Triple

T15361435
Position Surface form Disambiguated ID Type / Status
Subject ResNeXt E367297 entity
Predicate hasVariant P455 FINISHED
Object ResNeXt-152 E367297 NE FINISHED

Disambiguation candidates (1 decision)

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: ResNeXt-152
Context triple: [ResNeXt, hasVariant, ResNeXt-152]
  • A. ResNeXt chosen
    ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
  • B. 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.
  • C. 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.
  • D. ShuffleNetV2
    ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
  • E. DenseNet
    DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

Stage Batch ID Job type Status
creating batch_69d85a1483788190ad93c2748e8af34b elicitation completed
NER batch_69e03e4607408190ab281a7f7a8012d3 ner completed
NED1 batch_69ff1a6991148190b522684b35c07b1a ned_source_triple completed
Created at: April 10, 2026, 3:18 a.m.