Triple

T921636
Position Surface form Disambiguated ID Type / Status
Subject Christian Szegedy E19896 entity
Predicate notableWork P4 FINISHED
Object Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning E107999 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: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | Statement: [Christian Szegedy, notableWork, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Context triple: [Christian Szegedy, notableWork, Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning]
  • A. Inception architecture chosen
    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. ResNet
    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.
  • C. Deep Learning (book)
    Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
  • D. Generative Adversarial Networks
    Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
  • E. “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.
  • 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_69a493a099788190a696d9d8408cbaf4 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b313cb908190ad78b3a54e4f2eb7 completed March 1, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a933a103908190a624039492079f82 completed March 5, 2026, 7:41 a.m.
Created at: March 1, 2026, 7:40 p.m.