Triple
T9843725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cauchy distribution |
E239287
|
entity |
| Predicate | hasKurtosis |
P90313
|
FINISHED |
| Object | undefined |
—
|
LITERAL 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: undefined | Statement: [Cauchy distribution, hasKurtosis, undefined]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKurtosis Context triple: [Cauchy distribution, hasKurtosis, undefined]
-
A.
excessKurtosis
Indicates that a distribution’s kurtosis exceeds that of a normal distribution, reflecting heavier or lighter tails relative to the normal case.
-
B.
hasHeavierTailsThan
Indicates that the probability distribution of one entity has heavier tails—i.e., higher likelihood of extreme values—than the probability distribution of another entity.
-
C.
hasSkewness
Indicates that a distribution or dataset exhibits a specific degree and direction of asymmetry around its central value.
-
D.
hasKPoint
Indicates that an entity possesses or is associated with a specific K-point, typically a designated point in reciprocal or parameter space.
-
E.
hasVariance
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
- F. None of above. chosen
Provenance (4 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb35c8e348190aa090c71bf6f30eb |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06ace53081909b5f81f382f6591e |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:33 p.m.