Triple
T10982370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scharmützelsee |
E259539
|
entity |
| Predicate | outflow |
P967
|
FINISHED |
| Object | Dahme |
E227470
|
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: Dahme | Statement: [Scharmützelsee, outflow, Dahme]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dahme Context triple: [Scharmützelsee, outflow, Dahme]
-
A.
Dahme
chosen
The Dahme is a river in eastern Germany that flows through Brandenburg and Berlin before joining the Spree.
-
B.
Dahme
Dahme is a small coastal town on the Baltic Sea in northern Germany, known for its beaches and seaside tourism.
-
C.
Oder-Spree
Oder-Spree is a rural district in the eastern German state of Brandenburg, known for its lakes, forests, and towns along the Oder and Spree rivers.
-
D.
River Spree
River Spree is a major river flowing through Berlin, Germany, known for shaping the city’s landscape and passing many historic and cultural landmarks.
-
E.
Unstrut River
The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d772eb518c8190a885a417815f2ff6 |
completed | April 9, 2026, 9:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6037fdf80819091fb2c8bf128582d |
completed | April 20, 2026, 10:44 a.m. |
Created at: April 8, 2026, 9:24 p.m.