Triple
T32607207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nash equilibrium |
E833553
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | game-theoretic solution concept |
C42711
|
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-theoretic solution concept Context triple: [Nash equilibrium, instanceOf, game-theoretic solution concept]
-
A.
bargaining solution concept
A bargaining solution concept is a formal rule or principle that specifies how two or more parties should divide the benefits of cooperation given their feasible payoffs and disagreement outcomes.
-
B.
game-theoretic scenario
chosen
A game-theoretic scenario is a structured situation in which multiple decision-makers (players) with potentially conflicting interests choose strategies whose outcomes and payoffs depend on the combined actions of all participants.
-
C.
result in cooperative game theory
In cooperative game theory, a result is a formal theorem or proposition that characterizes properties of coalitions, solution concepts, or payoff allocations under specified assumptions about players’ cooperation and preferences.
-
D.
cooperative game
A cooperative game is a strategic situation in which players can form binding agreements and coalitions to share payoffs and work together to achieve mutually beneficial outcomes.
-
E.
solution concept in stochastic control
A solution concept in stochastic control is a rigorous mathematical framework that specifies what it means for a control policy or strategy to optimally govern a stochastic dynamical system, typically defining admissible controls, performance criteria, and the form of optimality (e.g., value functions, optimal policies, or equilibria).
- 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_69f3492bfa648190b6ae472074634e29 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:05 a.m.