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 |
How this triple was built (4 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 property | Statement: [Markov random field, satisfies, Markov property]
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.
NEDg
Description generation
gpt-5.1
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.
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.
Provenance (5 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
| NEDg | Description generation | batch_69e35570b0bc8190a939b0c8e3ce8105 |
completed | April 18, 2026, 9:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e359508a388190a16d48a17015e13e |
completed | April 18, 2026, 10:13 a.m. |
Created at: April 8, 2026, 9:25 p.m.