Triple

T4325976
Position Surface form Disambiguated ID Type / Status
Subject torchvision E96634 entity
Predicate hasSubmodule P25619 FINISHED
Object torchvision.datasets E96634 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: torchvision.datasets | Statement: [torchvision, hasSubmodule, torchvision.datasets]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: torchvision.datasets
Context triple: [torchvision, hasSubmodule, torchvision.datasets]
  • A. torchvision (ecosystem) chosen
    torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
  • B. CIFAR-10
    CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
  • C. MNIST
    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.
  • D. ImageNet
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • E. CIFAR-100
    CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
  • 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: hasSubmodule
Context triple: [torchvision, hasSubmodule, torchvision.datasets]
  • A. hasSubcomponent chosen
    Indicates that one entity is a constituent part or component of another, larger entity.
  • B. hasSisterSubsystem
    Indicates that one subsystem is related to another as a sister subsystem, meaning they share a common parent system or hierarchical level.
  • C. hasSubService
    Indicates that one service includes or is composed of another, more specific service as a subordinate or component part.
  • D. hasSubConcept
    Indicates that one concept is a more specific, subordinate, or narrower idea within the scope of another, more general concept.
  • E. hasSubProcess
    Indicates that one process is composed of or includes another process as a subordinate or component step.
  • 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_69b34542fd908190b11b08faad8decfd completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3513020f481909ff2fec3934f3002 completed March 12, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d09861a4819086a88bb42a8ea2e4 completed March 14, 2026, 9:18 p.m.
PD Predicate disambiguation batch_69b34f4bec888190987fc2631498b637 completed March 12, 2026, 11:42 p.m.
Created at: March 12, 2026, 11:13 p.m.