Triple
T15218039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fashion-MNIST |
E363689
|
entity |
| Predicate | numberOfTrainingExamples |
P48396
|
FINISHED |
| Object | 60000 |
—
|
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: 60000 | Statement: [Fashion-MNIST, numberOfTrainingExamples, 60000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTrainingExamples Context triple: [Fashion-MNIST, numberOfTrainingExamples, 60000]
-
A.
trainingSetSize
chosen
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
B.
trainingDatasetSize
Indicates the number of data samples or instances used to train a model or system.
-
C.
trainingPopulation
Indicates that one entity serves as the group of individuals or instances used to train or develop another entity, typically a model, system, or process.
-
D.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
E.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076f90c481909989befe031a2cae |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:11 a.m.