Triple
T22666637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson process |
E559807
|
entity |
| Predicate | hasProbabilityGeneratingFunctionOfN(t) |
P9754
|
FINISHED |
| Object | exp(λt(z − 1)) |
—
|
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: exp(λt(z − 1)) | Statement: [Poisson process, hasProbabilityGeneratingFunctionOfN(t), exp(λt(z − 1))]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProbabilityGeneratingFunctionOfN(t) Context triple: [Poisson process, hasProbabilityGeneratingFunctionOfN(t), exp(λt(z − 1))]
-
A.
hasSuccessProbabilitySymbol
Indicates that an entity is associated with a symbolic representation of its probability of success.
-
B.
hasFailureProbabilitySymbol
Indicates that an entity is associated with a symbolic representation of its probability of failure.
-
C.
hasDistributionFunction
chosen
Indicates that an entity is associated with a specific distribution function that characterizes how its values or occurrences are probabilistically or statistically distributed.
-
D.
hasNumberOfOutcomesPerTrial
Indicates that an entity (such as an experiment or trial) is associated with a specific count of possible outcomes for each individual trial.
-
E.
hasMomentGeneratingFunction
Indicates that a random variable or probability distribution possesses a well-defined moment generating function characterizing all of its moments.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1781c2c808190baf6964ca1eced6f |
completed | April 29, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:09 p.m.