Triple
T15218006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IDX |
E363688
|
entity |
| Predicate | commonlyDistributedBy |
P1951
|
FINISHED |
| Object | Yann LeCun's MNIST website |
E74103
|
NE FINISHED |
How this triple was built (3 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: Yann LeCun's MNIST website | Statement: [IDX, commonlyDistributedBy, Yann LeCun's MNIST website]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yann LeCun's MNIST website Context triple: [IDX, commonlyDistributedBy, Yann LeCun's MNIST website]
-
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.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
C.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
-
D.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonlyDistributedBy Context triple: [IDX, commonlyDistributedBy, Yann LeCun's MNIST website]
-
A.
distributesFrom
Indicates that something acts as the source or origin from which items, resources, or information are distributed to others.
-
B.
isDistributedThrough
Indicates that something is supplied, circulated, or made available to others via a particular channel, medium, or distribution mechanism.
-
C.
distributesIn
Indicates that one entity allocates or hands out something (e.g., items, resources, information) within or across a specified area, group, or context.
-
D.
distributorType
Indicates the specific category or role of a distributor in relation to the distribution of a product or service.
-
E.
distributor
chosen
Indicates that an entity is responsible for supplying, delivering, or making another entity’s products or materials available to others.
- F. None of above.
Provenance (4 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_69fed345d58c81908a8fd182c0fe7c15 |
completed | May 9, 2026, 6:25 a.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:11 a.m.