Triple
T9843711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cauchy distribution |
E239287
|
entity |
| Predicate | hasAllMoments |
P90308
|
FINISHED |
| Object | do not exist |
—
|
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: do not exist | Statement: [Cauchy distribution, hasAllMoments, do not exist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAllMoments Context triple: [Cauchy distribution, hasAllMoments, do not exist]
-
A.
hasMonth
Indicates that something is associated with, occurs in, or is assigned to a specific month.
-
B.
hasConceptOfTime
Indicates that an entity possesses an understanding or awareness of time as an abstract concept or measurable dimension.
-
C.
meetsEvery
Indicates that one entity encounters or comes into contact with every member of a specified set of entities.
-
D.
hasMultiple
Indicates that an entity is associated with more than one instance or occurrence of another related entity.
-
E.
hasDateWith
Indicates that one entity is scheduled to go on or is engaged in a romantic or social date with another entity.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb35c8e348190aa090c71bf6f30eb |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06ace53081909b5f81f382f6591e |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:33 p.m.