Triple
T17023460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siegen-Wittgenstein |
E413003
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Bad Berleburg |
—
|
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: Bad Berleburg | Statement: [Siegen-Wittgenstein, contains, Bad Berleburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Berleburg Context triple: [Siegen-Wittgenstein, contains, Bad Berleburg]
-
A.
Bad Berleburg
chosen
Bad Berleburg is a spa town in the Siegen-Wittgenstein district of North Rhine-Westphalia, Germany, known for its historic castle and location in the Rothaar Mountains.
-
B.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
C.
Harbach
Harbach is a surname most notably associated with Otto Harbach, an American lyricist and librettist of early 20th-century musical theatre.
-
D.
Hückeswagen
Hückeswagen is a small historic town in western Germany’s North Rhine-Westphalia, known for its medieval castle and location in the hilly Bergisches Land region.
-
E.
Wehrheim
Wehrheim is a small municipality in the Hochtaunus district of Hesse, Germany, known for its rural character and proximity to the Taunus mountain range.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d2abbc81908943becf5f539fc6 |
completed | April 18, 2026, 7:04 p.m. |
Created at: April 10, 2026, 5:33 a.m.