Triple

T5002184
Position Surface form Disambiguated ID Type / Status
Subject AS E112398 entity
Predicate airlineFrequentFlyerProgram P13481 FINISHED
Object Mileage Plan E112400 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: Mileage Plan | Statement: [AS, airlineFrequentFlyerProgram, Mileage Plan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mileage Plan
Context triple: [AS, airlineFrequentFlyerProgram, Mileage Plan]
  • A. Mileage Plan chosen
    Mileage Plan is Alaska Airlines’ loyalty program that rewards members with miles for flights and partner activities, redeemable for travel and other benefits.
  • B. MileagePlus
    MileagePlus is United Airlines’ loyalty program that rewards members with miles and elite benefits for flying and partner activity.
  • C. Lotusmiles
    Lotusmiles is the frequent-flyer loyalty program of Vietnam Airlines, offering members mileage accrual and tiered benefits for their travel with the carrier and its partners.
  • D. LifeMiles
    LifeMiles is the frequent-flyer loyalty program of Avianca, allowing members to earn and redeem miles for flights and related travel benefits.
  • E. Dividend Miles
    Dividend Miles was the frequent-flyer loyalty program of US Airways, allowing members to earn and redeem miles for flights, upgrades, and other travel-related rewards.
  • 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_69bd4433d0b08190877e83959ef40d81 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd72bf0de08190a07419514afc3a06 completed March 20, 2026, 4:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69be92598ff88190b63a589524180272 completed March 21, 2026, 12:43 p.m.
Created at: March 20, 2026, 1:34 p.m.