Triple
T13489212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glienicke/Nordbahn |
E318588
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Oberhavel |
E78334
|
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: Oberhavel | Statement: [Glienicke/Nordbahn, partOf, Oberhavel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oberhavel Context triple: [Glienicke/Nordbahn, partOf, Oberhavel]
-
A.
Oberhavel (district)
chosen
Oberhavel is a rural district in the German state of Brandenburg, known for its lakes, forests, and proximity to northern Berlin.
-
B.
Luhe-Wildenau
Luhe-Wildenau is a municipality in the district of Neustadt an der Waldnaab in the Upper Palatinate region of Bavaria, Germany.
-
C.
Nordharz
Nordharz is a municipality in the Harz region of Saxony-Anhalt, Germany, known for its proximity to the Harz Mountains and its blend of rural landscapes and small-town settlements.
-
D.
Havelland
Havelland is a rural district in western Brandenburg, Germany, known for its river landscapes along the Havel, historic towns, and agricultural character.
-
E.
Niederschöneweide
Niederschöneweide is a locality in the Berlin borough of Treptow-Köpenick, known for its riverside setting along the Spree and its mix of residential areas and former industrial sites.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3cbe2081908c6792362c67c8f1 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d3afa0c81908733f3fd193d4e0f |
completed | May 3, 2026, 7:08 p.m. |
Created at: April 9, 2026, 9:43 p.m.