Triple

T15532292
Position Surface form Disambiguated ID Type / Status
Subject Shaoqing Ren E370248 entity
Predicate algorithmTypeWorkedOn P21840 FINISHED
Object residual networks 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: residual networks | Statement: [Shaoqing Ren, algorithmTypeWorkedOn, residual networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: residual networks
Context triple: [Shaoqing Ren, algorithmTypeWorkedOn, residual networks]
  • A. 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.
  • 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. 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.
  • 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. 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)

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_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e0414877d88190804ee76566004e13 completed April 16, 2026, 1:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d5e82a48190bb0a10ebc2412129 completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 4:06 a.m.