Triple
T10745982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siebengebirge region |
E253449
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Königswinter |
E432458
|
NE FINISHED |
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: Königswinter | Statement: [Siebengebirge region, nearbySettlement, Königswinter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Königswinter Context triple: [Siebengebirge region, nearbySettlement, Königswinter]
-
A.
Königswinter
chosen
Königswinter is a town on the right bank of the Rhine in North Rhine-Westphalia, Germany, known for the Drachenfels hill and its scenic location in the Siebengebirge range.
-
B.
Fürstenzug
Fürstenzug is a famous large porcelain mural in Dresden depicting a procession of Saxon rulers.
-
C.
Am Hof
Am Hof is a historic square in Vienna’s Innere Stadt district, known for its medieval origins, notable architecture, and role as a former center of civic and religious life.
-
D.
Bad Muskau
Bad Muskau is a spa town in eastern Germany best known for the UNESCO-listed Muskau Park, a vast landscaped park that spans the German-Polish border.
-
E.
Dinkelscherben
Dinkelscherben is a municipality in the Swabian region of Bavaria in southern Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d711b77c4881909d16c6e82a9b86ca |
completed | April 9, 2026, 2:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de230b461c81909a98085079676b95 |
completed | April 14, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:15 p.m.