Triple
T19560632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Røyken |
E489438
|
entity |
| Predicate | otherSettlement |
P53596
|
FINISHED |
| Object |
Slemmestad
Slemmestad is a village in Asker Municipality in Viken county, Norway, known historically for its cement industry along the Oslofjord.
|
E1467453
|
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: Slemmestad | Statement: [Røyken, otherSettlement, Slemmestad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slemmestad Context triple: [Røyken, otherSettlement, Slemmestad]
-
A.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
B.
Svarstad
Svarstad is a Norwegian surname associated with individuals such as Maren Svarstad.
-
C.
Slemdal
Slemdal is a residential neighborhood in the Vestre Aker borough of Oslo, Norway, known for its green surroundings and affluent character.
-
D.
Smestad
Smestad is a residential neighborhood in Oslo, Norway, known for its affluent housing and proximity to green areas and good public transport.
-
E.
Holmestrand
Holmestrand is a coastal town and municipality in Vestfold, Norway, known for its harbor, steep hillsides, and role as a regional transport hub along the Oslofjord.
- 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: Slemmestad Triple: [Røyken, otherSettlement, Slemmestad]
Generated description
Slemmestad is a village in Asker Municipality in Viken county, Norway, known historically for its cement industry along the Oslofjord.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Slemmestad Target entity description: Slemmestad is a village in Asker Municipality in Viken county, Norway, known historically for its cement industry along the Oslofjord.
-
A.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
B.
Svarstad
Svarstad is a Norwegian surname associated with individuals such as Maren Svarstad.
-
C.
Slemdal
Slemdal is a residential neighborhood in the Vestre Aker borough of Oslo, Norway, known for its green surroundings and affluent character.
-
D.
Smestad
Smestad is a residential neighborhood in Oslo, Norway, known for its affluent housing and proximity to green areas and good public transport.
-
E.
Holmestrand
Holmestrand is a coastal town and municipality in Vestfold, Norway, known for its harbor, steep hillsides, and role as a regional transport hub along the Oslofjord.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f7442e08190ad030151ec0a97d4 |
completed | April 20, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0965c74be4819092c3bdbc2ac36aff |
completed | May 17, 2026, 6:52 a.m. |
| NEDg | Description generation | batch_6a0966a973c88190a80dea4cac560614 |
completed | May 17, 2026, 6:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09671c01a08190a9034fdb91119c3f |
completed | May 17, 2026, 6:58 a.m. |
Created at: April 10, 2026, 1:42 p.m.