Triple
T2820864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langer See, Grünau |
E54804
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Dahme river system |
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 river system | Statement: [Langer See, Grünau, partOf, Dahme river system]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dahme river system Context triple: [Langer See, Grünau, partOf, Dahme river system]
-
A.
Dahme
chosen
The Dahme is a river in eastern Germany that flows through Brandenburg and Berlin before joining the Spree.
-
B.
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.
-
C.
River Mulde
The River Mulde is a river in central Germany that flows through the states of Saxony and Saxony-Anhalt before joining the Elbe.
-
D.
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.
-
E.
Chemnitz River
The Chemnitz River is a waterway in the German state of Saxony that flows through and gives its name to the city of Chemnitz.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde6e85008190a08eb2bf8e393e7e |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afcea809e48190b22f25a3c8c1acdd |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:59 p.m.