Triple
T22005586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramsen (Switzerland) |
E543443
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object | Stein am Rhein |
—
|
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: Stein am Rhein | Statement: [Ramsen (Switzerland), neighboringMunicipality, Stein am Rhein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stein am Rhein Context triple: [Ramsen (Switzerland), neighboringMunicipality, Stein am Rhein]
-
A.
Stein am Rhein
chosen
Stein am Rhein is a small medieval town in northern Switzerland renowned for its well-preserved old town and richly painted half-timbered houses along the Rhine River.
-
B.
Weil am Rhein
Weil am Rhein is a German town in the state of Baden-Württemberg, located at the tripoint border with France and Switzerland near Basel.
-
C.
Schönaich
Schönaich is a municipality in the German state of Baden-Württemberg, known for its local community life and international town twinning partnerships.
-
D.
Biesheim
Biesheim is a commune in the Haut-Rhin department of northeastern France, near the Rhine River and the German border.
-
E.
Notzingen
Notzingen is a small municipality in the German state of Baden-Württemberg, located in the Stuttgart region.
- 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_69e11e2db934819095556760c7d85e4d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127a0afa881908318a4ebe211ca28 |
completed | April 28, 2026, 9:33 p.m. |
Created at: April 16, 2026, 8:21 p.m.