Triple
T3017411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Safar Flyer |
E82369
|
entity |
| Predicate | loyaltyScope |
P37955
|
FINISHED |
| Object | international |
—
|
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: international | Statement: [Safar Flyer, loyaltyScope, international]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loyaltyScope Context triple: [Safar Flyer, loyaltyScope, international]
-
A.
loyaltyDomain
chosen
Indicates a relationship where loyalty, allegiance, or steadfast support is directed toward or governed by a particular domain, context, or sphere of influence.
-
B.
loyaltyIntegration
Indicates the degree to which a loyalty or rewards program is connected, synchronized, or functionally embedded with another system, platform, or service.
-
C.
loyaltyProgramType
Indicates the specific category or kind of loyalty program associated with an entity (such as points-based, tiered, or subscription-based).
-
D.
laterLoyalty
Indicates that one entity becomes loyal to another at a later time, rather than from the outset of their relationship.
-
E.
loyaltyPointName
Indicates the designated name or label assigned to a specific type or category of loyalty points in a loyalty program.
- 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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a90ea64819080620e60bbd6aa24 |
completed | March 8, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69ad961a97188190809dc73430a8eda8 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3 p.m.