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.