Triple
T33412183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uber Gold |
E855615
|
entity |
| Predicate | statusBenefitsCategory |
P172817
|
FINISHED |
| Object | customer experience enhancement |
—
|
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 experience enhancement | Statement: [Uber Gold, statusBenefitsCategory, customer experience enhancement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusBenefitsCategory Context triple: [Uber Gold, statusBenefitsCategory, customer experience enhancement]
-
A.
benefitsCategory
chosen
Indicates that one entity provides, falls under, or is associated with a particular category of benefits for another entity.
-
B.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
-
C.
benefitsLevel
Indicates the degree or extent to which one entity gains advantages, support, or positive outcomes from another entity or action.
-
D.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
E.
benefitAppliesTo
Indicates that a particular benefit is applicable to, or valid for, a specified entity or 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_69f3496f04a08190804e56ac5098b8e4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff0491409c8190be40f633a58da0b1 |
completed | May 9, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69ff040bb5cc81909534c7eee85d5e90 |
completed | May 9, 2026, 9:53 a.m. |
Created at: May 1, 2026, 1:36 a.m.