Triple
T10808000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kähler–Ricci flow |
E255017
|
entity |
| Predicate | normalizationPurpose |
P48405
|
FINISHED |
| Object | keep total volume fixed |
—
|
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: keep total volume fixed | Statement: [Kähler–Ricci flow, normalizationPurpose, keep total volume fixed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: normalizationPurpose Context triple: [Kähler–Ricci flow, normalizationPurpose, keep total volume fixed]
-
A.
normalizationAttempt
Indicates an effort to convert something into a standard or consistent form according to defined rules or criteria.
-
B.
usesNormalization
chosen
Indicates that one entity applies or relies on a normalization process or technique in relation to another entity or data.
-
C.
standardizationGoal
Indicates the intended level, outcome, or target state to be achieved through a process of standardization.
-
D.
refinesNormalization
Indicates that one normalization process or scheme improves, clarifies, or makes more precise another existing normalization.
-
E.
notationPurpose
Indicates that one notation is used with the specific purpose or function of representing, explaining, or supporting another entity or concept.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733b506488190921e6a1f4168dd9e |
completed | April 9, 2026, 5:05 a.m. |
| PD | Predicate disambiguation | batch_69d6f3188f00819094ee8d65b187a333 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:18 p.m.