Triple
T4518792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aeroflot Bonus |
E103214
|
entity |
| Predicate | loyaltyGoal |
P57185
|
FINISHED |
| Object | customer retention |
—
|
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: customer retention | Statement: [Aeroflot Bonus, loyaltyGoal, customer retention]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loyaltyGoal Context triple: [Aeroflot Bonus, loyaltyGoal, customer retention]
-
A.
loyaltyIntegration
Indicates the degree to which a loyalty or rewards program is connected, synchronized, or functionally embedded with another system, platform, or service.
-
B.
loyaltyDomain
Indicates a relationship where loyalty, allegiance, or steadfast support is directed toward or governed by a particular domain, context, or sphere of influence.
-
C.
laterLoyalty
Indicates that one entity becomes loyal to another at a later time, rather than from the outset of their relationship.
-
D.
legacyGoal
Indicates that an entity has a long-term, enduring objective or impact it aims to leave behind beyond its immediate actions or existence.
-
E.
loyaltyProgramType
Indicates the specific category or kind of loyalty program associated with an entity (such as points-based, tiered, or subscription-based).
- F. None of above. chosen
Provenance (4 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_69bd43dba59881908cf59b31df8c7ae1 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd57465a10819086866e29f7a6eb02 |
completed | March 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69bd521abea48190b3e758a1f98dd55e |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56b3e4c88190a7ade3d0ed0ab606 |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:02 p.m.