Triple
T645546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A fast learning algorithm for deep belief nets |
E11232
|
entity |
| Predicate | fineTuningMethod |
P16019
|
FINISHED |
| Object | backpropagation |
—
|
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: backpropagation | Statement: [A fast learning algorithm for deep belief nets, fineTuningMethod, backpropagation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fineTuningMethod Context triple: [A fast learning algorithm for deep belief nets, fineTuningMethod, backpropagation]
-
A.
trainingMethod
chosen
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
-
C.
adaptationType
Indicates the specific kind or category of adaptation that relates one entity to another or to a particular context.
-
D.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
E.
regularization
Indicates the application of a constraint or penalty to a model or function to prevent overfitting and encourage simpler, more generalizable behavior.
- 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.