Triple
T9975524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göhren |
E196316
|
entity |
| Predicate | hasHarbour |
P3007
|
FINISHED |
| Object |
Göhren marina
Göhren marina is a small recreational harbor and docking facility serving leisure boats and tourists in the seaside resort of Göhren on the island of Rügen in Germany.
|
E831564
|
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: Göhren marina | Statement: [Göhren, hasHarbour, Göhren marina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Göhren marina Context triple: [Göhren, hasHarbour, Göhren marina]
-
A.
Timmendorfer Strand
Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
-
B.
Tegeler Hafen
Tegeler Hafen is a harbor area in Berlin’s Tegel district, known for its waterfront promenade, residential developments, and leisure boating on Lake Tegel.
-
C.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
D.
Heiligenhafen
Heiligenhafen is a coastal town in northern Germany on the Baltic Sea, known for its fishing harbor, beaches, and tourism.
-
E.
Travemünde
Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
- 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: Göhren marina Triple: [Göhren, hasHarbour, Göhren marina]
Generated description
Göhren marina is a small recreational harbor and docking facility serving leisure boats and tourists in the seaside resort of Göhren on the island of Rügen in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Göhren marina Target entity description: Göhren marina is a small recreational harbor and docking facility serving leisure boats and tourists in the seaside resort of Göhren on the island of Rügen in Germany.
-
A.
Timmendorfer Strand
Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
-
B.
Tegeler Hafen
Tegeler Hafen is a harbor area in Berlin’s Tegel district, known for its waterfront promenade, residential developments, and leisure boating on Lake Tegel.
-
C.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
D.
Heiligenhafen
Heiligenhafen is a coastal town in northern Germany on the Baltic Sea, known for its fishing harbor, beaches, and tourism.
-
E.
Travemünde
Travemünde is a Baltic Sea resort town and seaside district of Lübeck in northern Germany, known for its beaches, harbor, and maritime tourism.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb84b47308190aa2f94fa7320cdc3 |
completed | April 2, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23ddcc01c8190bb00ac13cbbbb1fb |
completed | April 5, 2026, 10:47 a.m. |
| NEDg | Description generation | batch_69d23eb33f8481909c1ec01e126455ca |
completed | April 5, 2026, 10:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d23f279df88190b155ec48e7588c66 |
completed | April 5, 2026, 10:53 a.m. |
Created at: March 30, 2026, 8:48 p.m.