Triple
T20261942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heinrich Ehrhardt |
E498860
|
entity |
| Predicate | hasHeritage |
P1494
|
FINISHED |
| Object | Thuringian |
—
|
NE NERFINISHED |
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: Thuringian | Statement: [Heinrich Ehrhardt, hasHeritage, Thuringian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thuringian Context triple: [Heinrich Ehrhardt, hasHeritage, Thuringian]
-
A.
Thuringian dialect
chosen
The Thuringian dialect is a group of East Central German vernaculars spoken primarily in the German state of Thuringia and surrounding central regions.
-
B.
Rhenish Franconian
Rhenish Franconian is a group of West Central German dialects spoken primarily in parts of western Germany, Luxembourg, and eastern France.
-
C.
Nuremberg dialect
The Nuremberg dialect is a regional variety of East Franconian German traditionally spoken in and around the city of Nuremberg in Bavaria.
-
D.
Giessen dialect
The Giessen dialect is a regional variety of German spoken around the city of Gießen, belonging to the Central Hessian group of dialects.
-
E.
Meissen dialect
The Meissen dialect is a variety of the Upper Saxon German dialect traditionally spoken around the town of Meissen in eastern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674cba2748190a886ecd8316dc518 |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:41 p.m.