Triple

T18787696
Position Surface form Disambiguated ID Type / Status
Subject MIT course 6.034 Artificial Intelligence E459419 entity
Predicate coversTopic P380 FINISHED
Object Markov decision processes — NE NERFINISHED

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: [MIT course 6.034 Artificial Intelligence, coversTopic, Markov decision processes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov decision processes
Context triple: [MIT course 6.034 Artificial Intelligence, coversTopic, 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. Bayes-adaptive planning and learning in Markov decision processes
    "Bayes-adaptive planning and learning in Markov decision processes" is a doctoral thesis that develops Bayesian reinforcement learning methods for decision-making under uncertainty in Markov decision processes.
  • C. 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.
  • D. Markov processes
    Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
  • E. Bayes-Adaptive Monte-Carlo Planning and Learning in POMDPs
    Bayes-Adaptive Monte-Carlo Planning and Learning in POMDPs is a research work that introduces a Bayesian reinforcement learning approach using Monte Carlo planning methods to efficiently learn and act in partially observable environments.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d396f54c8190ba49db31e8743842 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e59783ea4c8190b1b04d08f65b7d19 completed April 20, 2026, 3:03 a.m.
Created at: April 10, 2026, 11:53 a.m.