Triple
T8993085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elmo shogi engine |
E214836
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | game-playing artificial intelligence system |
C8758
|
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: game-playing artificial intelligence system Context triple: [Elmo shogi engine, instanceOf, game-playing artificial intelligence system]
-
A.
game-playing AI
chosen
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.
-
B.
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.
-
C.
test of machine intelligence
A test of machine intelligence is a systematic procedure or set of tasks designed to evaluate a machine's ability to exhibit behaviors or problem-solving capabilities that are typically associated with human cognitive processes.
-
D.
combinatorial game
A combinatorial game is a two-player, perfect-information game with no chance elements where players move alternately and the outcome depends solely on their strategic choices under well-defined rules.
-
E.
benchmark in artificial intelligence
A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
- 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_69ca83a05c608190bdfdbdb25e994b39 |
completed | March 30, 2026, 2:07 p.m. |
Created at: March 30, 2026, 7:04 p.m.