Triple
T1756320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Germany |
E38556
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Schleswig-Holstein |
E45540
|
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: Schleswig-Holstein | Statement: [Northern Germany, hasPart, Schleswig-Holstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schleswig-Holstein Context triple: [Northern Germany, hasPart, Schleswig-Holstein]
-
A.
Schleswig-Holstein
chosen
Schleswig-Holstein is Germany’s northernmost state, known for its North Sea and Baltic Sea coastlines, maritime heritage, and shared border with Denmark.
-
B.
Mecklenburg-Vorpommern
Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
-
C.
Schleswig
Schleswig is a historic town in northern Germany known for its Viking heritage, medieval cathedral, and location on the Schlei inlet.
-
D.
Lower Saxony
Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
-
E.
Brandenburg
Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa643b623081908064be75758ec5de |
completed | March 6, 2026, 5:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c342f8f4819095cdf17553c02268 |
completed | March 14, 2026, 2:09 a.m. |
Created at: March 4, 2026, 7:31 p.m.