12329 FINISHED
Object Madrid–Barcelona NE NERFINISHED

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: Madrid–Barcelona | Statement: [Spanish AVE, connectsCityPair, Madrid–Barcelona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrid–Barcelona
Context triple: [Spanish AVE, connectsCityPair, Madrid–Barcelona]
  • A. Madrid–Barcelona chosen
    Madrid–Barcelona is a major high-speed rail corridor in Spain connecting the country’s capital with its leading Catalan metropolis.
  • B. Madrid–Valencia
    Madrid–Valencia is a major high-speed rail corridor in Spain linking the capital Madrid with the Mediterranean coastal city of Valencia.
  • C. Barcelona
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • D. Barcelona
    Barcelona is a coastal municipality in the province of Sorsogon in the Bicol Region of the Philippines, known for its historic church and scenic seaside views.
  • E. Rome–Barcelona
    Rome–Barcelona was an international air route connecting the Italian capital with the major Spanish Mediterranean city, historically served by the Italian airline Ala Littoria.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b50192308190a284fcc89dd23a49 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e6fc5764188190829de6f5abd6e00f completed April 21, 2026, 4:25 a.m.
Created at: April 16, 2026, 1:54 p.m.