Triple

T18016469
Position Surface form Disambiguated ID Type / Status
Subject Faster R-CNN E431008 entity
Predicate typicalBackbone P56257 FINISHED
Object ResNet-101 — 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: ResNet-101 | Statement: [Faster R-CNN, typicalBackbone, ResNet-101]

Disambiguation candidates (1 decision)

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: ResNet-101
Context triple: [Faster R-CNN, typicalBackbone, ResNet-101]
  • A. ResNet chosen
    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.
  • 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. 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.
  • D. DenseNet
    DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
  • E. NASNet
    NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

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