Triple
T19688125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tau |
E472765
|
entity |
| Predicate | usedAsSymbolFor |
P9907
|
FINISHED |
| Object | Kendall rank correlation coefficient |
—
|
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: Kendall rank correlation coefficient | Statement: [Tau, usedAsSymbolFor, Kendall rank correlation coefficient]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kendall rank correlation coefficient Context triple: [Tau, usedAsSymbolFor, Kendall rank correlation coefficient]
-
A.
Spearman rank-order correlation coefficient
The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
-
B.
Kruskal–Goodman measures of association
Kruskal–Goodman measures of association are nonparametric statistics used to quantify the strength and direction of association between ordinal variables.
-
C.
Pearson correlation coefficient
The Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables.
-
D.
Bhattacharyya coefficient
The Bhattacharyya coefficient is a statistical measure of similarity between two probability distributions, often used to quantify their overlap in fields like pattern recognition and signal processing.
-
E.
Kruskal–Wallis test
The Kruskal–Wallis test is a nonparametric statistical method used to determine whether there are statistically significant differences between the medians of three or more independent groups.
- 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: Kendall rank correlation coefficient Target entity description: The Kendall rank correlation coefficient is a nonparametric statistic that measures the strength and direction of association between two variables based on the concordance of their ranked data.
-
A.
Spearman rank-order correlation coefficient
The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
-
B.
Kruskal–Goodman measures of association
Kruskal–Goodman measures of association are nonparametric statistics used to quantify the strength and direction of association between ordinal variables.
-
C.
Pearson correlation coefficient
The Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables.
-
D.
Bhattacharyya coefficient
The Bhattacharyya coefficient is a statistical measure of similarity between two probability distributions, often used to quantify their overlap in fields like pattern recognition and signal processing.
-
E.
Kruskal–Wallis test
The Kruskal–Wallis test is a nonparametric statistical method used to determine whether there are statistically significant differences between the medians of three or more independent groups.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6420d39688190ad3a84dbffce4ffe |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:45 p.m.