Triple

T7375862
Position Surface form Disambiguated ID Type / Status
Subject Ciénega region E170120 entity
Predicate hasCity P316 FINISHED
Object Ayotlán E683124 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: Ayotlán | Statement: [Ciénega region, hasCity, Ayotlán]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ayotlán
Context triple: [Ciénega region, hasCity, Ayotlán]
  • A. Ayotlán chosen
    Ayotlán is a municipality and town in the Los Altos region of the Mexican state of Jalisco, known for its agricultural traditions and regional culture.
  • B. Poncitlán
    Poncitlán is a municipality and town in the Mexican state of Jalisco, known for its agricultural activity and proximity to Lake Chapala.
  • C. Apatzingán
    Apatzingán is a city in the Mexican state of Michoacán, historically notable as a key site in the country’s early independence movement.
  • D. Ocotlán
    Ocotlán is a city in the Mexican state of Jalisco, known for its furniture industry, religious traditions, and location near Lake Chapala.
  • E. Tultitlán
    Tultitlán is a municipality in the State of Mexico within the Greater Mexico City metropolitan area, known for its dense urban development and industrial activity.
  • 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_69c68a5bfaac81909ce7f001dfb70c76 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f1a780f88190abf11994e307b6ad completed March 27, 2026, 9:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c90092f9ac8190a53d65cfdeafca29 completed March 29, 2026, 10:36 a.m.
Created at: March 27, 2026, 3:07 p.m.