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.