Triple
T17587733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James J. Heckman |
E428367
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables” |
—
|
NE NERFINISHED |
How this triple was built (2 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: “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables” | Statement: [James J. Heckman, notableWork, “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables” Context triple: [James J. Heckman, notableWork, “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables”]
-
A.
Heckman selection model
chosen
The Heckman selection model is an econometric technique that corrects for sample selection bias in regression analysis by jointly modeling the selection process and the outcome equation.
-
B.
“Sample Selection Bias as a Specification Error”
“Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
-
C.
The Probability Approach in Econometrics
The Probability Approach in Econometrics is Trygve Haavelmo’s landmark work that founded modern econometrics by rigorously formulating economic relationships within a probabilistic, statistical framework.
-
D.
Generalized method of moments
The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e41bf08190963848f1597b6e9f |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.