Triple
T197655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Learning (book) |
E4032
|
entity |
| Predicate | coversAlgorithm |
P1393
|
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: [Deep Learning (book), coversAlgorithm, stochastic gradient descent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversAlgorithm Context triple: [Deep Learning (book), coversAlgorithm, stochastic gradient descent]
-
A.
numberOfElementsCovered
Indicates the count of distinct elements that are included or encompassed by a given entity or condition.
-
B.
coversPolicyArea
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
-
C.
includes
chosen
Indicates that one entity contains, encompasses, or has another entity as a part, member, or subset.
-
D.
extendsTo
Indicates that one entity reaches, stretches, or continues its scope, influence, or coverage up to or into another entity.
-
E.
overlies
Indicates that one entity is positioned directly above and covering or resting on another entity, often with partial or complete contact.
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69a25b47481c8190add47c641c977bb9 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.