Triple
T19377075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dynamical Mean-Field Theory |
E484699
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | many-body method |
C40616
|
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: many-body method Context triple: [Dynamical Mean-Field Theory, instanceOf, many-body method]
-
A.
many-body quantum system
A many-body quantum system is a collection of a large number of interacting quantum particles whose collective behavior exhibits complex phenomena that cannot be understood by considering the particles individually.
-
B.
quantum many-body theory
Quantum many-body theory studies systems of a large number of interacting quantum particles, aiming to understand their collective behavior and emergent phenomena using quantum mechanics and statistical methods.
-
C.
technique in quantum many-body physics
chosen
A technique in quantum many-body physics is a systematic method or computational framework used to analyze, approximate, or simulate the collective behavior and emergent properties of interacting quantum particles.
-
D.
molecular quantum mechanics method
A molecular quantum mechanics method is a theoretical and computational approach that applies quantum mechanical principles to describe and predict the electronic structure, properties, and behavior of molecules.
-
E.
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.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
Created at: April 10, 2026, 1:35 p.m.