Triple

T12207632
Position Surface form Disambiguated ID Type / Status
Subject Fréchet Inception Distance E290874 entity
Predicate uses P98 FINISHED
Object Inception network E107999 NE FINISHED

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: Inception network | Statement: [Fréchet Inception Distance, uses, Inception network]

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: Inception network
Context triple: [Fréchet Inception Distance, uses, Inception network]
  • 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. 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.
  • D. 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.
  • E. Reformer architecture
    The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

Stage Batch ID Job type Status
creating batch_69d6ab65923081909acfc61b7a612233 elicitation completed
NER batch_69d91c7d8f5c8190a46e9caa2a920fa9 ner completed
NED1 batch_69f61e5666f48190a28eed761e7b9210 ned_source_triple completed
Created at: April 8, 2026, 9:51 p.m.