Triple
T17752832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaussian orthogonal ensemble |
E443153
|
entity |
| Predicate | matrixEntryVariance |
P27172
|
FINISHED |
| Object | depends on normalization convention |
—
|
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: depends on normalization convention | Statement: [Gaussian orthogonal ensemble, matrixEntryVariance, depends on normalization convention]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matrixEntryVariance Context triple: [Gaussian orthogonal ensemble, matrixEntryVariance, depends on normalization convention]
-
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.
hasVarianceSymbol
Indicates that one entity is associated with, or represented by, a specific variance symbol in a mathematical or statistical context.
-
C.
numberOfIndependentMatrices
Indicates the count of matrices in a set that are linearly independent from each other.
-
D.
usesVAR
Indicates that one entity makes use of, employs, or utilizes another entity as a variable or resource in performing some function or operation.
-
E.
hasCovarianceStructure
Indicates that one entity possesses or is associated with a specific covariance structure that characterizes how its variables co-vary.
- 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_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. |
Created at: April 10, 2026, 10:10 a.m.