Triple

T3507199
Position Surface form Disambiguated ID Type / Status
Subject MNIST E74103 entity
Predicate introducedInPublication P309 FINISHED
Object Gradient-based learning applied to document recognition E74104 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: Gradient-based learning applied to document recognition | Statement: [MNIST, introducedInPublication, Gradient-based learning applied to document recognition]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gradient-based learning applied to document recognition
Context triple: [MNIST, introducedInPublication, Gradient-based learning applied to document recognition]
  • A. Gradient-based learning applied to document recognition chosen
    "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.
  • B. “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.
  • C. “A fast learning algorithm for deep belief nets”
    “A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
  • D. Kurzweil OCR (optical character recognition) systems
    Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
  • E. Hopfield networks
    Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbf52bd8819085a2ac5f48cc5c68 completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373e0dc7881909af631182970d132 completed March 13, 2026, 2:18 a.m.
Created at: March 8, 2026, 3:18 p.m.