Triple
T591887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LeNet |
E17289
|
entity |
| Predicate | activationFunction |
P16017
|
FINISHED |
| Object | sigmoid function |
—
|
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: sigmoid function | Statement: [LeNet, activationFunction, sigmoid function]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activationFunction Context triple: [LeNet, activationFunction, sigmoid function]
-
A.
assignedFunction
Indicates that a specific role, duty, or function has been formally allocated to an entity.
-
B.
functionHypothesis
Indicates that an entity is proposed or assumed to serve as a function or functional explanation for another entity or phenomenon.
-
C.
kernelType
Indicates the specific kind or category of kernel associated with or used by an entity.
-
D.
secondaryFunction
Indicates that an entity has an additional, supporting role or purpose beyond its primary function.
-
E.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
- F. None of above. chosen
Provenance (4 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bbaf53081908eed240bed09f63b |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494cc13988190892ca10bd7ae9f09 |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985ada988190aaea628a9b55bca4 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:33 p.m.