Triple
T15218147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EMNIST |
E363691
|
entity |
| Predicate | hasSubset |
P5797
|
FINISHED |
| Object | EMNIST ByClass |
E363691
|
NE 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: EMNIST ByClass | Statement: [EMNIST, hasSubset, EMNIST ByClass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EMNIST ByClass Context triple: [EMNIST, hasSubset, EMNIST ByClass]
-
A.
EMNIST
chosen
EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character recognition tasks.
-
B.
KMNIST
KMNIST is a benchmark image dataset of handwritten Japanese characters (hiragana) designed as a more complex, drop-in replacement for the original MNIST digit dataset.
-
C.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
D.
Fashion-MNIST
Fashion-MNIST is a popular benchmark dataset of Zalando clothing item images used as a more challenging drop-in replacement for the original MNIST handwritten digits in machine learning research.
-
E.
SVHN
SVHN (Street View House Numbers) is a real-world image dataset of house number digits captured from Google Street View, commonly used for training and evaluating machine learning models in digit recognition tasks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd3159fc81908c05cfbd0bd7e5ac |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 3:11 a.m.