Triple

T18016467
Position Surface form Disambiguated ID Type / Status
Subject Faster R-CNN E431008 entity
Predicate typicalBackbone P56257 FINISHED
Object VGG16 — NE NERFINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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: VGG16 | Statement: [Faster R-CNN, typicalBackbone, VGG16]

Disambiguation candidates (2 decisions)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VGG16
Context triple: [Faster R-CNN, typicalBackbone, VGG16]
  • A. VGG chosen
    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.
  • B. 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.
  • C. LeNet
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • D. 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.
  • 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: typicalBackbone
Context triple: [Faster R-CNN, typicalBackbone, VGG16]
  • A. backboneRoute
    Indicates a primary, high-capacity network path that carries core traffic between major nodes or segments in a system.
  • B. typicalBase
    Indicates that one entity serves as the standard or most representative base or foundation for another entity in typical or common cases.
  • C. typicalComponent chosen
    Indicates that one entity is a standard or representative component or part of another entity.
  • D. typicalControllerType
    Indicates the usual or most common type of controller associated with, or used to operate, a given entity.
  • E. isCombinationBackboneWith
    Indicates a structural relationship where one entity serves as a backbone or core framework that is combined with another entity to form a composite or integrated whole.
  • F. None of above.

Provenance (3 batches)

Stage Batch ID Job type Status
creating batch_69d8b904530081908bf341d842464856 elicitation completed
NER batch_69e4b523f588819097389e067dda7f23 ner completed
PD batch_69e3f904b8048190add43883cd7cb191 pd completed
Created at: April 10, 2026, 10:24 a.m.