Triple
T15218041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fashion-MNIST |
E363689
|
entity |
| Predicate | totalNumberOfExamples |
P17874
|
FINISHED |
| Object | 70000 |
—
|
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: 70000 | Statement: [Fashion-MNIST, totalNumberOfExamples, 70000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalNumberOfExamples Context triple: [Fashion-MNIST, totalNumberOfExamples, 70000]
-
A.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
-
B.
numberOfInstances
chosen
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
C.
trainingDatasetSize
Indicates the number of data samples or instances used to train a model or system.
-
D.
numberOfEntities
Indicates the total count of distinct entities involved in or associated with a given context or situation.
-
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.