Triple
T9495221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wagria |
E228986
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Timmendorfer Strand
Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
|
E802831
|
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: Timmendorfer Strand | Statement: [Wagria, contains, Timmendorfer Strand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timmendorfer Strand Context triple: [Wagria, contains, Timmendorfer Strand]
-
A.
Südstrand
Südstrand is a popular German seaside beach area known for its sandy shoreline, coastal promenades, and recreational tourism.
-
B.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
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.
-
E.
St. Peter-Ording
St. Peter-Ording is a popular seaside resort town on Germany’s North Sea coast, known for its expansive sandy beaches, stilt houses, and spa 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: Timmendorfer Strand Triple: [Wagria, contains, Timmendorfer Strand]
Generated description
Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Timmendorfer Strand Target entity description: Timmendorfer Strand is a popular seaside resort town on Germany’s Baltic Sea coast, known for its long sandy beaches and tourism.
-
A.
Südstrand
Südstrand is a popular German seaside beach area known for its sandy shoreline, coastal promenades, and recreational tourism.
-
B.
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
C.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
D.
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.
-
E.
St. Peter-Ording
St. Peter-Ording is a popular seaside resort town on Germany’s North Sea coast, known for its expansive sandy beaches, stilt houses, and spa 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95eb87b081908fc7255598cd9a24 |
completed | April 1, 2026, 10:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12d34967881909980be6f1be80885 |
completed | April 4, 2026, 3:24 p.m. |
| NEDg | Description generation | batch_69d13113474881909201282ce1385073 |
completed | April 4, 2026, 3:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d131ade0588190bdf3cfdbbdd6df8e |
completed | April 4, 2026, 3:43 p.m. |
Created at: March 30, 2026, 7:56 p.m.