Triple
T11560526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modern Probability Theory and Its Applications |
E274129
|
entity |
| Predicate | subject |
P450
|
FINISHED |
| Object | Markov chains |
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 chains | Statement: [Modern Probability Theory and Its Applications, subject, Markov chains]
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 chains Context triple: [Modern Probability Theory and Its Applications, subject, Markov chains]
-
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.
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.
-
D.
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.
-
E.
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.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
elicitation | completed |
| NER | batch_69d88a899d4481909a3bce3147763b51 |
ner | completed |
| NED1 | batch_69e6e88b84d48190948243646bb5fd2b |
ned_source_triple | completed |
Created at: April 8, 2026, 9:37 p.m.