Triple
T645512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A fast learning algorithm for deep belief nets |
E11232
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | machine learning paper |
C427
|
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: machine learning paper Context triple: [A fast learning algorithm for deep belief nets, instanceOf, machine learning paper]
-
A.
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.
-
B.
machine learning researcher
A machine learning researcher is a specialist who develops, analyzes, and improves algorithms and models that enable computers to learn from data and make predictions or decisions.
-
C.
scientific paper
chosen
A scientific paper is a structured, peer-oriented document that reports original research, methods, analyses, and conclusions to advance knowledge within a specific academic or scientific field.
-
D.
deep learning model
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
E.
computer science book
A computer science book is a structured, written resource that explains concepts, theories, and practices related to computing, algorithms, programming, and information systems.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
Created at: March 1, 2026, 7:36 p.m.