Triple
T14517785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akasa Air |
E340567
|
entity |
| Predicate | loyaltyApproach |
P73992
|
FINISHED |
| Object | value-focused rather than full-service perks |
—
|
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: value-focused rather than full-service perks | Statement: [Akasa Air, loyaltyApproach, value-focused rather than full-service perks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loyaltyApproach Context triple: [Akasa Air, loyaltyApproach, value-focused rather than full-service perks]
-
A.
loyaltyDimension
Indicates the degree or aspect of loyalty characterizing the relationship between entities.
-
B.
loyaltyProgramFocus
Indicates that an entity’s primary emphasis or activity is centered on managing, offering, or optimizing a loyalty or rewards program.
-
C.
loyaltyMechanism
chosen
Indicates a mechanism or process through which loyalty is established, maintained, or reinforced between entities.
-
D.
loyaltyIncentive
Indicates a relationship where benefits or rewards are provided to encourage or recognize continued commitment or repeat engagement.
-
E.
loyaltyReason
Indicates the reason or motivation behind one entity’s loyalty or allegiance to another.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a6f50208190b687b505f5cd1aa2 |
completed | April 14, 2026, 7:50 p.m. |
| PD | Predicate disambiguation | batch_69de5c518fc08190a6ce4d8be05c4c5d |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:22 a.m.