Triple

T11002985
Position Surface form Disambiguated ID Type / Status
Subject Markov random field E260046 entity
Predicate satisfies P4233 FINISHED
Object Markov property
The Markov property is a memoryless characteristic of stochastic processes where the future behavior depends only on the present state and not on the sequence of events that preceded it.
 E48274 NE FINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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 property | Statement: [Markov random field, satisfies, Markov property]

Disambiguation candidates (2 decisions)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov property
Context triple: [Markov random field, satisfies, Markov property]
  • A. Markov processes
    Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
  • B. Markov
    Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
  • C. Chapman–Kolmogorov equation
    The Chapman–Kolmogorov equation is a fundamental relation in the theory of stochastic processes that expresses how transition probabilities of a Markov process over longer time intervals can be obtained by integrating over intermediate states.
  • D. 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.
  • E. 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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Markov property
Target entity description: The Markov property is a memoryless characteristic of stochastic processes where the future behavior depends only on the present state and not on the sequence of events that preceded it.
  • 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. Markov
    Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
  • C. Chapman–Kolmogorov equation
    The Chapman–Kolmogorov equation is a fundamental relation in the theory of stochastic processes that expresses how transition probabilities of a Markov process over longer time intervals can be obtained by integrating over intermediate states.
  • D. 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.
  • E. 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.
  • F. None of above.

How the object was described

The object's one-sentence description was generated by prompting gpt-5.1 with the object name and this triple as context.

Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Markov property
Triple: [Markov random field, satisfies, Markov property]
Generated description
The Markov property is a memoryless characteristic of stochastic processes where the future behavior depends only on the present state and not on the sequence of events that preceded it.

Provenance (5 batches)

Stage Batch ID Job type Status
creating batch_69d6aa8a6a548190a750f944ccdc8064 elicitation completed
NER batch_69d797546f448190946ee6442d657dc5 ner completed
NED1 batch_69e3453d181081908cb58a957f4d1295 ned_source_triple completed
NED2 batch_69e359508a388190a16d48a17015e13e ned_description completed
NEDg batch_69e35570b0bc8190a939b0c8e3ce8105 nedg completed
Created at: April 8, 2026, 9:25 p.m.