Triple

T238222
Position Surface form Disambiguated ID Type / Status
Subject SkyTeam E4870 entity
Predicate hasMember P10 FINISHED
Object Aeroflot E18861 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: Aeroflot | Statement: [SkyTeam, hasMember, Aeroflot]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aeroflot
Context triple: [SkyTeam, hasMember, Aeroflot]
  • A. Aeroflot chosen
    Aeroflot is Russia's largest and flag-carrying airline, headquartered in Moscow and operating an extensive network of domestic and international flights.
  • B. Cubana de Aviación
    Cubana de Aviación is the national flag carrier airline of Cuba, operating domestic and international flights primarily from its Havana hub.
  • C. Russian Railways
    Russian Railways is Russia’s state-owned national railway company, operating the country’s extensive passenger and freight rail network across its vast territory.
  • D. Finnair
    Finnair is the flag carrier and largest airline of Finland, operating an extensive network of domestic and international flights with a strong focus on routes between Europe and Asia.
  • E. Japan Airlines
    Japan Airlines is the flag carrier of Japan, operating an extensive network of domestic and international flights across Asia, Europe, and the Americas.
  • 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_69a257c3d0708190b0871c4269d273e6 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25ccda0ac8190a44f4bebfc0aba67 completed Feb. 28, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69a36735dff881908ed9cefbf6f5e09d completed Feb. 28, 2026, 10:07 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.