Triple

T22666617
Position Surface form Disambiguated ID Type / Status
Subject Poisson process E559807 entity
Predicate isSpecialCaseOf P2372 FINISHED
Object Markov jump process — 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 jump process | Statement: [Poisson process, isSpecialCaseOf, Markov jump process]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov jump process
Context triple: [Poisson process, isSpecialCaseOf, Markov jump process]
  • 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. "Continuous Markov Processes and Stochastic Equations"
    "Continuous Markov Processes and Stochastic Equations" is a foundational mathematical work that rigorously develops the theory of continuous-time Markov processes and their representation via stochastic differential equations.
  • E. Markov semigroup
    A Markov semigroup is a family of linear operators describing the time evolution of probability distributions in a Markov process, forming a semigroup under composition and preserving positivity and total mass.
  • 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_69e2454a158c819093b8e35f5045efb6 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1781c2c808190baf6964ca1eced6f completed April 29, 2026, 3:16 a.m.
Created at: April 17, 2026, 3:09 p.m.