Triple
T18629545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Q-learning |
E455376
|
entity |
| Predicate | isModelFree |
P132456
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Q-learning, isModelFree, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isModelFree Context triple: [Q-learning, isModelFree, true]
-
A.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
B.
isCoordinateFree
Indicates that a property, formulation, or expression does not depend on any particular choice of coordinate system.
-
C.
supportsModelingOf
Indicates that one entity provides the capability or functionality needed to represent, simulate, or model another entity or process.
-
D.
isMemoryless
Indicates that the outcome or state of a process depends only on its present condition and not on any past history or prior events.
-
E.
isModelOf
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
- 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.