Triple
T8883114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwangau |
E211457
|
entity |
| Predicate | hasBodyOfWater |
P1778
|
FINISHED |
| Object | Schwansee |
E215122
|
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: Schwansee | Statement: [Schwangau, hasBodyOfWater, Schwansee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwansee Context triple: [Schwangau, hasBodyOfWater, Schwansee]
-
A.
Schwansee
chosen
Schwansee is a picturesque alpine lake in Bavaria, Germany, known for its scenic setting near Neuschwanstein and Hohenschwangau castles.
-
B.
Tachinger See
Tachinger See is a small scenic lake in the Chiemgau region of Bavaria, Germany, known for swimming, fishing, and its tranquil natural surroundings.
-
C.
Gebesee
Gebesee is a small town in the German state of Thuringia, situated near the confluence of the Unstrut and Gera rivers and known for its agricultural surroundings.
-
D.
Stadtsee
Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
-
E.
Plau am See
Plau am See is a small town and lakeside resort in the Mecklenburg Lake District of northern Germany, known for its natural scenery and water-based recreation.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616b2d988190b923ef1e33aab787 |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b54de2cc8190b14e3e726b4e9384 |
completed | April 5, 2026, 7:17 p.m. |
Created at: March 30, 2026, 6:53 p.m.