Triple

T901405
Position Surface form Disambiguated ID Type / Status
Subject Laura Esquivel E19453 entity
Predicate workLocation P7 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: [Laura Esquivel, workLocation, Mexico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mexico
Context triple: [Laura Esquivel, workLocation, 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. 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.
  • D. Guatemala
    Guatemala is a Central American country known for its Mayan heritage, volcanic landscapes, and vibrant indigenous cultures.
  • E. southern Mexico
    Southern Mexico is a culturally diverse and geographically varied region of Mexico known for its mountainous terrain, indigenous communities, and important archaeological and ecological sites.
  • 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_69a4939e889c8190ac148b3ac1a7f90b completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ad4412408190a6bf8fc7484a5781 completed March 1, 2026, 9:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69a9339e557c81908d4d44f922994ba0 completed March 5, 2026, 7:41 a.m.
Created at: March 1, 2026, 7:39 p.m.