Triple

T15361381
Position Surface form Disambiguated ID Type / Status
Subject Xiangyu Zhang E367296 entity
Predicate notableWork P4 FINISHED
Object ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices is a lightweight deep learning architecture designed to deliver high accuracy with very low computational cost, making it well-suited for deployment on mobile and embedded devices.
 E1154232 NE FINISHED

Disambiguation candidates (2 decisions)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Context triple: [Xiangyu Zhang, notableWork, ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices]
  • A. ShuffleNetV2
    ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
  • B. SqueezeNet
    SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
  • C. MobileNetV2
    MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
  • D. Learning Transferable Architectures for Scalable Image Recognition
    "Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
  • E. NASNet
    NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Target entity description: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices is a lightweight deep learning architecture designed to deliver high accuracy with very low computational cost, making it well-suited for deployment on mobile and embedded devices.
  • A. ShuffleNetV2
    ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
  • B. SqueezeNet
    SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
  • C. MobileNetV2
    MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
  • D. Learning Transferable Architectures for Scalable Image Recognition
    "Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
  • E. NASNet
    NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
  • F. None of above. chosen

Provenance (5 batches)

Stage Batch ID Job type Status
creating batch_69d85a1483788190ad93c2748e8af34b elicitation completed
NER batch_69e03e4607408190ab281a7f7a8012d3 ner completed
NED1 batch_69ff1343862481908962dfe0ab946b97 ned_source_triple completed
NED2 batch_69ff14e1a7b881909ad2ba0d35847ea1 ned_description completed
NEDg batch_69ff143c0e448190b4775711ee7545d1 nedg completed
Created at: April 10, 2026, 3:18 a.m.