Triple
T11108890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potts model |
E262702
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
Fortuin–Kasteleyn random cluster model
The Fortuin–Kasteleyn random cluster model is a unifying probabilistic framework in statistical mechanics that represents spin systems and percolation models, notably providing a graphical reformulation of the Potts model.
|
E906308
|
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: Fortuin–Kasteleyn random cluster model | Statement: [Potts model, relatedTo, Fortuin–Kasteleyn random cluster model]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fortuin–Kasteleyn random cluster model Context triple: [Potts model, relatedTo, Fortuin–Kasteleyn random cluster model]
-
A.
Hammersley–Clifford theorem
The Hammersley–Clifford theorem is a fundamental result in probability theory and statistics that links Markov random fields with Gibbs distributions by showing that, under positivity conditions, the Markov property is equivalent to factorization over cliques of an underlying graph.
-
B.
Potts model
The Potts model is a generalization of the Ising model in statistical mechanics that describes interacting spins with more than two possible states, used to study phase transitions and critical phenomena.
-
C.
Kramers–Wannier duality in the Ising model
Kramers–Wannier duality in the Ising model is a mathematical transformation that relates the high-temperature and low-temperature phases of the two-dimensional Ising model, revealing the location of its critical point and illustrating a deep symmetry between ordered and disordered states.
-
D.
Mayer cluster expansion in statistical mechanics
The Mayer cluster expansion in statistical mechanics is a mathematical method that expresses the thermodynamic properties of interacting particle systems as a series in terms of cluster integrals, enabling systematic analysis of non-ideal gases and liquids.
-
E.
Kac ring model
The Kac ring model is a simplified mathematical model in statistical mechanics introduced by Mark Kac to illustrate how macroscopic irreversibility can emerge from time-reversible microscopic dynamics.
- 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: Fortuin–Kasteleyn random cluster model Triple: [Potts model, relatedTo, Fortuin–Kasteleyn random cluster model]
Generated description
The Fortuin–Kasteleyn random cluster model is a unifying probabilistic framework in statistical mechanics that represents spin systems and percolation models, notably providing a graphical reformulation of the Potts model.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fortuin–Kasteleyn random cluster model Target entity description: The Fortuin–Kasteleyn random cluster model is a unifying probabilistic framework in statistical mechanics that represents spin systems and percolation models, notably providing a graphical reformulation of the Potts model.
-
A.
Hammersley–Clifford theorem
The Hammersley–Clifford theorem is a fundamental result in probability theory and statistics that links Markov random fields with Gibbs distributions by showing that, under positivity conditions, the Markov property is equivalent to factorization over cliques of an underlying graph.
-
B.
Potts model
The Potts model is a generalization of the Ising model in statistical mechanics that describes interacting spins with more than two possible states, used to study phase transitions and critical phenomena.
-
C.
Kramers–Wannier duality in the Ising model
Kramers–Wannier duality in the Ising model is a mathematical transformation that relates the high-temperature and low-temperature phases of the two-dimensional Ising model, revealing the location of its critical point and illustrating a deep symmetry between ordered and disordered states.
-
D.
Mayer cluster expansion in statistical mechanics
The Mayer cluster expansion in statistical mechanics is a mathematical method that expresses the thermodynamic properties of interacting particle systems as a series in terms of cluster integrals, enabling systematic analysis of non-ideal gases and liquids.
-
E.
Kac ring model
The Kac ring model is a simplified mathematical model in statistical mechanics introduced by Mark Kac to illustrate how macroscopic irreversibility can emerge from time-reversible microscopic dynamics.
- F. None of above. chosen
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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a67d10c8190815d4c27d55270e8 |
completed | April 9, 2026, 12:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d72f8f48190a7414119a6be9d5e |
completed | April 19, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69e4374700b881908ebb185ae020487b |
completed | April 19, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4399385c08190852c3cbd730a1f11 |
completed | April 19, 2026, 2:10 a.m. |
Created at: April 8, 2026, 9:27 p.m.