Triple

T18016421
Position Surface form Disambiguated ID Type / Status
Subject SqueezeNet E431007 entity
Predicate accuracyComparableTo P278 FINISHED
Object AlexNet NE NERFINISHED

How this triple was built (3 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: AlexNet | Statement: [SqueezeNet, accuracyComparableTo, AlexNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AlexNet
Context triple: [SqueezeNet, accuracyComparableTo, AlexNet]
  • A. AlexNet chosen
    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.
  • B. LeNet
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • C. 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.
  • 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. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: accuracyComparableTo
Context triple: [SqueezeNet, accuracyComparableTo, AlexNet]
  • A. accuracyDependsOn
    Indicates that the accuracy of one entity or process is contingent upon, or influenced by, another entity or factor.
  • B. isComparedTo chosen
    Indicates that one entity is evaluated or measured in relation to another to highlight similarities, differences, or relative qualities.
  • C. comparisonLevel
    Indicates the degree or intensity to which two or more entities are being compared within a given context.
  • D. hasAccuracy
    Indicates that something possesses a specified level or measure of correctness, precision, or exactness in relation to a standard or reference.
  • E. scaleComparison
    Indicates a relationship where two or more entities are compared in terms of their size, magnitude, or scale relative to one another.
  • F. None of above.

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_69d8b904530081908bf341d842464856 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4b523f588819097389e067dda7f23 completed April 19, 2026, 10:57 a.m.
PD Predicate disambiguation batch_69e3f904b8048190add43883cd7cb191 completed April 18, 2026, 9:35 p.m.
Created at: April 10, 2026, 10:24 a.m.