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.