Triple
T11002188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolis algorithm |
E260028
|
entity |
| Predicate | uses |
P98
|
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: [Metropolis algorithm, uses, 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: [Metropolis algorithm, uses, Markov chain]
-
A.
Markov
Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
-
B.
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.
-
C.
Hidden Markov Model
A Hidden Markov Model is a statistical model that represents systems with unobserved (hidden) states generating observable outputs, widely used for sequence analysis tasks such as speech recognition, bioinformatics, and natural language processing.
-
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.
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.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
elicitation | completed |
| NER | batch_69d796d760008190930228fa77b61b8b |
ner | completed |
| NED1 | batch_69e3453d181081908cb58a957f4d1295 |
ned_source_triple | completed |
Created at: April 8, 2026, 9:25 p.m.