Triple
T6594807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Cerberus |
E148448
|
entity |
| Predicate | destinationPort |
P1763
|
FINISHED |
| Object |
Brunsbüttel
Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
|
E607908
|
NE FINISHED |
How this triple was built (4 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: Brunsbüttel | Statement: [Operation Cerberus, destinationPort, Brunsbüttel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brunsbüttel Context triple: [Operation Cerberus, destinationPort, Brunsbüttel]
-
A.
Fuhlsbüttel
Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
-
B.
Norderstedt
Norderstedt is a city in northern Germany that forms part of the Hamburg metropolitan area and is one of the larger urban centers in the state of Schleswig-Holstein.
-
C.
Travemünde
Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
-
D.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Brunsbüttel Triple: [Operation Cerberus, destinationPort, Brunsbüttel]
Generated description
Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brunsbüttel Target entity description: Brunsbüttel is a German port town at the western entrance of the Kiel Canal on the North Sea coast of Schleswig-Holstein.
-
A.
Fuhlsbüttel
Fuhlsbüttel is a district in the northern German city of Hamburg best known for hosting the city’s international airport.
-
B.
Norderstedt
Norderstedt is a city in northern Germany that forms part of the Hamburg metropolitan area and is one of the larger urban centers in the state of Schleswig-Holstein.
-
C.
Travemünde
Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
-
D.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6d0a262808190a33ac94374affde4 |
completed | March 27, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e42d6ba08190beccfad588594780 |
completed | March 27, 2026, 8:10 p.m. |
| NEDg | Description generation | batch_69c6e7c75140819082a32e4662e0b07c |
completed | March 27, 2026, 8:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e8881b848190bd6184aeaf311d24 |
completed | March 27, 2026, 8:28 p.m. |
Created at: March 27, 2026, 1:55 p.m.