Triple
T37760078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maroldsweisach |
E941240
|
entity |
| Predicate | hasHistoricFranconianCharacter |
P200658
|
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: [Maroldsweisach, hasHistoricFranconianCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricFranconianCharacter Context triple: [Maroldsweisach, hasHistoricFranconianCharacter, true]
-
A.
containsHistoricCharacters
Indicates that something includes or features characters who are historically significant or based on real historical figures.
-
B.
hasFranconianName
Indicates that an entity is associated with a name or designation in the Franconian language or dialect.
-
C.
isLocatedInFranconiaRegion
Indicates that something is situated within the geographical boundaries of the Franconia region.
-
D.
historicalLanguageOfBearers
Indicates that the specified language is historically spoken or used by the bearers of a given name, title, or designation.
-
E.
hasHistoricalOrigin
Indicates that something originated, was first established, or came into existence during a specific historical period or context.
- F. None of above. chosen
Provenance (4 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_69f76ee3251881909bb4451aad50752b |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff9d9cb4f8819083682be3c483b599 |
completed | May 9, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69ff9c38bf9c8190bbb85b32f3ae3d2e |
completed | May 9, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69ff9d9ba1ac8190a0cca5764bb5904d |
completed | May 9, 2026, 8:48 p.m. |
Created at: May 3, 2026, 4:19 p.m.