Triple
T19377128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gutzwiller approximation |
E484700
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | method in condensed matter physics |
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: method in condensed matter physics Context triple: [Gutzwiller approximation, instanceOf, method in condensed matter physics]
-
A.
model in solid-state physics
A model in solid-state physics is a theoretical framework or simplified representation used to describe, predict, and understand the behavior of electrons, atoms, and quasiparticles in crystalline and condensed matter systems.
-
B.
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.
-
C.
solid-state physics technique
A solid-state physics technique is a method or experimental approach used to investigate and characterize the physical properties of solid materials at atomic, electronic, and structural levels.
-
D.
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.
-
E.
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.
- 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.