Triple
T13042056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iveland |
E327217
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Birkenes |
E308960
|
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: Birkenes | Statement: [Iveland, hasNeighbour, Birkenes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Birkenes Context triple: [Iveland, hasNeighbour, Birkenes]
-
A.
Birkenes
chosen
Birkenes is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and small villages.
-
B.
Enebakk
Enebakk is a rural municipality in Viken county, Norway, known for its forests, lakes, and proximity to the Oslo metropolitan area.
-
C.
Bjerke
Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
-
D.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
-
E.
Brynseng
Brynseng is a neighborhood and transport hub in Oslo, Norway, served by the Oslo Metro and other public transit connections.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804f0318819081516e2ca1de6797 |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbd5139c8190aaec6487f074f251 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:56 p.m.