Triple

T1879829
Position Surface form Disambiguated ID Type / Status
Subject Santos Dumont Airport E39826 entity
Predicate servesAirlines P12356 FINISHED
Object LATAM Brasil E209312 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: LATAM Brasil | Statement: [Santos Dumont Airport, servesAirlines, LATAM Brasil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LATAM Brasil
Context triple: [Santos Dumont Airport, servesAirlines, LATAM Brasil]
  • A. LATAM Brasil chosen
    LATAM Brasil is a major Brazilian airline and subsidiary of LATAM Airlines Group, operating extensive domestic and international routes across South America and beyond.
  • B. LATAM Colombia
    LATAM Colombia is a Colombian airline that operates domestic and international flights as part of the LATAM Airlines Group.
  • C. LATAM Perú
    LATAM Perú is a major Peruvian airline and subsidiary of LATAM Airlines Group, operating domestic and international flights primarily from Lima.
  • D. Brazil
    Brazil is the largest country in South America, known for its vast Amazon rainforest, diverse culture, and major cities like São Paulo and Rio de Janeiro.
  • E. Brazil
    Brazil is a 1985 dystopian science fiction film known for its darkly satirical portrayal of a bureaucratic, totalitarian society and its distinctive, surreal visual style.
  • 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_69a88633e4fc8190b7eb40463e048ec5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb7c376208190bbf28504f1aac881 completed March 7, 2026, 5:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeae44f6c8190a5924609863030a4 completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:34 p.m.