Triple
T3507245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gradient-based learning applied to document recognition |
E74104
|
entity |
| Predicate | learningAlgorithm |
P16019
|
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: [Gradient-based learning applied to document recognition, learningAlgorithm, stochastic gradient descent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningAlgorithm Context triple: [Gradient-based learning applied to document recognition, learningAlgorithm, stochastic gradient descent]
-
A.
machineLearningLibrary
Indicates that one entity is a software library or framework specifically designed to support machine learning tasks for another entity.
-
B.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
C.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
D.
algorithmType
Indicates the specific kind or category of algorithm associated with an entity or process.
-
E.
trainingMethod
chosen
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf52bd8819085a2ac5f48cc5c68 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.