Triple
T15361498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CIFAR-10 |
E367298
|
entity |
| Predicate | baselineModelType |
P24990
|
FINISHED |
| Object | convolutional neural network |
—
|
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: convolutional neural network | Statement: [CIFAR-10, baselineModelType, convolutional neural network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: baselineModelType Context triple: [CIFAR-10, baselineModelType, convolutional neural network]
-
A.
baselineType
chosen
Indicates the type or category of a baseline used as a reference point for comparison or evaluation.
-
B.
baselineFunction
Indicates the standard or reference function against which other functions, behaviors, or performance are compared.
-
C.
baseType
Indicates that one entity serves as the underlying or parent type from which another entity is derived or specialized.
-
D.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
E.
baseStandard
Indicates that one entity serves as the foundational or reference standard upon which another entity is defined, measured, or evaluated.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4607408190ab281a7f7a8012d3 |
completed | April 16, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:18 a.m.