Triple

T19096801
Position Surface form Disambiguated ID Type / Status
Subject Santa Lucía Air Force Base E467426 entity
Predicate hasIataCode P2569 FINISHED
Object NLU NE NERFINISHED

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: NLU | Statement: [Santa Lucía Air Force Base, hasIataCode, NLU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: NLU
Context triple: [Santa Lucía Air Force Base, hasIataCode, NLU]
  • A. NLU chosen
    NLU is the IATA airport code for Felipe Ángeles International Airport, a major commercial airport serving the Mexico City metropolitan area.
  • B. RMLNLU
    RMLNLU is a premier public law university located in Lucknow, Uttar Pradesh, India, offering undergraduate and postgraduate legal education and research programs.
  • C. NLI
    NLI is the commonly used abbreviation for the National Library of Ireland, the country’s primary institution for preserving and providing access to Ireland’s documentary and literary heritage.
  • D. NLI
    NLI is the vehicle registration code used on license plates for cars registered in Orneta, Poland.
  • E. Google Natural Language API
    Google Natural Language API is a cloud-based service that uses machine learning to analyze and understand text, offering features like sentiment analysis, entity recognition, syntax parsing, and content classification.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd05ac4c8190b1967d8f97f3fb2f completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e369aeac81908913c21f4c234c8e completed April 20, 2026, 8:27 a.m.
Created at: April 10, 2026, 12:04 p.m.