Triple
T18630841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucas asset pricing model |
E455411
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | representative agent model |
C35812
|
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: representative agent model Context triple: [Lucas asset pricing model, instanceOf, representative agent model]
-
A.
optimal growth model
chosen
An optimal growth model is a dynamic economic framework that determines how a representative agent or planner allocates consumption and investment over time to maximize intertemporal welfare subject to resource and technological constraints.
-
B.
econometric model
An econometric model is a quantitative representation of economic relationships that uses statistical methods and real-world data to estimate, test, and forecast economic behavior.
-
C.
macroeconomic theory
Macroeconomic theory is the branch of economics that studies the behavior, performance, and structure of an economy as a whole, focusing on aggregate measures like output, inflation, unemployment, and economic growth, and the policies that influence them.
-
D.
macroeconomic aggregate
A macroeconomic aggregate is a broad measure that summarizes the overall level or performance of key economic variables—such as output, income, prices, or employment—across an entire economy.
-
E.
economic forecasting model
An economic forecasting model is a structured analytical framework that uses historical data, statistical methods, and assumptions about future conditions to predict key economic variables such as growth, inflation, and employment.
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 11:46 a.m.