Triple

T6385151
Position Surface form Disambiguated ID Type / Status
Subject Frisch–Waugh–Lovell theorem E143681 entity
Predicate alsoKnownAs P39 FINISHED
Object Frisch–Waugh theorem E143681 NE FINISHED

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: Frisch–Waugh theorem | Statement: [Frisch–Waugh–Lovell theorem, alsoKnownAs, Frisch–Waugh theorem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frisch–Waugh theorem
Context triple: [Frisch–Waugh–Lovell theorem, alsoKnownAs, Frisch–Waugh theorem]
  • A. Frisch–Waugh–Lovell theorem chosen
    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.
  • B. Gauss–Markov theorem
    The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
  • C. “Statistical Confluence Analysis by Means of Complete Regression Systems”
    “Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
  • D. Heckman selection model
    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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c008dac1ec81909cef8157ccd69962 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0686764648190864163d390db292d completed March 22, 2026, 10:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69c638791ce8819081aeec3b11e1c96e completed March 27, 2026, 7:57 a.m.
Created at: March 22, 2026, 4:34 p.m.