Triple
T3507311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlexNet |
E74105
|
entity |
| Predicate | usesNonlinearity |
P16017
|
FINISHED |
| Object | rectified linear units |
—
|
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: rectified linear units | Statement: [AlexNet, usesNonlinearity, rectified linear units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNonlinearity Context triple: [AlexNet, usesNonlinearity, rectified linear units]
-
A.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
B.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
-
C.
usesFunction
Indicates that one entity employs, invokes, or relies on a particular function to perform an operation or achieve a result.
-
D.
isLinear
Indicates that a relationship, function, or structure preserves linearity, typically meaning it satisfies additivity and homogeneity (or forms a straight-line dependence between variables).
-
E.
activationFunction
chosen
Indicates the specific mathematical transformation applied to a neuron's input to produce its output in a computational or neural model.
- 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_69adbc0b635c81909bc95ba2562d8f94 |
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.