Triple
T18016375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ShuffleNetV2 |
E431006
|
entity |
| Predicate | introducedIn |
P513
|
FINISHED |
| Object | paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" |
—
|
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: paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" | Statement: [ShuffleNetV2, introducedIn, paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" Context triple: [ShuffleNetV2, introducedIn, paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"]
-
A.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices is a lightweight deep learning architecture designed to deliver high accuracy with very low computational cost, making it well-suited for deployment on mobile and embedded devices.
-
B.
ShuffleNetV2
chosen
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
C.
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.
-
D.
Learning Transferable Architectures for Scalable Image Recognition
"Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
-
E.
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.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.