Triple
T21287807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiehen Hills |
E524706
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Bad Essen |
—
|
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 Essen | Statement: [Wiehen Hills, nearbyCity, Bad Essen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Essen Context triple: [Wiehen Hills, nearbyCity, Bad Essen]
-
A.
Bad Essen
chosen
Bad Essen is a small spa town in Lower Saxony, Germany, known for its historic center, saltwater thermal baths, and location near the Wiehen Hills.
-
B.
Bad Salzig
Bad Salzig is a spa district and riverside locality of the town of Boppard in Rhineland-Palatinate, Germany, known for its mineral springs and scenic setting along the Middle Rhine.
-
C.
Bad Bayersoien
Bad Bayersoien is a small spa village and municipality in Bavaria, Germany, known for its scenic lakeside setting and traditional Upper Bavarian character.
-
D.
Bad Nauheim
Bad Nauheim is a spa town in the German state of Hesse, historically known for its therapeutic mineral springs and health resorts.
-
E.
Bad Grönenbach
Bad Grönenbach is a spa town in the Bavarian Allgäu region of southern Germany, known for its health resorts and picturesque rural surroundings.
- 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_69e0b5171f6c8190a5d57201ede73811 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e736d7c57c8190bc4180ea590a62d4 |
completed | April 21, 2026, 8:35 a.m. |
Created at: April 16, 2026, 4:03 p.m.