Gibbs ensemble
E284681
The Gibbs ensemble is a statistical physics framework that describes the probabilistic distribution of microstates for systems in thermal equilibrium, typically at fixed temperature, volume, and particle number.
All labels observed (4)
| Label | Occurrences |
|---|---|
| Gibbs ensemble canonical | 1 |
| Gibbs ensemble in statistical mechanics | 1 |
| Gibbs ensembles | 1 |
| grand canonical ensemble | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2631317 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gibbs ensemble Context triple: [statistical physics, usesConcept, Gibbs ensemble]
-
A.
Kirkwood approximation in statistical mechanics
The Kirkwood approximation in statistical mechanics is a method for approximating many-particle correlation functions by expressing higher-order correlations in terms of lower-order ones, simplifying the description of interacting particle systems.
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B.
Gibbs
Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
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C.
Computer experiments on classical fluids
"Computer experiments on classical fluids" is a pioneering work in computational physics that used numerical simulations to study the behavior and dynamics of classical fluid systems.
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D.
Boltzmann distribution
The Boltzmann distribution is a fundamental probability distribution in statistical mechanics that describes how particles or states are populated over different energy levels at thermal equilibrium.
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E.
Gibbs sampling
Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gibbs ensemble Target entity description: The Gibbs ensemble is a statistical physics framework that describes the probabilistic distribution of microstates for systems in thermal equilibrium, typically at fixed temperature, volume, and particle number.
-
A.
Kirkwood approximation in statistical mechanics
The Kirkwood approximation in statistical mechanics is a method for approximating many-particle correlation functions by expressing higher-order correlations in terms of lower-order ones, simplifying the description of interacting particle systems.
-
B.
Gibbs
Gibbs is a common English surname borne by various notable individuals across fields such as sports, science, and entertainment.
-
C.
Computer experiments on classical fluids
"Computer experiments on classical fluids" is a pioneering work in computational physics that used numerical simulations to study the behavior and dynamics of classical fluid systems.
-
D.
Boltzmann distribution
The Boltzmann distribution is a fundamental probability distribution in statistical mechanics that describes how particles or states are populated over different energy levels at thermal equilibrium.
-
E.
Gibbs sampling
Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
- F. None of above. chosen
Statements (30)
| Predicate | Object |
|---|---|
| instanceOf |
concept in statistical mechanics
ⓘ
statistical ensemble ⓘ |
| appliesTo | systems in thermal equilibrium ⓘ |
| assumes |
ergodic hypothesis
ⓘ
large number of degrees of freedom ⓘ |
| basedOn |
Boltzmann distribution
ⓘ
surface form:
Boltzmann factor
|
| characterizedBy |
fixed particle number
ⓘ
fixed temperature ⓘ fixed volume ⓘ |
| contrastsWith | non-equilibrium ensembles ⓘ |
| describes | probabilistic distribution of microstates ⓘ |
| field |
statistical mechanics
ⓘ
thermodynamics ⓘ |
| formalizedBy | Josiah Willard Gibbs ⓘ |
| hasKeyQuantity | partition function ⓘ |
| mathematicallyFormulatedAs | probability measure on phase space ⓘ |
| namedAfter | Josiah Willard Gibbs ⓘ |
| probabilityWeight | exponential of minus energy over k_B T ⓘ |
| provides | link between microscopic dynamics and macroscopic thermodynamics ⓘ |
| relatedTo |
canonical ensemble
ⓘ
grand canonical ensemble ⓘ microcanonical ensemble ⓘ |
| requires | definition of Hamiltonian of the system ⓘ |
| underlies |
canonical distribution
ⓘ
grand canonical distribution ⓘ |
| usedFor |
calculating expectation values of observables
ⓘ
deriving thermodynamic properties from microscopic states ⓘ |
| usedIn |
chemical physics
ⓘ
equilibrium statistical mechanics ⓘ theoretical physics ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Gibbs ensemble Description of subject: The Gibbs ensemble is a statistical physics framework that describes the probabilistic distribution of microstates for systems in thermal equilibrium, typically at fixed temperature, volume, and particle number.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Gibbs ensemble in statistical mechanics
this entity surface form:
grand canonical ensemble
this entity surface form:
Gibbs ensembles