Triple
T8002697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bærum |
E186288
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Fornebu |
E620142
|
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: Fornebu | Statement: [Bærum, hasSettlement, Fornebu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fornebu Context triple: [Bærum, hasSettlement, Fornebu]
-
A.
Sinsen
Sinsen is a neighborhood and major transport hub in Oslo, Norway, known for its busy traffic interchange and public transit connections.
-
B.
Oslo East
Oslo East is the eastern part of Norway’s capital city, often associated with working-class neighborhoods, cultural diversity, and a strong local football supporter culture.
-
C.
Lysaker
Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
-
D.
Ullensaker
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
E.
Fornebu, Norway
chosen
Fornebu, Norway is a coastal area in Bærum just outside Oslo, known for its transformation from the city’s former main airport into a modern hub for technology companies, offices, and residential developments.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf2918081909ee0afab11caed63 |
completed | March 31, 2026, 3:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63bf5efc8190aa5cdc6707adbf71 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 5:18 p.m.