Triple
T35125955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qantas Business Rewards |
E1014302
|
entity |
| Predicate | pointsAccrualMechanism |
P80047
|
FINISHED |
| Object | business earns points on employee 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: business earns points on employee flights | Statement: [Qantas Business Rewards, pointsAccrualMechanism, business earns points on employee flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsAccrualMechanism Context triple: [Qantas Business Rewards, pointsAccrualMechanism, business earns points on employee flights]
-
A.
loyaltyMechanism
Indicates a mechanism or process through which loyalty is established, maintained, or reinforced between entities.
-
B.
pointsAwarded
Indicates that a specified number of points has been granted to an entity as a result of some action or event.
-
C.
rewardMechanism
chosen
Indicates a relationship where an entity provides or defines a system of incentives or compensation in response to certain actions, behaviors, or outcomes.
-
D.
pointsEarnedFrom
Indicates the number of points that an entity has received as a result of another specified source, action, or event.
-
E.
penaltyMechanism
Indicates a relationship where a rule, system, or process imposes a negative consequence or sanction in response to certain actions or conditions.
- 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_69f76dd8b6948190aaa32b081816bd94 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ce78b508190955848e133398dc8 |
completed | May 3, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69f78b8f4cc08190b49fccd798cb25d7 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:02 p.m.