Triple
T18629560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Q-learning |
E455376
|
entity |
| Predicate | usesLearningRateParameter |
P132458
|
FINISHED |
| Object | alpha |
—
|
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: alpha | Statement: [Q-learning, usesLearningRateParameter, alpha]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLearningRateParameter Context triple: [Q-learning, usesLearningRateParameter, alpha]
-
A.
typicalDefaultLearningRate
Indicates the standard or commonly used learning rate value typically applied by default in a learning or optimization process.
-
B.
parameterLearning
Indicates a process or relationship in which parameters of a model, system, or function are adjusted or inferred—typically from data—to improve performance or fit.
-
C.
usesTrainingStrategy
Indicates that one entity applies or follows a particular training strategy in carrying out its learning or optimization process.
-
D.
usesLearningMechanism
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
E.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning 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_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. |
| PDg | Predicate description generation | batch_69e485f5d1588190b44f31cbc54c0a9d |
completed | April 19, 2026, 7:36 a.m. |
Created at: April 10, 2026, 11:46 a.m.