Triple
T11002980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markov random field |
E260046
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Markov network |
E260046
|
NE FINISHED |
How this triple was built (2 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 network | Statement: [Markov random field, hasAlternativeName, Markov network]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markov network Context triple: [Markov random field, hasAlternativeName, Markov network]
-
A.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
B.
Markov random fields
chosen
Markov random fields are probabilistic graphical models that represent the joint distribution of a set of random variables with local dependencies encoded by an undirected graph, widely used in areas like statistical physics, computer vision, and spatial statistics.
-
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
Markov is a Russian surname most famously associated with mathematician Andrey Markov, known for his pioneering work on stochastic processes and Markov chains.
-
E.
Probabilistic Graphical Models: Principles and Techniques
Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: April 8, 2026, 9:25 p.m.