Triple
T25432929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurwitz theorem |
E637304
|
entity |
| Predicate | hasOptimalConstant |
P9148
|
FINISHED |
| Object | 1/sqrt(5) |
—
|
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: 1/sqrt(5) | Statement: [Hurwitz theorem, hasOptimalConstant, 1/sqrt(5)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOptimalConstant Context triple: [Hurwitz theorem, hasOptimalConstant, 1/sqrt(5)]
-
A.
canBeOptimizedFor
Indicates that one entity is capable of being improved or adjusted to perform better with respect to another specified criterion, context, or target.
-
B.
usesConstant
chosen
Indicates that one entity makes use of a specific constant value defined or provided by another entity.
-
C.
hasVariantConstant
Indicates that one entity is a specific constant-valued variant or fixed-value form of another entity.
-
D.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
E.
hasApproximationGuarantee
Indicates that there exists a formal bound on how close a solution or outcome is to the optimal one.
- 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_69e75db58a1c8190891b9ff7c2f8414e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 21, 2026, 1:58 p.m.