Triple
T12160471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert J. Hodrick |
E289691
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Hodrick–Prescott filter
The Hodrick–Prescott filter is a widely used econometric tool for decomposing a time series into trend and cyclical components, especially in macroeconomic and business cycle analysis.
|
E966268
|
NE FINISHED |
How this triple was built (4 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hodrick–Prescott filter | Statement: [Robert J. Hodrick, notableWork, Hodrick–Prescott filter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hodrick–Prescott filter Context triple: [Robert J. Hodrick, notableWork, Hodrick–Prescott filter]
-
A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
B.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
C.
Statistical Testing of Business-Cycle Theories
"Statistical Testing of Business-Cycle Theories" is an econometric work that rigorously evaluates and compares competing explanations of business cycles using quantitative data and formal statistical methods.
-
D.
Econometric Model of the United States
Econometric Model of the United States is a large-scale macroeconometric model developed to analyze and forecast the U.S. economy, particularly associated with the pioneering work of economist Lawrence Klein.
-
E.
Klein–Tinbergen macroeconometric models
The Klein–Tinbergen macroeconometric models are pioneering large-scale quantitative models of national economies that integrated economic theory with statistical estimation to analyze and forecast macroeconomic activity.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hodrick–Prescott filter Triple: [Robert J. Hodrick, notableWork, Hodrick–Prescott filter]
Generated description
The Hodrick–Prescott filter is a widely used econometric tool for decomposing a time series into trend and cyclical components, especially in macroeconomic and business cycle analysis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hodrick–Prescott filter Target entity description: The Hodrick–Prescott filter is a widely used econometric tool for decomposing a time series into trend and cyclical components, especially in macroeconomic and business cycle analysis.
-
A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
B.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
C.
Statistical Testing of Business-Cycle Theories
"Statistical Testing of Business-Cycle Theories" is an econometric work that rigorously evaluates and compares competing explanations of business cycles using quantitative data and formal statistical methods.
-
D.
Econometric Model of the United States
Econometric Model of the United States is a large-scale macroeconometric model developed to analyze and forecast the U.S. economy, particularly associated with the pioneering work of economist Lawrence Klein.
-
E.
Klein–Tinbergen macroeconometric models
The Klein–Tinbergen macroeconometric models are pioneering large-scale quantitative models of national economies that integrated economic theory with statistical estimation to analyze and forecast macroeconomic activity.
- F. None of above. chosen
Provenance (5 batches)
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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915c395e48190a16e97fd29787a51 |
completed | April 10, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6a0baf0819094e90b7e92b979d4 |
completed | May 2, 2026, 1:05 p.m. |
| NEDg | Description generation | batch_69f600b7385881909ddb86a1d39ff5d4 |
completed | May 2, 2026, 1:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f601ef0a9c8190ac922562a8856def |
completed | May 2, 2026, 1:53 p.m. |
Created at: April 8, 2026, 9:50 p.m.