Triple
T36467701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marchenko–Pastur law |
E898463
|
entity |
| Predicate | matrixModel |
P2006
|
FINISHED |
| Object | X X^T where X has i.i.d. entries with zero mean and finite variance |
—
|
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: X X^T where X has i.i.d. entries with zero mean and finite variance | Statement: [Marchenko–Pastur law, matrixModel, X X^T where X has i.i.d. entries with zero mean and finite variance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: matrixModel Context triple: [Marchenko–Pastur law, matrixModel, X X^T where X has i.i.d. entries with zero mean and finite variance]
-
A.
modelIn
Indicates that one entity serves as a representation or simulation of another entity.
-
B.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
D.
namespaceModel
Indicates a relationship where a model is defined within, or associated with, a particular namespace or logical grouping.
-
E.
concurrentModel
Indicates that two or more processes, activities, or states occur or are valid at the same time, potentially interacting or overlapping in execution.
- 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_69f76e58ebd88190b75d9b169b59d793 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.