Triple

T13683077
Position Surface form Disambiguated ID Type / Status
Subject Bailén E328049 entity
Predicate locatedInCountrySubdivision P766 FINISHED
Object Jaén E66602 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: Jaén | Statement: [Bailén, locatedInCountrySubdivision, Jaén]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jaén
Context triple: [Bailén, locatedInCountrySubdivision, Jaén]
  • A. Jaén chosen
    Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
  • B. Jaén
    Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
  • C. Jaen
    Jaen is a landlocked agricultural municipality in the province of Nueva Ecija in the Central Luzon region of the Philippines.
  • D. Jerez de la Frontera
    Jerez de la Frontera is a historic city in southwestern Spain renowned for its sherry wine production, flamenco heritage, and equestrian traditions.
  • E. Écija
    Écija is a historic Andalusian city in southern Spain, renowned for its baroque architecture and extremely hot summer climate that has earned it the nickname "the frying pan of Andalusia."
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc66e75188190a9e82fdc5eb26513 completed April 12, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc315ff848190a6c8cbd5b90db7fc completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 9:53 p.m.