Triple
T645539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A fast learning algorithm for deep belief nets |
E11232
|
entity |
| Predicate | evaluationDataset |
P16906
|
FINISHED |
| Object | MNIST |
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: MNIST | Statement: [A fast learning algorithm for deep belief nets, evaluationDataset, MNIST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MNIST Context triple: [A fast learning algorithm for deep belief nets, evaluationDataset, 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
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
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.
-
E.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
- 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: evaluationDataset Context triple: [A fast learning algorithm for deep belief nets, evaluationDataset, MNIST]
-
A.
dataPortal
Indicates that an entity serves as or is associated with an online interface or gateway through which data can be accessed, managed, or distributed.
-
B.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
-
C.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
D.
dataScienceLibrary
Indicates a relationship where a software library is specifically designed for performing data science tasks, such as data processing, analysis, and modeling.
-
E.
evaluationCycle
Indicates the recurring period or sequence in which evaluations or assessments are conducted and reviewed.
- F. None of above. chosen
Provenance (5 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f19f9a08190b0bf6e19b32427ff |
completed | March 1, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a57b5a0c0c81909aa3339d7ba62a0d |
completed | March 2, 2026, 11:58 a.m. |
| PD | Predicate disambiguation | batch_69a49d0a0ab481909871461418a00be7 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49dc0e6a08190b81d82a6f2571c41 |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.