Triple

T11804639
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Villa María E280713 entity
Predicate locatedIn P40 FINISHED
Object Villa María E720369 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: Villa María | Statement: [Diocese of Villa María, locatedIn, Villa María]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Villa María
Context triple: [Diocese of Villa María, locatedIn, Villa María]
  • A. Villa María chosen
    Villa María is a mid-sized Argentine city in the province of Córdoba, known as an important regional center for dairy production, education, and transportation.
  • B. Villa Carlos Paz
    Villa Carlos Paz is a popular tourist city in central Argentina, known for its scenic lakeside setting in the Sierras de Córdoba and vibrant leisure and entertainment offerings.
  • C. Villa Maria
    Villa Maria is a prominent New Zealand winery renowned for its high-quality wines, particularly Sauvignon Blanc, produced in the Marlborough region.
  • D. Villa La Unión
    Villa La Unión is a town in central Ecuador that serves as the administrative center of Colta Canton in Chimborazo Province.
  • E. Almagro
    Almagro is a traditional middle-class neighborhood in central Buenos Aires, Argentina, known for its historic tango culture, cafes, and densely populated residential streets.
  • 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_69d6ab26aae88190b2489efcb2a24234 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5c8324481909a54852a9bb714e0 completed April 10, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1315b4b1481908106984a1362be89 completed April 28, 2026, 10:14 p.m.
Created at: April 8, 2026, 9:42 p.m.