Triple
T11219394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milnor–Thurston kneading theory |
E265519
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | theory in one-dimensional dynamics |
C21648
|
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: theory in one-dimensional dynamics Context triple: [Milnor–Thurston kneading theory, instanceOf, theory in one-dimensional dynamics]
-
A.
tool in dynamical systems theory
chosen
A tool in dynamical systems theory is a conceptual or computational method—such as phase portraits, Lyapunov functions, or Poincaré maps—used to analyze, visualize, and understand the qualitative and quantitative behavior of dynamical systems over time.
-
B.
landmark paper in nonlinear science
A landmark paper in nonlinear science is a seminal research work that fundamentally advances understanding of complex, nonlinear phenomena and significantly shapes subsequent theory, methods, or applications in the field.
-
C.
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.
-
D.
stability concept in functional equations
A stability concept in functional equations studies how small deviations from an exact functional relationship affect the existence and form of nearby exact solutions, typically quantifying when approximate solutions imply true solutions close in some specified sense.
-
E.
theory in theoretical physics
A theory in theoretical physics is a mathematically formulated, logically consistent framework that explains and predicts physical phenomena by modeling fundamental entities and their interactions.
- 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
Created at: April 8, 2026, 9:30 p.m.