Triple
T26116404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Etihad Guest Miles |
E658834
|
entity |
| Predicate | earningRateDependsOn |
P159967
|
FINISHED |
| Object | cabin class |
—
|
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: cabin class | Statement: [Etihad Guest Miles, earningRateDependsOn, cabin class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earningRateDependsOn Context triple: [Etihad Guest Miles, earningRateDependsOn, cabin class]
-
A.
earningRateDependsOn
chosen
Indicates that the rate at which something is earned is determined or influenced by another factor or set of factors.
-
B.
earnOn
Indicates that one entity gains income, profit, or returns as a result of another entity or activity.
-
C.
learningCurve
Indicates how the difficulty or required effort to acquire proficiency in a task or system changes as experience or practice increases.
-
D.
usesLearningRateParameter
Indicates that an entity employs a specific learning rate parameter when performing a learning or optimization process.
-
E.
regulatesTrainingAccordingTo
Indicates that one entity sets or enforces rules, standards, or guidelines governing how training is conducted in relation to another entity or context.
- 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_69ee5bc20298819099a42be042eb2349 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60ac86d908190bf582de7891fb4b3 |
completed | May 2, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69f5f7fd90fc81909055b211368f9139 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 8:05 p.m.