Triple

T18016321
Position Surface form Disambiguated ID Type / Status
Subject MobileNetV2 E431005 entity
Predicate availableInLibrary P57368 FINISHED
Object PyTorch torchvision NE NERFINISHED

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: PyTorch torchvision | Statement: [MobileNetV2, availableInLibrary, PyTorch torchvision]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PyTorch torchvision
Context triple: [MobileNetV2, availableInLibrary, PyTorch torchvision]
  • 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. PyTorch
    PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
  • C. ChainerCV
    ChainerCV is a computer vision toolkit built on top of the Chainer deep learning framework, providing ready-to-use models and utilities for tasks like object detection and semantic segmentation.
  • D. Caffe Model Zoo
    Caffe Model Zoo is a public collection of pre-trained deep learning models shared by the Caffe community for tasks like image classification, detection, and segmentation.
  • E. TensorFlow Metal
    TensorFlow Metal is an integration that enables TensorFlow to run efficiently on Apple GPUs via the Metal framework, accelerating machine learning workloads on macOS and iOS devices.
  • 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: availableInLibrary
Context triple: [MobileNetV2, availableInLibrary, PyTorch torchvision]
  • A. usedInLibrary chosen
    Indicates that something is utilized or applied within the context of a library, such as a software library or a physical library system.
  • B. servedByLibrary
    Indicates that an entity receives services, resources, or support from a particular library.
  • C. availableWith
    Indicates that one entity can be obtained, accessed, or used in conjunction with another entity.
  • D. availableAs
    Indicates that one entity can be used, accessed, or offered in the form, role, or capacity of another entity.
  • E. isLendingLibrary
    Indicates that an entity functions as a library that lends items (such as books or media) to users.
  • F. None of above.

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_69d8b904530081908bf341d842464856 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4b523f588819097389e067dda7f23 completed April 19, 2026, 10:57 a.m.
PD Predicate disambiguation batch_69e3f904b8048190add43883cd7cb191 completed April 18, 2026, 9:35 p.m.
Created at: April 10, 2026, 10:24 a.m.