Triple
T1382154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | standard normal distribution |
E29361
|
entity |
| Predicate | hasVariance |
P27172
|
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: [standard normal distribution, hasVariance, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVariance Context triple: [standard normal distribution, hasVariance, 1]
-
A.
hasVariability
Indicates that an entity exhibits variation or fluctuation in its state, value, or characteristics over time or across instances.
-
B.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
C.
hasVariantSystem
Indicates that one system is an alternative or variant form of another system within the same general framework or category.
-
D.
hasVow
Indicates that one entity has made or is bound by a formal vow or promise in relation to another entity or context.
-
E.
hasVector
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3361bf08190b3f6bbf82e17685b |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.