Triple
T19050763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirichlet distribution |
E466249
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Dirichlet-multinomial distribution |
—
|
NE NERFINISHED |
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: Dirichlet-multinomial distribution | Statement: [Dirichlet distribution, relatedTo, Dirichlet-multinomial distribution]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dirichlet-multinomial distribution Context triple: [Dirichlet distribution, relatedTo, Dirichlet-multinomial distribution]
-
A.
Dirichlet distribution
chosen
The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
-
B.
Carsey-Werner Distribution
Carsey-Werner Distribution is a television distribution company best known for handling popular sitcoms produced by Carsey-Werner, including major hits from the late 1980s and 1990s.
-
C.
Dirichlet process models
Dirichlet process models are a class of Bayesian nonparametric models that allow flexible, potentially infinite mixture modeling without fixing the number of components in advance.
-
D.
Pearson distribution
The Pearson distribution is a family of continuous probability distributions introduced by Karl Pearson to flexibly model data with varying skewness and kurtosis.
-
E.
Pitman–Yor process models
Pitman–Yor process models are Bayesian nonparametric models that generalize Dirichlet process models by allowing power-law behavior and heavier-tailed distributions over clusters.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc02597c8190b39fd2c7b7e42258 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 10, 2026, 12:03 p.m.