Triple

T15361382
Position Surface form Disambiguated ID Type / Status
Subject Xiangyu Zhang E367296 entity
Predicate notableWork P4 FINISHED
Object Deep Residual Learning for Image Recognition (co-author) E74928 NE FINISHED

How this triple was built (2 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: Deep Residual Learning for Image Recognition (co-author) | Statement: [Xiangyu Zhang, notableWork, Deep Residual Learning for Image Recognition (co-author)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Deep Residual Learning for Image Recognition (co-author)
Context triple: [Xiangyu Zhang, notableWork, Deep Residual Learning for Image Recognition (co-author)]
  • A. 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.
  • B. Aggregated Residual Transformations for Deep Neural Networks
    "Aggregated Residual Transformations for Deep Neural Networks" is the research paper that introduced the ResNeXt architecture, a deep convolutional neural network design that improves accuracy and efficiency by using grouped convolutions and aggregated residual transformations.
  • C. ResNet chosen
    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.
  • D. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1343862481908962dfe0ab946b97 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:18 a.m.