Triple

T15502885
Position Surface form Disambiguated ID Type / Status
Subject Soviet school of probability theory E379004 entity
Predicate notableWork P4 FINISHED
Object Markov processes E48274 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 processes | Statement: [Soviet school of probability theory, notableWork, Markov processes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov processes
Context triple: [Soviet school of probability theory, notableWork, Markov processes]
  • A. Markov processes chosen
    Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
  • B. Stochastic Processes
    "Stochastic Processes" is a foundational textbook by Emanuel Parzen that rigorously introduces the theory and applications of random processes in probability and statistics.
  • C. Stochastic Processes
    Stochastic Processes is a foundational 1953 monograph by Joseph L. Doob that rigorously develops the theory of stochastic processes and modern probability using measure-theoretic methods.
  • D. Markov
    Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
  • E. Markov decision processes
    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.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fcc5bb88190b8a9a81419a9a38b completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3669f908819087162b1b8a4e4320 completed May 9, 2026, 1:28 p.m.
Created at: April 10, 2026, 3:54 a.m.