Triple
T4804623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT series |
E106918
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence model series |
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: artificial intelligence model series Context triple: [GPT series, instanceOf, artificial intelligence model series]
-
A.
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.
-
B.
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.
-
C.
benchmark in artificial intelligence
A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
-
D.
recurrent artificial neural network
A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
-
E.
former model
A former model is an individual who previously worked professionally in modeling but has since left the industry or no longer does it as their primary occupation.
- 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_69bd43f6a1e08190bf0a372bfc336ee5 |
completed | March 20, 2026, 12:56 p.m. |
Created at: March 20, 2026, 1:23 p.m.