Triple
T18927141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guldborgsund Municipality |
E463002
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Stubbekøbing
Stubbekøbing is a small historic town in southeastern Denmark known for its old harbor, well-preserved streets, and maritime heritage.
|
E1429670
|
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: Stubbekøbing | Statement: [Guldborgsund Municipality, containsSettlement, Stubbekøbing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stubbekøbing Context triple: [Guldborgsund Municipality, containsSettlement, Stubbekøbing]
-
A.
Rudkøbing
Rudkøbing is a small historic town on the Danish island of Langeland, known for its well-preserved old streets and as the birthplace of physicist Hans Christian Ørsted.
-
B.
Sakskøbing
Sakskøbing is a small town on the Danish island of Lolland, known for its historic church, harbor, and surrounding agricultural landscape.
-
C.
Tranekær
Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
-
D.
Vækerø
Vækerø is a residential and commercial area in Oslo, Norway, located along the western waterfront and known for its mix of housing, offices, and green spaces.
-
E.
Blangsted
Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
- 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: Stubbekøbing Triple: [Guldborgsund Municipality, containsSettlement, Stubbekøbing]
Generated description
Stubbekøbing is a small historic town in southeastern Denmark known for its old harbor, well-preserved streets, and maritime heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stubbekøbing Target entity description: Stubbekøbing is a small historic town in southeastern Denmark known for its old harbor, well-preserved streets, and maritime heritage.
-
A.
Rudkøbing
Rudkøbing is a small historic town on the Danish island of Langeland, known for its well-preserved old streets and as the birthplace of physicist Hans Christian Ørsted.
-
B.
Sakskøbing
Sakskøbing is a small town on the Danish island of Lolland, known for its historic church, harbor, and surrounding agricultural landscape.
-
C.
Tranekær
Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
-
D.
Vækerø
Vækerø is a residential and commercial area in Oslo, Norway, located along the western waterfront and known for its mix of housing, offices, and green spaces.
-
E.
Blangsted
Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
- 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_69d8dcfdbbb881909964fa5a75bd0b48 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c9bc36588190ae9cc3b8abf8afd4 |
completed | April 20, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a087b010eb481908d66983622ffa3d7 |
completed | May 16, 2026, 2:11 p.m. |
| NEDg | Description generation | batch_6a088014c30c8190937586dc3f23862e |
completed | May 16, 2026, 2:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08810e37c08190bd7e8b584204064c |
completed | May 16, 2026, 2:37 p.m. |
Created at: April 10, 2026, 11:59 a.m.