Triple
T22040140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reddit Gold |
E544315
|
entity |
| Predicate | rewardScope |
P146363
|
FINISHED |
| Object | posts |
—
|
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: posts | Statement: [Reddit Gold, rewardScope, posts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rewardScope Context triple: [Reddit Gold, rewardScope, posts]
-
A.
rewardUse
Indicates that one entity grants or provides a reward in response to the use or utilization of another entity.
-
B.
rewardSignal
Indicates that one entity provides a signal representing feedback or incentive (such as a reward or penalty) to guide another entity’s behavior or learning process.
-
C.
rewardUnit
Indicates that one entity serves as the unit or measure in which a reward is quantified or granted to another entity.
-
D.
rewardMechanism
Indicates a relationship where an entity provides or defines a system of incentives or compensation in response to certain actions, behaviors, or outcomes.
-
E.
rewardModel
Indicates a relationship where one entity serves as a model or framework for assigning rewards or evaluating outcomes for another entity or process.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f532b08190be80c5af039b4c29 |
completed | April 28, 2026, 9:34 p.m. |
| PD | Predicate disambiguation | batch_69e6f63b0d048190b241622759aab9de |
completed | April 21, 2026, 3:59 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:25 p.m.