Triple
T22666628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson process |
E559807
|
entity |
| Predicate | hasInterarrivalTimesNotation |
P140477
|
FINISHED |
| Object | T1, T2, … |
—
|
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: T1, T2, … | Statement: [Poisson process, hasInterarrivalTimesNotation, T1, T2, …]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInterarrivalTimesNotation Context triple: [Poisson process, hasInterarrivalTimesNotation, T1, T2, …]
-
A.
hasTimeIndication
Indicates that something includes, specifies, or is associated with a particular time-related indication (such as a timestamp, time period, or temporal marker).
-
B.
hasInterval
Indicates that something is associated with a specific span or range between two points in time, space, or value.
-
C.
hasTimingCapability
Indicates that an entity possesses the ability to measure, control, or manage timing-related aspects of an operation or process.
-
D.
hasTimeComponent
chosen
Indicates that something includes, is associated with, or is characterized by a specific temporal aspect or time-related element.
-
E.
hasDurationType
Indicates that something is associated with a specific kind or category of duration (e.g., temporary, permanent, short-term, long-term).
- 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.