Triple
T10524253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asiana Club Diamond Plus |
E248255
|
entity |
| Predicate | benefitsApplyOn |
P79751
|
FINISHED |
| Object | Asiana Airlines-operated flights |
—
|
LITERAL 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: Asiana Airlines-operated flights | Statement: [Asiana Club Diamond Plus, benefitsApplyOn, Asiana Airlines-operated flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitsApplyOn Context triple: [Asiana Club Diamond Plus, benefitsApplyOn, Asiana Airlines-operated flights]
-
A.
benefitAvailableAt
chosen
Indicates that a particular benefit can be obtained, accessed, or used at a specified location, time, or context.
-
B.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
C.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
D.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
E.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
- F. None of above.
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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f4020c8190b78c49da086df757 |
completed | April 7, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69d4fb94fa10819091f585bab4379c6f |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:29 p.m.