Triple
T23113556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schweriner See |
E576385
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Schwerin skyline |
E138060
|
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: Schwerin skyline | Statement: [Schweriner See, hasViewOf, Schwerin skyline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwerin skyline Context triple: [Schweriner See, hasViewOf, Schwerin skyline]
-
A.
Schwerin
chosen
Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
-
B.
Schwanenwerder
Schwanenwerder is a small, affluent island neighborhood in southwestern Berlin, known for its exclusive villas and scenic location in the River Havel.
-
C.
Warnemünde
Warnemünde is a seaside district and popular Baltic Sea resort of the German city of Rostock, known for its wide sandy beaches and maritime atmosphere.
-
D.
Schwerin Castle
Schwerin Castle is a historic lakeside palace in Schwerin, Germany, renowned for its fairy-tale architecture and role as the seat of the state parliament of Mecklenburg-Vorpommern.
-
E.
Cölln
Cölln was a historic town on the River Spree that, together with Berlin, formed the core of what later became the city of Berlin.
- 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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e1121c08190a1d29fe594071c46 |
completed | April 29, 2026, 4:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23ec62f08190a4dda0d537a7b024 |
completed | May 19, 2026, 8:48 a.m. |
Created at: April 17, 2026, 3:59 p.m.