Triple

T1959662
Position Surface form Disambiguated ID Type / Status
Subject Aeropuertos y Servicios Auxiliares E42351 entity
Predicate hasAbbreviation P43 FINISHED
Object ASA E219284 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: ASA | Statement: [Aeropuertos y Servicios Auxiliares, hasAbbreviation, ASA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ASA
Context triple: [Aeropuertos y Servicios Auxiliares, hasAbbreviation, ASA]
  • A. ASA chosen
    ASA is the acronym for Aeropuertos y Servicios Auxiliares, the Mexican government agency responsible for operating and managing numerous airports and providing auxiliary aviation services in Mexico.
  • B. ASA
    ASA is an abbreviation commonly used to refer to the Assistant Secretary of the Army, a senior civilian official in the United States Department of the Army responsible for high-level policy and oversight.
  • C. ASA
    ASA is the ICAO airline designator used to identify Alaska Airlines in international aviation operations and communications.
  • D. ASA
    ASA is the commonly used abbreviation for the Academy of Sciences of Albania, the country’s leading scientific research and advisory institution.
  • E. ASA
    ASA is a standards organization that played a key role in formalizing technical specifications such as the ASCII character encoding.
  • 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_69a8870eea088190a38781990812a9bc completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb37f737881908130bb828affcaa2 completed March 7, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae031ef4e48190af93dfd6f33184d3 completed March 8, 2026, 11:15 p.m.
Created at: March 4, 2026, 7:36 p.m.