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.