Triple

T20049273
Position Surface form Disambiguated ID Type / Status
Subject Learning Transferable Architectures for Scalable Image Recognition E499148 entity
Predicate introduces P201 FINISHED
Object NASNet-A — 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: NASNet-A | Statement: [Learning Transferable Architectures for Scalable Image Recognition, introduces, NASNet-A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: NASNet-A
Context triple: [Learning Transferable Architectures for Scalable Image Recognition, introduces, NASNet-A]
  • A. NASNet chosen
    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.
  • B. MobileNet
    MobileNet is a family of lightweight convolutional neural network architectures designed for efficient deployment on mobile and embedded devices.
  • 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. 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.
  • E. MobileNetV2
    MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
  • 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_69da6276bcf48190aabbf279192a5fb4 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6632cccb481908278c8b2930a8c26 completed April 20, 2026, 5:32 p.m.
Created at: April 11, 2026, 3:37 p.m.