Triple
T23587188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernoulli differential equation |
E582378
|
entity |
| Predicate | becomesLinearFor |
P149524
|
FINISHED |
| Object | n = 0 |
—
|
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 | Statement: [Bernoulli differential equation, becomesLinearFor, n = 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: becomesLinearFor Context triple: [Bernoulli differential equation, becomesLinearFor, n = 0]
-
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.
canBeLinearizedAs
chosen
Indicates that one entity can be represented or transformed into an equivalent linear (ordered or sequential) form as the other entity.
-
C.
linearity
Indicates that a relationship between quantities preserves addition and scalar multiplication, so outputs change in direct proportion to inputs.
-
D.
isLinearGroupVia
Indicates that one group is realized as a linear group via a specific faithful representation into a group of invertible matrices (or linear transformations).
-
E.
supportsLinearPrediction
Indicates that one entity provides the necessary structure or properties for another entity to perform or rely on linear prediction.
- 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.