Triple

T21972877
Position Surface form Disambiguated ID Type / Status
Subject Daniel Stroock E542634 entity
Predicate notableWork P4 FINISHED
Object Markov Processes from K. Itô’s Perspective — 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 Processes from K. Itô’s Perspective | Statement: [Daniel Stroock, notableWork, Markov Processes from K. Itô’s Perspective]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov Processes from K. Itô’s Perspective
Context triple: [Daniel Stroock, notableWork, Markov Processes from K. Itô’s Perspective]
  • A. "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.
  • B. Itô processes
    Itô processes are a class of stochastic processes, typically modeled as solutions to stochastic differential equations, that form the fundamental objects of study in Itô calculus and modern stochastic analysis.
  • C. Modern Probability Theory and Its Applications
    "Modern Probability Theory and Its Applications" is a foundational textbook by Emanuel Parzen that systematically develops modern probability theory and demonstrates its use in a wide range of statistical and applied contexts.
  • D. 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.
  • E. 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.
  • 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_69e0c48070988190909db97667b9a0ac completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f124857dcc8190ab474cd8ab9c130a completed April 28, 2026, 9:20 p.m.
Created at: April 16, 2026, 8:02 p.m.