Triple
T10084784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Edward Mayer |
E215191
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
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.
|
E841011
|
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: Mayer cluster expansion in statistical mechanics | Statement: [Joseph Edward Mayer, notableWork, Mayer cluster expansion in statistical mechanics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayer cluster expansion in statistical mechanics Context triple: [Joseph Edward Mayer, notableWork, Mayer cluster expansion in statistical mechanics]
-
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.
Boltzmann–Gibbs entropy in statistical mechanics
Boltzmann–Gibbs entropy in statistical mechanics is the standard measure of disorder or uncertainty in a system, quantifying how many microscopic configurations correspond to a given macroscopic state and forming the basis of classical equilibrium statistical mechanics.
-
C.
Ehrenfest classification of phase transitions
The Ehrenfest classification of phase transitions is an early theoretical scheme that categorizes phase transitions by the order of discontinuity in thermodynamic derivatives, such as entropy or specific heat, at the transition point.
-
D.
Mathematical Foundations of Statistical Mechanics
Mathematical Foundations of Statistical Mechanics is a classic monograph by Aleksandr Khinchin that rigorously develops the probabilistic and measure-theoretic underpinnings of statistical mechanics.
-
E.
The Principles of Statistical Mechanics
The Principles of Statistical Mechanics is a classic 1938 textbook by Richard C. Tolman that systematically develops the foundations of statistical mechanics and its applications to thermodynamics and physical chemistry.
- 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: Mayer cluster expansion in statistical mechanics Triple: [Joseph Edward Mayer, notableWork, Mayer cluster expansion in statistical mechanics]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mayer cluster expansion in statistical mechanics Target entity description: 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.
-
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.
Boltzmann–Gibbs entropy in statistical mechanics
Boltzmann–Gibbs entropy in statistical mechanics is the standard measure of disorder or uncertainty in a system, quantifying how many microscopic configurations correspond to a given macroscopic state and forming the basis of classical equilibrium statistical mechanics.
-
C.
Ehrenfest classification of phase transitions
The Ehrenfest classification of phase transitions is an early theoretical scheme that categorizes phase transitions by the order of discontinuity in thermodynamic derivatives, such as entropy or specific heat, at the transition point.
-
D.
Mathematical Foundations of Statistical Mechanics
Mathematical Foundations of Statistical Mechanics is a classic monograph by Aleksandr Khinchin that rigorously develops the probabilistic and measure-theoretic underpinnings of statistical mechanics.
-
E.
The Principles of Statistical Mechanics
The Principles of Statistical Mechanics is a classic 1938 textbook by Richard C. Tolman that systematically develops the foundations of statistical mechanics and its applications to thermodynamics and physical chemistry.
- 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_69ca83a1eed081908b2e9580f2ebeea7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd044c1ec8190b5b48cdb0584d00c |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b68188c48190ac783cdfc072c502 |
completed | April 5, 2026, 7:22 p.m. |
| NEDg | Description generation | batch_69d2b7aecdb081909f651c1bc1bcfd75 |
completed | April 5, 2026, 7:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2b86bf8948190a79046efadc4adea |
completed | April 5, 2026, 7:30 p.m. |
Created at: March 30, 2026, 9 p.m.