Triple
T7921612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacobi matrix |
E183956
|
entity |
| Predicate | spectralMeasure |
P79796
|
FINISHED |
| Object | associated orthogonality measure |
—
|
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: associated orthogonality measure | Statement: [Jacobi matrix, spectralMeasure, associated orthogonality measure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spectralMeasure Context triple: [Jacobi matrix, spectralMeasure, associated orthogonality measure]
-
A.
spectralTheory
Indicates the relationship between an operator (or matrix) and the structure of its spectrum—its eigenvalues, eigenvectors, and related spectral properties—typically within a functional-analytic or linear-algebraic context.
-
B.
spectralProperty
Indicates a relationship where an entity possesses or is characterized by a specific spectral feature, measurement, or behavior (e.g., in its frequency, wavelength, or energy spectrum).
-
C.
measurability
Indicates that a quantity, property, or outcome can be defined, quantified, or assessed using a consistent measurement framework.
-
D.
hasLebesgueMeasure
Indicates that a set is assigned a specific value by the Lebesgue measure, representing its "size" in the sense of measure theory.
-
E.
spectralResolution
Indicates the fineness with which a system can distinguish or separate different wavelengths or frequencies within a spectrum.
- 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_69ca828efbe48190bd48482650182e79 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a9499cc8190b6bd81f4625c77ab |
completed | March 31, 2026, 3:08 a.m. |
| PD | Predicate disambiguation | batch_69cae9316e98819080be7bf1a6ff92f1 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7882b048190baa333af9f698590 |
completed | March 30, 2026, 10:22 p.m. |
Created at: March 30, 2026, 5:06 p.m.