Triple

T19203638
Position Surface form Disambiguated ID Type / Status
Subject Yangqing Jia E480174 entity
Predicate knownFor P22 FINISHED
Object Caffe deep learning framework — NE NERFINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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: Caffe deep learning framework | Statement: [Yangqing Jia, knownFor, Caffe deep learning framework]

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: Caffe deep learning framework
Context triple: [Yangqing Jia, knownFor, Caffe deep learning framework]
  • A. 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.
  • B. ImageNet Classification with Deep Convolutional Neural Networks
    "ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
  • C. ImageNet CNN
    ImageNet CNN is a convolutional neural network model trained on the large-scale ImageNet dataset, commonly used as a powerful pretrained feature extractor for various computer vision tasks.
  • D. Very Deep Convolutional Networks for Large-Scale Image Recognition
    "Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
  • 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. 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: Caffe deep learning framework
Target entity description: Caffe is an open-source deep learning framework focused on speed and modularity, widely used for computer vision and convolutional neural networks.
  • A. 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.
  • B. ImageNet Classification with Deep Convolutional Neural Networks
    "ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
  • C. ImageNet CNN
    ImageNet CNN is a convolutional neural network model trained on the large-scale ImageNet dataset, commonly used as a powerful pretrained feature extractor for various computer vision tasks.
  • D. Very Deep Convolutional Networks for Large-Scale Image Recognition
    "Very Deep Convolutional Networks for Large-Scale Image Recognition" is the influential 2014 research paper that introduced the VGG family of deep convolutional neural network architectures, demonstrating that significantly increasing network depth with small convolutional filters leads to substantial improvements in image classification performance.
  • 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. chosen

Provenance (2 batches)

Stage Batch ID Job type Status
creating batch_69d8e8cb8c348190b52075823911c869 elicitation completed
NER batch_69e5f99a571c8190a1d53eb1994e0058 ner completed
Created at: April 10, 2026, 1:15 p.m.