Triple

T19991958
Position Surface form Disambiguated ID Type / Status
Subject Gibbard–Satterthwaite theorem E494084 entity
Predicate relatesTo P37 FINISHED
Object Satterthwaite's theorem NE NERFINISHED

How this triple was built (3 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: Satterthwaite's theorem | Statement: [Gibbard–Satterthwaite theorem, relatesTo, Satterthwaite's theorem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Satterthwaite's theorem
Context triple: [Gibbard–Satterthwaite theorem, relatesTo, Satterthwaite's theorem]
  • A. Behrens–Fisher problem
    The Behrens–Fisher problem is a classic statistical inference problem concerning the comparison of means from two normal populations with unknown and unequal variances.
  • B. Scheffé's method
    Scheffé's method is a conservative multiple comparison procedure in analysis of variance that provides simultaneous confidence intervals for all possible contrasts among group means.
  • C. Hotelling’s T-squared distribution
    Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
  • D. Dunnett's test
    Dunnett's test is a multiple comparison statistical procedure used to compare several treatment groups directly against a single control group while controlling the overall type I error rate.
  • E. 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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Satterthwaite's theorem
Target entity description: Satterthwaite's theorem is a foundational result in social choice theory that characterizes the limitations of fair and non-manipulable voting systems, closely associated with the Gibbard–Satterthwaite theorem.
  • A. Behrens–Fisher problem
    The Behrens–Fisher problem is a classic statistical inference problem concerning the comparison of means from two normal populations with unknown and unequal variances.
  • B. Scheffé's method
    Scheffé's method is a conservative multiple comparison procedure in analysis of variance that provides simultaneous confidence intervals for all possible contrasts among group means.
  • C. Hotelling’s T-squared distribution
    Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
  • D. Dunnett's test
    Dunnett's test is a multiple comparison statistical procedure used to compare several treatment groups directly against a single control group while controlling the overall type I error rate.
  • E. 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.
  • F. None of above. chosen

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_69da626a67648190af9653832a3aeced completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65fe10ffc81908c94168b0a8ea9c9 completed April 20, 2026, 5:18 p.m.
Created at: April 11, 2026, 3:31 p.m.