Triple
T7367576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Treptow-Köpenick |
E169909
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Seddinsee |
E437924
|
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: Seddinsee | Statement: [Treptow-Köpenick, hasLake, Seddinsee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seddinsee Context triple: [Treptow-Köpenick, hasLake, Seddinsee]
-
A.
Seddinsee
chosen
Seddinsee is a lake in southeastern Berlin, Germany, known for its recreational boating, natural shoreline, and role as part of the city’s interconnected waterway network.
-
B.
Schwansee
Schwansee is a picturesque alpine lake in Bavaria, Germany, known for its scenic setting near Neuschwanstein and Hohenschwangau castles.
-
C.
Unterseen
Unterseen is a historic Swiss town in the Bernese Oberland, situated near Interlaken at the confluence of the Aare and Lombach rivers with views of the surrounding Alps.
-
D.
Wilsede
Wilsede is a small village in the Lüneburg Heath region of Lower Saxony, Germany, known for its well-preserved heathland landscape and traditional car-free character.
-
E.
Sorpesee
Sorpesee is a popular artificial lake in the Sauerland region of Germany, known for recreation, water sports, and scenic surroundings.
- 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_69c68a5ade988190885b7175f63b7534 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f17ea0608190955ac3474f6da7bb |
completed | March 27, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802bc25908190ad444de63b7526a0 |
completed | March 28, 2026, 4:33 p.m. |
Created at: March 27, 2026, 3:06 p.m.