Triple
T34761232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NK model of fitness landscapes |
E1002070
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | fitness landscape model |
C49542
|
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: fitness landscape model Context triple: [NK model of fitness landscapes, instanceOf, fitness landscape model]
-
A.
mathematical model in molecular evolution
chosen
A mathematical model in molecular evolution is a formal, quantitative framework that describes and predicts how genetic sequences change over time under processes such as mutation, selection, recombination, and genetic drift.
-
B.
framework in evolutionary biology
A framework in evolutionary biology is a conceptual structure that organizes theories, models, and empirical findings to explain how evolutionary processes generate and shape biological diversity over time.
-
C.
phyllotaxis model
A phyllotaxis model is a conceptual representation that mathematically or computationally simulates the spatial arrangement of leaves, seeds, or other botanical elements around a plant axis, often using spiral patterns and divergence angles.
-
D.
population dynamics model
A population dynamics model is a mathematical or computational framework that describes how the size and structure of a population change over time under the influence of births, deaths, migration, and interactions with the environment or other populations.
-
E.
optimization paradigm
An optimization paradigm is a conceptual framework that defines how to formulate, search for, and evaluate solutions to a problem in order to find the best (or sufficiently good) outcome under given constraints and objectives.
- 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_69f76db0fb30819096709d43f9a1f45f |
completed | May 3, 2026, 3:45 p.m. |
Created at: May 3, 2026, 3:59 p.m.