Triple
T17023469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siegen-Wittgenstein |
E413003
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Erndtebrück |
—
|
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: Erndtebrück | Statement: [Siegen-Wittgenstein, contains, Erndtebrück]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erndtebrück Context triple: [Siegen-Wittgenstein, contains, Erndtebrück]
-
A.
Erndtebrück
chosen
Erndtebrück is a municipality in the Siegen-Wittgenstein district of North Rhine-Westphalia, Germany, known for its rural setting in the Rothaar Mountains.
-
B.
Kreuztal
Kreuztal is a town in the Siegen-Wittgenstein district of North Rhine-Westphalia, Germany, known as an industrial and transport hub in the Siegerland region.
-
C.
Hersbruck
Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
-
D.
Schwabhausen
Schwabhausen is a municipality in Bavaria, Germany, known for its rural character and location within the greater Munich metropolitan region.
-
E.
Wuhletal
Wuhletal is a valley landscape in Berlin shaped by the course of the Wuhle river, featuring green spaces, walking paths, and recreational areas.
- 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.