Triple

T19693255
Position Surface form Disambiguated ID Type / Status
Subject Inception v1 E472887 entity
Predicate hasSuccessor P78 FINISHED
Object Inception v3 — NE NERFINISHED

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 v3 | Statement: [Inception v1, hasSuccessor, Inception v3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Inception v3
Context triple: [Inception v1, hasSuccessor, Inception v3]
  • A. GoogLeNet
    GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
  • 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. 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.
  • D. VGG
    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.
  • E. SqueezeNet
    SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e515bef88190bc30781aea50537a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e64211e5d481908358d922e0dca271 completed April 20, 2026, 3:11 p.m.
Created at: April 10, 2026, 1:46 p.m.