Triple
T11108867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potts model |
E262702
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | model in statistical mechanics |
C26339
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: model in statistical mechanics Context triple: [Potts model, instanceOf, model in statistical mechanics]
-
A.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
-
B.
equation in statistical physics
An equation in statistical physics is a mathematical relation that connects microscopic properties of particles and their interactions to macroscopic thermodynamic quantities, enabling the prediction of a system’s collective behavior.
-
C.
mean-field theory
Mean-field theory is an approximate method in statistical physics and related fields that replaces the complex interactions of many components with an average or "mean" effect, allowing tractable analysis of collective behavior.
-
D.
model in superconductivity
A model in superconductivity is a theoretical framework that describes how electrons pair and move without resistance in certain materials below a critical temperature, capturing key phenomena such as the Meissner effect and energy gap formation.
-
E.
model of irreversibility
A model of irreversibility is a conceptual framework that represents processes or systems whose evolution cannot be exactly reversed, typically due to entropy increase, information loss, or path-dependent dynamics.
- F. None of above.
Provenance (1 batch)
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. |
Created at: April 8, 2026, 9:27 p.m.