Triple

T3507251
Position Surface form Disambiguated ID Type / Status
Subject Gradient-based learning applied to document recognition E74104 entity
Predicate architectureName P48402 FINISHED
Object LeNet E17289 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: LeNet | Statement: [Gradient-based learning applied to document recognition, architectureName, LeNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LeNet
Context triple: [Gradient-based learning applied to document recognition, architectureName, LeNet]
  • A. LeNet chosen
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • B. AlexNet
    AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
  • C. VGG
    VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
  • D. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • E. Inception architecture
    The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
  • 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: architectureName
Context triple: [Gradient-based learning applied to document recognition, architectureName, LeNet]
  • A. architectureType
    Indicates the specific style or category of architecture that characterizes or defines an entity.
  • B. architecturalConcept
    Indicates that one entity represents or embodies an architectural concept in relation to another entity.
  • C. architecturalPlanner
    Indicates a relationship where an entity is responsible for designing, organizing, or planning the architectural structure or layout of another entity.
  • D. architecturalStyle
    Indicates the architectural design tradition, movement, or style that characterizes the form and appearance of a structure or built work.
  • E. architecturalProject
    Indicates that one entity is an architectural project associated with, created by, or undertaken for another entity.
  • 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_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.
PD Predicate disambiguation batch_69adae0e770481908528fa35eda53003 completed March 8, 2026, 5:12 p.m.
PDg Predicate description generation batch_69adaed74ecc8190b74dc70ab59a3e1c completed March 8, 2026, 5:16 p.m.
Created at: March 8, 2026, 3:18 p.m.