Triple
T7317003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grimstad |
E168438
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object | Lillesand |
E143453
|
NE FINISHED |
How this triple was built (2 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: Lillesand | Statement: [Grimstad, neighboringMunicipality, Lillesand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lillesand Context triple: [Grimstad, neighboringMunicipality, Lillesand]
-
A.
Lillesand
chosen
Lillesand is a small coastal town and municipality in Agder county, Norway, known for its white wooden houses, maritime heritage, and popular summer tourism along the Skagerrak coast.
-
B.
Sandvika
Sandvika is a town in southeastern Norway that serves as the administrative center of Bærum and a commercial hub in the Greater Oslo Region.
-
C.
Lilleaker
Lilleaker is a residential neighborhood in western Oslo, Norway, known for its mix of housing, green areas, and proximity to the Lysaker River.
-
D.
Sagene
Sagene is a central district in Oslo, Norway, known for its historic industrial heritage along the Akerselva river and its mix of old workers’ housing and modern urban development.
-
E.
Larvik
Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef162d488190bf1c63b71b20a294 |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7eef3b1c48190ae65a136121b39cb |
completed | March 28, 2026, 3:08 p.m. |
Created at: March 27, 2026, 3:02 p.m.