Triple
T18629579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Q-learning |
E455376
|
entity |
| Predicate | canUseFunctionApproximation |
P4447
|
FINISHED |
| Object | linear function approximator |
—
|
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: linear function approximator | Statement: [Q-learning, canUseFunctionApproximation, linear function approximator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canUseFunctionApproximation Context triple: [Q-learning, canUseFunctionApproximation, linear function approximator]
-
A.
hasTrainingFunction
Indicates that one entity serves as a training function or mechanism for another entity.
-
B.
hasApproximateUse
Indicates that one entity is used for a purpose that is similar to, but not exactly the same as, the use or function of another entity.
-
C.
hasLogisticFunction
Indicates that one entity is responsible for providing, managing, or supporting the logistics operations or services of another entity.
-
D.
usesFunction
Indicates that one entity employs, invokes, or relies on a particular function to perform an operation or achieve a result.
-
E.
approximationType
chosen
Indicates the specific method or scheme used to approximate a value, function, or relationship in 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54f06f4a081909b64f33814577488 |
completed | April 19, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69e478d4a7948190a4bb9223bb5dddfc |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:46 a.m.