Triple
T9029713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Augmented Transition Network |
E216137
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | finite-state machine formalism |
C7185
|
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 machine formalism Context triple: [Augmented Transition Network, instanceOf, finite-state machine formalism]
-
A.
automata theory technique
An automata theory technique is a formal method that uses abstract computational models like finite automata, pushdown automata, and Turing machines to analyze, design, and reason about languages, algorithms, and computational processes.
-
B.
formal language classification scheme
A formal language classification scheme is a systematic framework for categorizing formal languages based on their generative or recognitional power, typically using hierarchies such as the Chomsky hierarchy.
-
C.
formal grammar notation
A formal grammar notation is a precise symbolic system for defining the syntactic structure of languages by specifying how valid strings can be generated from a set of production rules.
-
D.
model of computation
chosen
A model of computation is an abstract mathematical framework that defines how algorithms are represented and executed, specifying the rules, operations, and resources available for performing computations.
-
E.
hierarchy of formal grammars
A hierarchy of formal grammars is an organized classification of grammars into levels based on their generative power and structural constraints, such as the Chomsky hierarchy from regular to recursively enumerable languages.
- 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_69ca83a5fa88819088144801b4dd7245 |
completed | March 30, 2026, 2:07 p.m. |
Created at: March 30, 2026, 7:08 p.m.