Triple
T22561527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gentofte Municipality |
E557826
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Dyssegård
Dyssegård is a residential neighborhood in the northern suburbs of Copenhagen, Denmark, known for its quiet streets and proximity to green areas.
|
E1551475
|
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: Dyssegård | Statement: [Gentofte Municipality, containsSettlement, Dyssegård]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dyssegård Context triple: [Gentofte Municipality, containsSettlement, Dyssegård]
-
A.
Tyssedal
Tyssedal is a small industrial village in Vestland county, Norway, known for its historic hydropower facilities and scenic location by the Sørfjorden.
-
B.
Dragsvik
Dragsvik is a Finnish military locality known for hosting a key coastal garrison of the Finnish Navy.
-
C.
Fyresdal
Fyresdal is a rural municipality in Telemark county, Norway, known for its forests, lakes, and traditional farming communities.
-
D.
Nadderud
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
-
E.
Drangedal
Drangedal is a rural municipality in southeastern Norway known for its forested landscape, lakes, and traditional farming communities.
- 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: Dyssegård Triple: [Gentofte Municipality, containsSettlement, Dyssegård]
Generated description
Dyssegård is a residential neighborhood in the northern suburbs of Copenhagen, Denmark, known for its quiet streets and proximity to green areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dyssegård Target entity description: Dyssegård is a residential neighborhood in the northern suburbs of Copenhagen, Denmark, known for its quiet streets and proximity to green areas.
-
A.
Tyssedal
Tyssedal is a small industrial village in Vestland county, Norway, known for its historic hydropower facilities and scenic location by the Sørfjorden.
-
B.
Dragsvik
Dragsvik is a Finnish military locality known for hosting a key coastal garrison of the Finnish Navy.
-
C.
Fyresdal
Fyresdal is a rural municipality in Telemark county, Norway, known for its forests, lakes, and traditional farming communities.
-
D.
Nadderud
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
-
E.
Drangedal
Drangedal is a rural municipality in southeastern Norway known for its forested landscape, lakes, and traditional farming communities.
- 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fa5f4008190921095b7aff4f4e2 |
completed | April 29, 2026, 1:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b8fafd1508190aeb4ea8322cee5ee |
completed | May 18, 2026, 10:16 p.m. |
| NEDg | Description generation | batch_6a0b90eb442481909bac0fc11e3a50be |
completed | May 18, 2026, 10:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b9153b6f88190982da7a3ed3774ff |
completed | May 18, 2026, 10:23 p.m. |
Created at: April 16, 2026, 8:52 p.m.