Triple
T32215668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Computational Learning Theory |
E822917
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | subfield of machine learning |
C24280
|
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: subfield of machine learning Context triple: [Computational Learning Theory, instanceOf, subfield of machine learning]
-
A.
subfield of computer science
chosen
A subfield of computer science is a specialized area of study and research within the broader discipline that focuses on a particular set of concepts, techniques, and applications, such as artificial intelligence, computer graphics, or cybersecurity.
-
B.
machine learning paradigm
A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
-
C.
subfield of cognitive science
A subfield of cognitive science is a specialized area of study that focuses on a particular aspect of how minds represent, process, and use information, often drawing on methods from multiple disciplines such as psychology, neuroscience, linguistics, philosophy, and computer science.
-
D.
machine learning division
The machine learning division is an organizational unit responsible for researching, developing, and deploying data-driven algorithms and models to solve complex problems and enhance products or services.
-
E.
subfield of graph theory
A subfield of graph theory is a specialized area of study within graph theory that focuses on a particular class of graphs, properties, or applications, such as extremal graph theory, spectral graph theory, or topological graph theory.
- 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_69f3490a3bec819097bc58d4731b9d08 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:37 a.m.