Triple
T15313824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Very Deep Convolutional Networks for Large-Scale Image Recognition |
E366102
|
entity |
| Predicate | datasetSize |
P48396
|
FINISHED |
| Object | over one million training images |
—
|
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: over one million training images | Statement: [Very Deep Convolutional Networks for Large-Scale Image Recognition, datasetSize, over one million training images]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: datasetSize Context triple: [Very Deep Convolutional Networks for Large-Scale Image Recognition, datasetSize, over one million training images]
-
A.
trainingDatasetSize
Indicates the number of data samples or instances used to train a model or system.
-
B.
trainingSetSize
chosen
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
C.
sampleSize
Indicates the number of units, observations, or instances included in a particular study, experiment, or dataset.
-
D.
distributionSize
Indicates the quantity or scale of items or units included in a particular distribution.
-
E.
collectionSize
Indicates the total number of items contained within a specified collection.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd050108190a584543cb93943a4 |
completed | April 16, 2026, 1:39 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.