Triple

T14828751
Position Surface form Disambiguated ID Type / Status
Subject operations research E348641 entity
Predicate usesMethod P859 FINISHED
Object Markov decision processes E1051244 NE FINISHED

How this triple was built (2 steps)

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.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Markov decision processes | Statement: [operations research, usesMethod, Markov decision processes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov decision processes
Context triple: [operations research, usesMethod, Markov decision processes]
  • A. Markov decision processes chosen
    Markov decision processes are mathematical frameworks for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker, widely used in reinforcement learning and control theory.
  • B. Foundations of a General Theory of Sequential Decision Functions
    Foundations of a General Theory of Sequential Decision Functions is a seminal work in statistics that established the mathematical foundations of sequential analysis and optimal decision-making under uncertainty.
  • C. Markov processes
    Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
  • D. Risk-Sensitive Optimal Control
    Risk-Sensitive Optimal Control is a foundational work in control theory that develops methods for designing controllers that explicitly account for uncertainty and variability in system performance.
  • E. Kemeny–Snell finite Markov chain theory
    Kemeny–Snell finite Markov chain theory is a foundational mathematical framework that rigorously develops the behavior and long-term properties of finite-state Markov chains, widely used in probability theory and stochastic processes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded0737d4c8190a49bf6b013da208c completed April 14, 2026, 11:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe38a16fd881909d246d8d1811a673 completed May 8, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:51 a.m.