Triple

T1125600
Position Surface form Disambiguated ID Type / Status
Subject Tucson E24712 entity
Predicate wasPartOf P35 FINISHED
Object Mexico E346 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: Mexico | Statement: [Tucson, wasPartOf, Mexico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mexico
Context triple: [Tucson, wasPartOf, Mexico]
  • A. Mexico chosen
    Mexico is a large North American country known for its rich pre-Columbian and colonial history, diverse cultures, and influential cuisine and arts.
  • B. State of Mexico
    The State of Mexico is a populous federal entity in central Mexico that surrounds much of Mexico City and is a major political, economic, and industrial hub of the country.
  • C. Guatimozín
    Guatimozín is another name for Cuauhtémoc, the last Aztec emperor who led the defense of Tenochtitlan against the Spanish conquest.
  • D. MEX
    MEX is the IATA airport code for Mexico City International Airport, the main international gateway serving Mexico City and one of the busiest airports in Latin America.
  • E. Guatemala
    Guatemala is a Central American country known for its Mayan heritage, volcanic landscapes, and vibrant indigenous cultures.
  • 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_69a4940712c88190aa244f3fc6070a65 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bbdaf2d4819086f480f69da127f9 completed March 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac6f0c7d4c819082672393b0542af4 completed March 7, 2026, 6:31 p.m.
Created at: March 1, 2026, 7:44 p.m.