Triple
T8640857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Probability theory |
E204642
|
entity |
| Predicate | usesConcept |
P531
|
FINISHED |
| Object | Markov chain |
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 chain | Statement: [Probability theory, usesConcept, Markov chain]
Disambiguation candidates (1 decision)
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 chain Context triple: [Probability theory, usesConcept, Markov chain]
-
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 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.
-
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 chain Monte Carlo
Markov chain Monte Carlo is a class of algorithms that uses Markov chains to generate samples from complex probability distributions, widely used in Bayesian inference, statistical physics, and machine learning.
-
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
| Stage | Batch ID | Job type | Status |
|---|---|---|---|
| creating | batch_69ca834ca1c88190a11ffb0200342fac |
elicitation | completed |
| NER | batch_69cc47944d1c819081f448f14d04bf9d |
ner | completed |
| NED1 | batch_69cebc3b1f508190978df29d995f494c |
ned_source_triple | completed |
Created at: March 30, 2026, 6:28 p.m.