Triple
T11022185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trehøje Municipality |
E260516
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object |
Vildbjerg
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
|
E901513
|
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: Vildbjerg | Statement: [Trehøje Municipality, hasCapital, Vildbjerg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vildbjerg Context triple: [Trehøje Municipality, hasCapital, Vildbjerg]
-
A.
Vadsø
Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
-
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.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
D.
Abildsø
Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
-
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: Vildbjerg Triple: [Trehøje Municipality, hasCapital, Vildbjerg]
Generated description
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vildbjerg Target entity description: Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
-
A.
Vadsø
Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
-
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.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
-
D.
Abildsø
Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
-
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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797bd88188190a644adc9283cabb8 |
completed | April 9, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a997d7bc8190982467039e0f5504 |
completed | April 18, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_69e3b1dfea348190a61eb19266801ad0 |
completed | April 18, 2026, 4:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b2c65fa081908038cc2f71e49073 |
completed | April 18, 2026, 4:35 p.m. |
Created at: April 8, 2026, 9:25 p.m.