Triple
T4223194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flying Club |
E94389
|
entity |
| Predicate | earnPointsFrom |
P9554
|
FINISHED |
| Object | Virgin Atlantic flights |
—
|
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: Virgin Atlantic flights | Statement: [Flying Club, earnPointsFrom, Virgin Atlantic flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earnPointsFrom Context triple: [Flying Club, earnPointsFrom, Virgin Atlantic flights]
-
A.
loyaltyProgramEarnings
chosen
Indicates the amount or details of rewards or benefits a participant accrues within a loyalty or rewards program.
-
B.
earnsMorePointsThan
Indicates that one entity receives a greater number of points than another entity in a given context or comparison.
-
C.
rewardUse
Indicates that one entity grants or provides a reward in response to the use or utilization of another entity.
-
D.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
E.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
- 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e4bf6088190926b982039a12079 |
completed | March 12, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69b347f1d7b48190bd8974c03c7dc937 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:04 p.m.