Triple

T15313755
Position Surface form Disambiguated ID Type / Status
Subject Andrew Zisserman E366101 entity
Predicate knownFor P22 FINISHED
Object Visual Geometry Group (VGG) models E74406 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: Visual Geometry Group (VGG) models | Statement: [Andrew Zisserman, knownFor, Visual Geometry Group (VGG) models]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Visual Geometry Group (VGG) models
Context triple: [Andrew Zisserman, knownFor, Visual Geometry Group (VGG) models]
  • 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. 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. VGG chosen
    VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
  • D. 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.
  • 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.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03dd050108190a584543cb93943a4 completed April 16, 2026, 1:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef8a3da3881909b50cfbec0543adc completed May 9, 2026, 9:04 a.m.
Created at: April 10, 2026, 3:16 a.m.