Triple

T6560917
Position Surface form Disambiguated ID Type / Status
Subject SITEUR E153779 entity
Predicate serviceArea P82 FINISHED
Object Zapopan E228636 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: Zapopan | Statement: [SITEUR, serviceArea, Zapopan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zapopan
Context triple: [SITEUR, serviceArea, Zapopan]
  • A. Zapopan chosen
    Zapopan is a major city in western Mexico known as part of the Guadalajara metropolitan area and for its significant commercial, industrial, and religious importance.
  • B. Tepic
    Tepic is the capital city of the Mexican state of Nayarit, known as a regional commercial and transportation hub in western Mexico.
  • C. Toluca
    Toluca is a major city and the capital of the State of Mexico, known for its high altitude, industrial activity, and proximity to the Nevado de Toluca volcano.
  • D. Tepatitlán de Morelos
    Tepatitlán de Morelos is a prominent city in Mexico known for its agricultural production, religious traditions, and vibrant regional culture.
  • E. Atlixco
    Atlixco is a historic city in the Mexican state of Puebla, known for its vibrant crafts tradition, flower production, and colonial architecture.
  • 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_69c6880cb35881909b763eb0125236b9 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae37a5b0819091692fc5def270b9 completed March 27, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e42523848190b02682e6a640ac05 completed March 27, 2026, 8:10 p.m.
Created at: March 27, 2026, 1:52 p.m.