Triple
T10023436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian networks |
E200666
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | directed graphical model |
C26339
|
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: directed graphical model Context triple: [Bayesian networks, instanceOf, directed graphical model]
-
A.
directed graph
A directed graph is a set of vertices connected by edges that have a specific direction, indicating ordered relationships from one vertex to another.
-
B.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
C.
deep learning model
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.
directive-based programming model
A directive-based programming model is a high-level parallel programming approach where developers annotate code with compiler-interpreted directives (pragmas) to express parallelism and data movement without explicitly managing low-level threading or synchronization details.
-
E.
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.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
Created at: March 30, 2026, 8:53 p.m.