Triple

T3380673
Position Surface form Disambiguated ID Type / Status
Subject LAN Airlines (shares) E71174 entity
Predicate underlyingIssuer P37863 FINISHED
Object LAN Airlines E123604 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: LAN Airlines | Statement: [LAN Airlines (shares), underlyingIssuer, LAN Airlines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LAN Airlines
Context triple: [LAN Airlines (shares), underlyingIssuer, LAN Airlines]
  • A. LAN Airlines chosen
    LAN Airlines was a major Chilean airline that became one of Latin America’s largest carriers before merging into the LATAM Airlines Group.
  • B. ATA Airlines
    ATA Airlines is an Iranian airline that operates domestic and regional flights, using Mehrabad International Airport in Tehran as one of its main bases.
  • C. Sky Airline
    Sky Airline is a Chilean low-cost carrier that operates domestic and regional flights across South America.
  • D. Spring Airlines
    Spring Airlines is a Chinese low-cost carrier headquartered in Shanghai, known for operating budget-friendly domestic and regional flights, particularly within East Asia.
  • E. Flair Airlines
    Flair Airlines is a Canadian ultra-low-cost carrier that operates domestic and select international flights, emphasizing budget-friendly travel options.
  • 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_69ad85a7f80c8190a05e43013f298942 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb5e7c7f48190afb78c311b424c93 completed March 8, 2026, 5:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bc17dd881908fc5fb0d4a23f40f completed March 12, 2026, 11:26 p.m.
Created at: March 8, 2026, 3:14 p.m.