Triple
T15218040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fashion-MNIST |
E363689
|
entity |
| Predicate | numberOfTestExamples |
P10947
|
FINISHED |
| Object | 10000 |
—
|
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: 10000 | Statement: [Fashion-MNIST, numberOfTestExamples, 10000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTestExamples Context triple: [Fashion-MNIST, numberOfTestExamples, 10000]
-
A.
numberOfTests
chosen
Indicates the quantity of tests associated with or performed in a given context or entity.
-
B.
numberOfTestCaps
Indicates the number of times an individual has appeared in test matches (test caps) for a team or organization.
-
C.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
D.
plannedNumberOfTests
Indicates the total count of tests that are intended or scheduled to be conducted for a given context or period.
-
E.
sampleNumber
Indicates that an entity is identified or associated with a specific sample number within a set of samples.
- 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.