Triple
T22411979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ImageNet Classification with Deep Convolutional Neural Networks |
E554013
|
entity |
| Predicate | numberOfLayersInModel |
P48113
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [ImageNet Classification with Deep Convolutional Neural Networks, numberOfLayersInModel, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLayersInModel Context triple: [ImageNet Classification with Deep Convolutional Neural Networks, numberOfLayersInModel, 8]
-
A.
hasNumberOfWeightLayers
chosen
Indicates the relationship that specifies how many distinct weight layers are present in a given model or structure.
-
B.
numberOfModels
Indicates the quantity or count of models associated with a given entity or context.
-
C.
layerNumber
Indicates the specific position or index of a layer within an ordered stack or layered structure.
-
D.
numberOfLevels
Indicates the total count of hierarchical layers, stages, or floors associated with an entity.
-
E.
hasNumberOfOccupationLayers
Indicates the number of distinct occupation layers or strata associated with an entity, such as levels of use, settlement, or functional roles.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15943dd84819099e77563da470594 |
completed | April 29, 2026, 1:05 a.m. |
| PD | Predicate disambiguation | batch_69e8989495bc81909d2699fce5992e28 |
completed | April 22, 2026, 9:44 a.m. |
Created at: April 16, 2026, 8:46 p.m.