Triple
T34030863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guttenberg |
E872642
|
entity |
| Predicate | hasTraditionalFranconianCharacter |
P123967
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Guttenberg, hasTraditionalFranconianCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalFranconianCharacter Context triple: [Guttenberg, hasTraditionalFranconianCharacter, true]
-
A.
hasFranconianName
Indicates that an entity is associated with a name or designation in the Franconian language or dialect.
-
B.
hasTraditionalEnglishCharacter
Indicates that something possesses qualities, features, or style typically associated with traditional English culture or heritage.
-
C.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
D.
hasTraditionalRegionCharacter
chosen
Indicates that an entity possesses a characteristic or feature that is typical of, or traditionally associated with, a specific region.
-
E.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
- 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_69f349a2527c81909a7cd4bda94d70ad |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff56ef0a5c8190ae729d66a8cf7fc4 |
completed | May 9, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ff539859c481909ec56310da418688 |
completed | May 9, 2026, 3:32 p.m. |
Created at: May 1, 2026, 1:51 a.m.