Triple
T22411998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ImageNet Classification with Deep Convolutional Neural Networks |
E554013
|
entity |
| Predicate | usesOptimizationAlgorithm |
P56342
|
FINISHED |
| Object | stochastic gradient descent |
—
|
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: stochastic gradient descent | Statement: [ImageNet Classification with Deep Convolutional Neural Networks, usesOptimizationAlgorithm, stochastic gradient descent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesOptimizationAlgorithm Context triple: [ImageNet Classification with Deep Convolutional Neural Networks, usesOptimizationAlgorithm, stochastic gradient descent]
-
A.
supportsOptimizationAlgorithm
chosen
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
B.
canBeOptimizedFor
Indicates that one entity is capable of being improved or adjusted to perform better with respect to another specified criterion, context, or target.
-
C.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
D.
optimizationSolver
Indicates a relationship where a solver entity is used to compute an optimal solution for a given optimization problem or task.
-
E.
optimizationDomain
Indicates the domain, field, or context within which an optimization process or optimization-related activity is applied.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15943dd84819099e77563da470594 |
completed | April 29, 2026, 1:05 a.m. |
| PD | Predicate disambiguation | batch_69e8989495bc81909d2699fce5992e28 |
completed | April 22, 2026, 9:44 a.m. |
Created at: April 16, 2026, 8:46 p.m.