Triple
T18016384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ShuffleNetV2 |
E431006
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object | ONNX model zoo |
—
|
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: ONNX model zoo | Statement: [ShuffleNetV2, implementedIn, ONNX model zoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ONNX model zoo Context triple: [ShuffleNetV2, implementedIn, ONNX model zoo]
-
A.
ONNX
chosen
ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models that enables interoperability between different deep learning frameworks and tools.
-
B.
Caffe Model Zoo
Caffe Model Zoo is a public collection of pre-trained deep learning models shared by the Caffe community for tasks like image classification, detection, and segmentation.
-
C.
TensorFlow Model Garden
TensorFlow Model Garden is a curated collection of state-of-the-art machine learning models, reference implementations, and examples built using TensorFlow to help developers train, evaluate, and deploy advanced AI solutions.
-
D.
ONNX Runtime
ONNX Runtime is a high-performance, cross-platform inference engine for running machine learning models in the Open Neural Network Exchange (ONNX) format across a variety of hardware and deployment environments.
-
E.
TensorFlow Hub
TensorFlow Hub is a library and online repository of reusable machine learning models and components designed to simplify sharing and deploying pretrained models in TensorFlow applications.
- 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.