Triple

T3507272
Position Surface form Disambiguated ID Type / Status
Subject AlexNet E74105 entity
Predicate introducedInPaper P513 FINISHED
Object ImageNet Classification with Deep Convolutional Neural Networks E238233 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: ImageNet Classification with Deep Convolutional Neural Networks | Statement: [AlexNet, introducedInPaper, ImageNet Classification with Deep Convolutional Neural Networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ImageNet Classification with Deep Convolutional Neural Networks
Context triple: [AlexNet, introducedInPaper, ImageNet Classification with Deep Convolutional Neural Networks]
  • A. 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.
  • B. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
    "Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
  • C. Large-Scale Distributed Deep Networks chosen
    Large-Scale Distributed Deep Networks is a seminal research work that introduced methods for training deep neural networks efficiently across large-scale distributed computing infrastructure, enabling breakthroughs in modern large-scale AI systems.
  • D. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • E. AlexNet
    AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc0b635c81909bc95ba2562d8f94 completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373e0dc7881909af631182970d132 completed March 13, 2026, 2:18 a.m.
Created at: March 8, 2026, 3:18 p.m.