Triple
T14720946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Probably Approximately Correct learning |
E345811
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | computational learning theory framework |
C12985
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: computational learning theory framework Context triple: [Probably Approximately Correct learning, instanceOf, computational learning theory framework]
-
A.
learning theory
chosen
Learning theory is the conceptual framework that explains how knowledge and skills are acquired, processed, retained, and applied through experience, instruction, and practice.
-
B.
machine learning framework
A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning models efficiently.
-
C.
artificial intelligence framework
An artificial intelligence framework is a structured software environment that provides tools, libraries, and interfaces to design, train, deploy, and manage AI and machine learning models efficiently.
-
D.
machine learning book
A machine learning book is a structured, written resource that explains the theories, algorithms, and practical applications of machine learning to help readers understand and apply data-driven modeling techniques.
-
E.
theory in artificial intelligence
A theory in artificial intelligence is a systematic, formal framework that explains, predicts, or guides the design of intelligent behavior in machines by defining underlying principles, models, and assumptions.
- F. None of above.
Provenance (1 batch)
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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 10, 2026, 1:29 a.m.