Triple
T17752915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson distribution with P(s) = e^{-s} |
E443155
|
entity |
| Predicate | hasMeanSpacing |
P128214
|
FINISHED |
| Object | 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: 1 | Statement: [Poisson distribution with P(s) = e^{-s}, hasMeanSpacing, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMeanSpacing
Context triple: [Poisson distribution with P(s) = e^{-s}, hasMeanSpacing, 1]
-
A.
hasAlternativeSpacing
Indicates that an entity is associated with one or more alternative ways of spacing its characters or components compared to a primary or standard form.
-
B.
hasMeanDensity
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
C.
hasWellMeasuredDistances
Indicates that accurate and reliable distance measurements have been obtained between the related entities.
-
D.
hasStopSpacing
Indicates that there is a specified distance or interval between consecutive stops in a route or sequence.
-
E.
hasSpatialResolution
Indicates that something is characterized by a specific level of spatial detail or granularity at which it can represent or distinguish features in space.
- F. None of above. chosen
Provenance (4 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4841c0540819093a32d759775c61f |
completed | April 19, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69e3cde9dc288190af0e2198487f2051 |
completed | April 18, 2026, 6:31 p.m. |
| PDg | Predicate description generation | batch_69e3cfab7edc8190b663282d565a0389 |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:10 a.m.