Triple

T7312535
Position Surface form Disambiguated ID Type / Status
Subject Air France Flying Blue Gold E168128 entity
Predicate partOf P40 FINISHED
Object Flying Blue E93839 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: Flying Blue | Statement: [Air France Flying Blue Gold, partOf, Flying Blue]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flying Blue
Context triple: [Air France Flying Blue Gold, partOf, Flying Blue]
  • A. Flying Blue chosen
    Flying Blue is the joint frequent flyer loyalty program of Air France–KLM and partner airlines, offering members miles, elite status levels, and travel-related rewards.
  • B. Blue Air
    Blue Air is a Romanian low-cost airline that operated scheduled passenger flights across Europe.
  • C. Flying Finn
    Flying Finn is the famous nickname of Finnish middle- and long-distance runner Paavo Nurmi, one of the most dominant athletes in Olympic history.
  • D. Airblue
    Airblue is a Pakistani low-cost airline that operates domestic and international flights, with a primary base at Jinnah International Airport in Karachi.
  • E. Delta One
    Delta One is Delta Air Lines’ flagship international business-class cabin, featuring lie-flat seats, premium dining, and enhanced amenities for long-haul travelers.
  • 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_69c6888d8e3c81909db79714903baf31 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ec02319c819096d25e3683943886 completed March 27, 2026, 8:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e56b4178819087341903a168440b completed March 28, 2026, 2:27 p.m.
Created at: March 27, 2026, 3:02 p.m.