Triple
T30369327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | transition network |
E772507
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | finite-state model |
C50437
|
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: finite-state model Context triple: [transition network, instanceOf, finite-state model]
-
A.
finite-state machine
chosen
A finite-state machine is an abstract computational model consisting of a finite set of states, transitions between those states based on inputs, and rules that determine state changes and outputs.
-
B.
nondeterministic finite automaton
A nondeterministic finite automaton is a theoretical computational model consisting of a finite set of states and transitions where, for a given state and input symbol, the machine may move to zero, one, or multiple possible next states (including via ε-moves), accepting an input string if at least one possible path leads to an accepting state.
-
C.
statistical model
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
D.
modeling framework
A modeling framework is a structured set of concepts, methods, and tools used to construct, analyze, and interpret representations of real-world systems or phenomena.
-
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_69f2248d71408190aec0d5c2001b1cff |
completed | April 29, 2026, 3:32 p.m. |
Created at: April 29, 2026, 7:59 p.m.