Triple
T27675199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaussian quadrature rules |
E697759
|
entity |
| Predicate | degreeOfExactness |
P9771
|
FINISHED |
| Object | 2n-1 for n nodes |
—
|
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: 2n-1 for n nodes | Statement: [Gaussian quadrature rules, degreeOfExactness, 2n-1 for n nodes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: degreeOfExactness Context triple: [Gaussian quadrature rules, degreeOfExactness, 2n-1 for n nodes]
-
A.
constantExactness
Indicates that a value or relationship holds with complete, unvarying precision, without any approximation or deviation.
-
B.
curacy
Indicates that one entity serves in the role or position of a curate (assistant clergy) in relation to another entity, typically a parish or church.
-
C.
accuracyDependsOn
Indicates that the accuracy of one entity or process is contingent upon, or influenced by, another entity or factor.
-
D.
hasAccuracy
Indicates that something possesses a specified level or measure of correctness, precision, or exactness in relation to a standard or reference.
-
E.
precision
chosen
Indicates the degree to which an action, measurement, or outcome is carried out with exactness, minimal deviation, and fine-grained accuracy.
- 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_69ef590d458c81909583290c3cd0478b |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 2:43 p.m.