Triple
T17520993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow SavedModel (via conversion) |
E426677
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | serializable model representation |
C15497
|
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: serializable model representation Context triple: [TensorFlow SavedModel (via conversion), instanceOf, serializable model representation]
-
A.
decentralized field representation
A decentralized field representation is a way of encoding a spatially or temporally varying quantity using many local, distributed parameters or agents, rather than a single centralized model, so that the overall field emerges from their collective behavior.
-
B.
data serialization language
A data serialization language is a formal notation used to structure, encode, and represent data so it can be stored or transmitted and later reconstructed consistently across different systems.
-
C.
machine learning model repository
chosen
A machine learning model repository is a centralized system for storing, versioning, organizing, and sharing trained models and their associated metadata throughout their lifecycle.
-
D.
data model
A data model is an abstract, structured representation of data and its relationships, designed to organize, define, and constrain how information is stored, accessed, and manipulated within a system.
-
E.
models
Models are abstract representations or simulations of real-world systems, processes, or concepts used to understand, predict, or optimize their behavior.
- 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.