Triple

T2262828
Position Surface form Disambiguated ID Type / Status
Subject TAR Aerolíneas E50075 entity
Predicate focusCity P164 FINISHED
Object Villahermosa E233929 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: Villahermosa | Statement: [TAR Aerolíneas, focusCity, Villahermosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Villahermosa
Context triple: [TAR Aerolíneas, focusCity, Villahermosa]
  • A. Villahermosa chosen
    Villahermosa is the capital and largest city of the Mexican state of Tabasco, known as a regional hub for oil, commerce, and culture in southeastern Mexico.
  • B. Xalapa
    Xalapa is a city in eastern Mexico known as the capital and cultural center of the state of Veracruz.
  • C. Matamoros
    Matamoros is a Mexican border city in the state of Tamaulipas, located directly across the Rio Grande from Brownsville, Texas, and known as an important hub for trade and manufacturing.
  • D. Culiacán
    Culiacán is the largest city and main economic and cultural center of the Mexican state of Sinaloa.
  • E. Torreón
    Torreón is a major industrial and commercial city in northern Mexico known for its manufacturing, agriculture, and role as an economic hub in the state of Coahuila.
  • 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_69a88b01e0048190ba96431b5f990ba9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc18be8308190abc4a59d37dfd93a completed March 7, 2026, 6:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f08f2b88190bba173acb132a160 completed March 9, 2026, 8:04 a.m.
Created at: March 4, 2026, 7:48 p.m.