Triple
T22666631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson process |
E559807
|
entity |
| Predicate | hasInterarrivalTimesVariance |
P27172
|
FINISHED |
| Object | 1/λ² |
—
|
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: 1/λ² | Statement: [Poisson process, hasInterarrivalTimesVariance, 1/λ²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInterarrivalTimesVariance Context triple: [Poisson process, hasInterarrivalTimesVariance, 1/λ²]
-
A.
hasVariance
chosen
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
-
B.
hasVariability
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
-
C.
hasVarianceSymbol
Indicates that one entity is associated with, or represented by, a specific variance symbol in a mathematical or statistical context.
-
D.
hasVariabilityType
Indicates that an entity is associated with a specific kind or category of variability (e.g., how or in what way it varies).
-
E.
hasStationaryMean
Indicates that the associated process or variable has a mean value that does not change over time.
- 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.