Triple

T7532439
Position Surface form Disambiguated ID Type / Status
Subject Sierra de Tapalpa E178057 entity
Predicate closestTown P3883 FINISHED
Object Tapalpa E670898 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: Tapalpa | Statement: [Sierra de Tapalpa, closestTown, Tapalpa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tapalpa
Context triple: [Sierra de Tapalpa, closestTown, Tapalpa]
  • A. Tapalpa chosen
    Tapalpa is a picturesque mountain town in the Mexican state of Jalisco, known for its colonial architecture, pine forests, and outdoor recreation.
  • B. Tapaz
    Tapaz is a landlocked agricultural municipality in the province of Capiz on Panay Island in the Philippines, known for its rural landscapes and river valleys.
  • C. Zapota
    Zapota is a metro station on Mexico City’s Line 12, serving passengers in the southeastern part of the city.
  • D. Tobalaba
    Tobalaba is a major Santiago Metro station in Chile that serves as an important transfer point between multiple lines in the city’s rapid transit network.
  • E. Tiendesitas
    Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
  • 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_69c69f2acdbc8190b5a8320168c1d0ba completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f84753fc81908bee2013004ef5fb completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c856b369c88190b8a9a196e2166f9f completed March 28, 2026, 10:31 p.m.
Created at: March 27, 2026, 3:47 p.m.