Triple
T29504378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | alpha–beta pruning |
E748469
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | adversarial search algorithm |
C24840
|
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: adversarial search algorithm Context triple: [alpha–beta pruning, instanceOf, adversarial search algorithm]
-
A.
game tree search method
chosen
A game tree search method is an algorithmic approach that systematically explores possible moves and their consequences in a game’s decision tree to determine optimal or near-optimal actions.
-
B.
game-playing AI
A game-playing AI is an artificial intelligence system designed to analyze game states, make strategic decisions, and execute actions to achieve optimal performance or victory within a defined set of game rules.
-
C.
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.
-
D.
algorithm
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.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
- 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_69f0bd455a9c8190b40a3e8ea38cf61f |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 28, 2026, 4:26 p.m.