Triple
T26813389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aho–Corasick algorithm |
E672058
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | finite-state machine based algorithm |
C6819
|
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 based algorithm Context triple: [Aho–Corasick algorithm, instanceOf, finite-state machine based algorithm]
-
A.
finite-state machine
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.
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.
-
D.
algorithm
chosen
An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
-
E.
Monte Carlo reinforcement learning algorithm
A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
- 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_69eeb3225a3c8190aaf6746efeded2f3 |
completed | April 27, 2026, 12:51 a.m. |
Created at: April 27, 2026, 4:30 a.m.