Triple
T6755393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Großer Döllnsee |
E154443
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Schorfheide |
E316727
|
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: Schorfheide | Statement: [Großer Döllnsee, locatedIn, Schorfheide]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schorfheide Context triple: [Großer Döllnsee, locatedIn, Schorfheide]
-
A.
Schorfheide
chosen
Schorfheide is a large forested and lake-rich area in Brandenburg, Germany, known for its protected natural landscapes and historical use as a royal and political hunting ground.
-
B.
Schönewalde
Schönewalde is a town in the state of Brandenburg, Germany, known for hosting a German Air Force base.
-
C.
Zossen
Zossen is a town in Brandenburg, Germany, historically notable as a major military command center, including serving as a key headquarters area during the Soviet occupation after World War II.
-
D.
Ludwigsfelde
Ludwigsfelde is a town in the German state of Brandenburg, located just south of Berlin and known for its industrial history and automotive manufacturing.
-
E.
Wilmersdorf
Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
- 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_69c6880fd5808190be684854081e27dd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1f465388190858207ca4c48f18d |
completed | March 27, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a78feb8819084314c2ae043d289 |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:11 p.m.