Triple
T20601180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernburg Castle |
E506183
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bernburg |
—
|
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: Bernburg | Statement: [Bernburg Castle, locatedIn, Bernburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernburg Context triple: [Bernburg Castle, locatedIn, Bernburg]
-
A.
Bernburg
chosen
Bernburg is a town in the German state of Saxony-Anhalt, historically known for its castle overlooking the Saale River and its role as an industrial and cultural center in the region.
-
B.
Rudolstadt
Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
-
C.
Basdorf
Basdorf is a village and district within the municipality of Wandlitz in the state of Brandenburg, Germany.
-
D.
Weiterstadt
Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
-
E.
Wernigerode
Wernigerode is a picturesque German town in Saxony-Anhalt known for its colorful half-timbered houses, medieval castle, and location on the northern slopes of the Harz Mountains.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa1ffd088190adeacb9fe4907530 |
completed | April 20, 2026, 10:35 p.m. |
Created at: April 16, 2026, 11:41 a.m.