Triple
T9990671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neu-Hohenschönhausen |
E196876
|
entity |
| Predicate | hasNeighbouringLocality |
P68061
|
FINISHED |
| Object | Wartenberg |
E767633
|
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: Wartenberg | Statement: [Neu-Hohenschönhausen, hasNeighbouringLocality, Wartenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wartenberg Context triple: [Neu-Hohenschönhausen, hasNeighbouringLocality, Wartenberg]
-
A.
Wartenberg
chosen
Wartenberg is a locality in the northeastern part of Berlin, Germany, known for its residential areas and proximity to green spaces.
-
B.
Willenberg
Willenberg is the former German name of the town now known as Wielbark, located in northern Poland.
-
C.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
D.
Wurmberg
Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
-
E.
Wipfeld
Wipfeld is a small municipality in northern Bavaria, Germany, situated along the Main River and known for its winegrowing and historic Franconian character.
- 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_69ca82f1678c819093d06320a05f16a4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdc7a0cb6481908d7bd1b43f93bd18 |
completed | April 2, 2026, 1:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d25821dde881909f5d4cc6ad048b01 |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:50 p.m.