Triple
T17520992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow SavedModel (via conversion) |
E426677
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | TensorFlow model artifact |
C4177
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: TensorFlow model artifact Context triple: [TensorFlow SavedModel (via conversion), instanceOf, TensorFlow model artifact]
-
A.
machine learning model repository
A machine learning model repository is a centralized system for storing, versioning, organizing, and sharing trained models and their associated metadata throughout their lifecycle.
-
B.
AI inference server
An AI inference server is a system that hosts trained machine learning models and processes incoming requests to generate predictions or responses in real time.
-
C.
deep learning model
chosen
A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
-
D.
deep learning framework
A deep learning framework is a software library or platform that provides tools, abstractions, and optimized components to design, train, and deploy neural network models efficiently.
-
E.
deep learning library
A deep learning library is a software framework that provides tools, abstractions, and optimized routines to design, train, and deploy neural network models.
- F. None of above.
Provenance (1 batch)
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:49 a.m.