Triple
T2248309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Star Alliance Silver |
E49556
|
entity |
| Predicate | benefitVariability |
P13845
|
FINISHED |
| Object | benefits may differ by airline |
—
|
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: benefits may differ by airline | Statement: [Star Alliance Silver, benefitVariability, benefits may differ by airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitVariability Context triple: [Star Alliance Silver, benefitVariability, benefits may differ by airline]
-
A.
hasVariability
chosen
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
-
B.
usageVariesBy
Indicates that the way something is used differs depending on a specified factor, such as context, user, location, or conditions.
-
C.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
D.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
benefitsCause
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or cause.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0ed5c38819080b45ea398fb59f2 |
completed | March 7, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69abbdb160248190aa75b38f11ad8602 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.