Triple

T22752209
Position Surface form Disambiguated ID Type / Status
Subject Pedro Ximénez E562732 entity
Predicate usedInRegion P908 FINISHED
Object Montilla-Moriles 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: Montilla-Moriles | Statement: [Pedro Ximénez, usedInRegion, Montilla-Moriles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montilla-Moriles
Context triple: [Pedro Ximénez, usedInRegion, Montilla-Moriles]
  • A. Montilla chosen
    Montilla is a town in the province of Córdoba, Andalusia, Spain, known for its wine production and historical significance.
  • B. Senillosa
    Senillosa is a small town in Argentina’s Neuquén Province, known for its agricultural activities and proximity to the city of Neuquén.
  • C. Peñalver
    Peñalver is a Spanish-language surname of likely toponymic origin, borne by various notable individuals including figures in Latin American history and politics.
  • D. Alhué
    Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
  • E. Mairena del Aljarafe
    Mairena del Aljarafe is a suburban municipality in Andalusia, Spain, located near the city of Seville and known for its residential character and growing services sector.
  • 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_69e24551ec7881909a9c924dbea155f6 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f179baa85881909140f41f2428cc98 completed April 29, 2026, 3:23 a.m.
Created at: April 17, 2026, 3:24 p.m.