Triple
T23801795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nirenberg problem |
E588691
|
entity |
| Predicate | hasApplications |
P14571
|
FINISHED |
| Object | construction of metrics with prescribed curvature on surfaces |
—
|
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: construction of metrics with prescribed curvature on surfaces | Statement: [Nirenberg problem, hasApplications, construction of metrics with prescribed curvature on surfaces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApplications Context triple: [Nirenberg problem, hasApplications, construction of metrics with prescribed curvature on surfaces]
-
A.
hasApp
chosen
Indicates that an entity possesses, provides, or is associated with a particular application.
-
B.
hasApplicationType
Indicates that an entity is associated with or classified by a specific type or category of application.
-
C.
hasCommonApplication
Indicates that two or more entities share at least one typical or frequent use, purpose, or practical application in common.
-
D.
hearsApplications
Indicates that an entity (such as a person or body) formally receives and considers applications submitted by others.
-
E.
hasModernApplication
Indicates that something is currently used or applicable in modern contexts, practices, or technologies.
- 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_69e25d15db58819092ac1e6791696fd9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c74e430481909debd10c71785912 |
completed | April 29, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:53 p.m.