Triple
T3017399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Safar Flyer |
E82369
|
entity |
| Predicate | statusBenefit |
P2188
|
FINISHED |
| Object | bonus miles |
—
|
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: bonus miles | Statement: [Safar Flyer, statusBenefit, bonus miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusBenefit Context triple: [Safar Flyer, statusBenefit, bonus miles]
-
A.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
B.
benefitTaxStatus
Indicates that an entity’s tax-related status or classification provides a financial or regulatory benefit in the context of the relationship.
-
C.
statusIndicates
Indicates that a particular status value conveys or reflects the current condition, state, or situation of an entity or process.
-
D.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
status
Indicates the current condition, state, or standing of an entity within a given context.
- 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.