Triple
T23587187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernoulli differential equation |
E582378
|
entity |
| Predicate | isNonlinearFor |
P89388
|
FINISHED |
| Object | n ≠ 0,1 |
—
|
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: n ≠ 0,1 | Statement: [Bernoulli differential equation, isNonlinearFor, n ≠ 0,1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNonlinearFor Context triple: [Bernoulli differential equation, isNonlinearFor, n ≠ 0,1]
-
A.
isLinear
Indicates that a relationship, function, or structure preserves linearity, typically meaning it satisfies additivity and homogeneity (or forms a straight-line dependence between variables).
-
B.
typeOfNonlinearity
chosen
Indicates the specific kind or form of nonlinearity that characterizes how one entity behaves or responds in relation to another.
-
C.
isNonzeroFor
Indicates that a given value, function, or quantity is not equal to zero under specified conditions or for specified inputs.
-
D.
isNonUniform
Indicates that the property, distribution, or structure of something varies across its domain rather than remaining constant or uniform.
-
E.
isNonSimple
Indicates that the relationship or structure in question is not simple, typically meaning it has additional complexity, such as multiple components, repetitions, or self-intersections.
- 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_69e248f8d8248190acd5aee77f0d1709 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b03195748190b7e34f334902ac93 |
completed | April 29, 2026, 7:16 a.m. |
| PD | Predicate disambiguation | batch_69f118c96a0081908a8ac98ef7e7e60c |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:41 p.m.