Triple
T23587253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernoulli distribution |
E582379
|
entity |
| Predicate | conjugatePrior |
P109213
|
FINISHED |
| Object | Beta distribution for p |
—
|
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: Beta distribution for p | Statement: [Bernoulli distribution, conjugatePrior, Beta distribution for p]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conjugatePrior Context triple: [Bernoulli distribution, conjugatePrior, Beta distribution for p]
-
A.
priorPost
Indicates that one post occurs earlier in sequence or time relative to another post.
-
B.
conjugateVariable
Indicates that one variable is the conjugate counterpart of another, typically related by a conjugation operation such as complex, algebraic, or canonical conjugation.
-
C.
conjugate
Indicates that one entity is a grammatical or mathematical counterpart of another, linked by a specific transformation such as verb inflection or complex-number pairing.
-
D.
conjugateTo
Indicates that one entity can be transformed into the other by a specific equivalence operation (such as similarity or group conjugation), so they are equivalent up to that transformation.
-
E.
definesPriorOver
chosen
Indicates that one entity specifies or establishes a prior probability distribution over another entity.
- F. None of above.
Provenance (3 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_69e248f8d8248190acd5aee77f0d1709 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b03195748190b7e34f334902ac93 |
completed | April 29, 2026, 7:16 a.m. |
| PD | Predicate disambiguation | batch_69f118c96a0081908a8ac98ef7e7e60c |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:41 p.m.