Triple
T645530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A fast learning algorithm for deep belief nets |
E11232
|
entity |
| Predicate | optimizationMethod |
P859
|
FINISHED |
| Object | contrastive divergence |
—
|
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: contrastive divergence | Statement: [A fast learning algorithm for deep belief nets, optimizationMethod, contrastive divergence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationMethod Context triple: [A fast learning algorithm for deep belief nets, optimizationMethod, contrastive divergence]
-
A.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
C.
usesComputationMethod
Indicates that an entity performs its processing or decision-making by applying a specified computational method or algorithm.
-
D.
discoveryMethod
Indicates the process, technique, or approach by which something was found, detected, or identified.
-
E.
method
chosen
Indicates the technique, procedure, or process used by an entity to perform an action or achieve a result.
- F. None of above.
Provenance (3 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f19f9a08190b0bf6e19b32427ff |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0a0ab481909871461418a00be7 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.