Triple
T15218113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KMNIST |
E363690
|
entity |
| Predicate | inspiredBy |
P9
|
FINISHED |
| Object | MNIST |
E74103
|
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: MNIST | Statement: [KMNIST, inspiredBy, MNIST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MNIST Context triple: [KMNIST, inspiredBy, MNIST]
-
A.
MNIST
chosen
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.
-
B.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
C.
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.
-
D.
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.
-
E.
EMNIST
EMNIST is an extended handwritten character dataset that builds on MNIST by including both digits and letters for more comprehensive character 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_69ff2ce894548190a19bab33285ad165 |
completed | May 9, 2026, 12:47 p.m. |
Created at: April 10, 2026, 3:11 a.m.