Triple
T18113121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ringebu |
E433530
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Fåvang |
—
|
NE NERFINISHED |
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: Fåvang | Statement: [Ringebu, hasSettlement, Fåvang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fåvang Context triple: [Ringebu, hasSettlement, Fåvang]
-
A.
Fåvang
chosen
Fåvang is a village in Innlandet county, Norway, known for its rural setting in the Gudbrandsdalen valley and its historic stave church.
-
B.
Vildbjerg
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
-
C.
Vækerø
Vækerø is a residential and commercial area in Oslo, Norway, located along the western waterfront and known for its mix of housing, offices, and green spaces.
-
D.
Rågsved
Rågsved is a suburban district in southern Stockholm, Sweden, known for its post-war residential architecture and metro station on the Green line.
-
E.
Nakskov
Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd3fd9c81909bfe95927f7553e3 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 10:28 a.m.