Triple
T9456240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langeland |
E228021
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Tranekær
Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
|
E832447
|
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: Tranekær | Statement: [Langeland, hasSettlement, Tranekær]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tranekær Context triple: [Langeland, hasSettlement, Tranekær]
-
A.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
B.
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.
-
C.
Hellebæk
Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
-
D.
Skjern
Skjern is a town in western Jutland, Denmark, known for its location near the Skjern River and its surrounding agricultural landscape.
-
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: Tranekær Triple: [Langeland, hasSettlement, Tranekær]
Generated description
Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tranekær Target entity description: Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
-
A.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
B.
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.
-
C.
Hellebæk
Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
-
D.
Skjern
Skjern is a town in western Jutland, Denmark, known for its location near the Skjern River and its surrounding agricultural landscape.
-
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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f8f7e1481909318e473ab4d6460 |
completed | April 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23caf70f8819090ba25c4395c3de2 |
completed | April 5, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_69d23e6ca3908190b7ad7b932ab35ad7 |
completed | April 5, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d241020074819092bc2deea85a6ac0 |
completed | April 5, 2026, 11:01 a.m. |
Created at: March 30, 2026, 7:52 p.m.