Triple
T2049922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schleswig-Holstein |
E45540
|
entity |
| Predicate | containsIsland |
P970
|
FINISHED |
| Object |
Sylt
Sylt is a popular German North Sea island known for its long sandy beaches, distinctive dune landscapes, and status as an upscale holiday destination.
|
E231952
|
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: Sylt | Statement: [Schleswig-Holstein, containsIsland, Sylt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sylt Context triple: [Schleswig-Holstein, containsIsland, Sylt]
-
A.
Gotland
Gotland is Sweden’s largest island, located in the Baltic Sea and known for its medieval town of Visby, limestone cliffs, and rich Viking-era history.
-
B.
Møn
Møn is a Danish island in the Baltic Sea known for its dramatic white chalk cliffs, scenic landscapes, and rich prehistoric and cultural heritage.
-
C.
Borkum
Borkum is a German North Sea island known for its seaside resorts, sandy beaches, and role as the westernmost of the East Frisian Islands.
-
D.
Rügen
Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
-
E.
Norderney
Norderney is a popular German North Sea island known for its sandy beaches, seaside resort town, and role as a major tourist destination in Lower Saxony.
- 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: Sylt Triple: [Schleswig-Holstein, containsIsland, Sylt]
Generated description
Sylt is a popular German North Sea island known for its long sandy beaches, distinctive dune landscapes, and status as an upscale holiday destination.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sylt Target entity description: Sylt is a popular German North Sea island known for its long sandy beaches, distinctive dune landscapes, and status as an upscale holiday destination.
-
A.
Gotland
Gotland is Sweden’s largest island, located in the Baltic Sea and known for its medieval town of Visby, limestone cliffs, and rich Viking-era history.
-
B.
Møn
Møn is a Danish island in the Baltic Sea known for its dramatic white chalk cliffs, scenic landscapes, and rich prehistoric and cultural heritage.
-
C.
Borkum
Borkum is a German North Sea island known for its seaside resorts, sandy beaches, and role as the westernmost of the East Frisian Islands.
-
D.
Rügen
Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
-
E.
Norderney
Norderney is a popular German North Sea island known for its sandy beaches, seaside resort town, and role as a major tourist destination in Lower Saxony.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb98e10d48190bb96cd1f8ea3c08b |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae27170870819083112aee662c681c |
completed | March 9, 2026, 1:49 a.m. |
| NEDg | Description generation | batch_69ae29ee1db88190b137cf3840cb1146 |
completed | March 9, 2026, 2:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae2a3bd7c08190898c7a7627cabf2b |
completed | March 9, 2026, 2:02 a.m. |
Created at: March 4, 2026, 7:39 p.m.