Triple
T25728654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volterra series |
E645177
|
entity |
| Predicate | firstOrderTermEquivalentTo |
P6530
|
FINISHED |
| Object | linear convolution |
—
|
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: linear convolution | Statement: [Volterra series, firstOrderTermEquivalentTo, linear convolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstOrderTermEquivalentTo Context triple: [Volterra series, firstOrderTermEquivalentTo, linear convolution]
-
A.
equivalentTo
chosen
Indicates that two entities represent the same concept, value, or state, and can be treated as interchangeable in the given context.
-
B.
firstTerms
Indicates that the related entities are the initial elements or starting terms in a sequence, series, or ordered collection.
-
C.
firstClauseKnownAs
Indicates that one clause in a legal or formal document is referred to or designated by a particular name or label.
-
D.
firstFactor
Indicates that one entity is the first factor (multiplicand) in a multiplication relationship with another entity.
-
E.
firstCongruence
Indicates that one entity is the initial or primary instance in a set of congruent (equivalent in form or measure) entities or relationships.
- 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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fd35d108908190b79b1e8e6bbd62aa |
completed | May 8, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fd34cb46108190b43c3b7f67ec4cd4 |
completed | May 8, 2026, 12:56 a.m. |
Created at: April 21, 2026, 11:09 p.m.