Triple
T23941072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin-Quinn scores |
E602784
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | time-series cross-sectional model |
C26339
|
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: time-series cross-sectional model Context triple: [Martin-Quinn scores, instanceOf, time-series cross-sectional model]
-
A.
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.
-
B.
U.S. economic time series
A U.S. economic time series is a chronologically ordered sequence of quantitative observations that track the evolution of a specific economic indicator (such as GDP, inflation, or unemployment) in the United States over time.
-
C.
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.
-
D.
cyclical forecasting system
A cyclical forecasting system is a predictive framework that analyzes recurring patterns and periodic trends in data to anticipate future states or events over repeating time intervals.
-
E.
statistical model
chosen
A statistical model is a mathematical representation of observed data and underlying random processes, used to describe relationships, make inferences, and generate predictions.
- 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_69e2953cf6e081909b8e25a10a52dddc |
completed | April 17, 2026, 8:17 p.m. |
Created at: April 17, 2026, 9:09 p.m.