Triple
T23080729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bøler |
E575460
|
entity |
| Predicate | hasNearbyLake |
P17985
|
FINISHED |
| Object | Nøklevann |
—
|
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: Nøklevann | Statement: [Bøler, hasNearbyLake, Nøklevann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nøklevann Context triple: [Bøler, hasNearbyLake, Nøklevann]
-
A.
Nøklevann
chosen
Nøklevann is a freshwater lake in the Østmarka forest area of Oslo, Norway, popular for outdoor recreation such as swimming, hiking, and fishing.
-
B.
Røssvatnet
Røssvatnet is one of Norway’s largest lakes, located in the northern part of the country and known for its scenic surroundings and hydroelectric significance.
-
C.
Maridalsvannet
Maridalsvannet is the largest lake supplying drinking water to Oslo, Norway, and a popular nearby recreation area.
-
D.
Sjusjøen
Sjusjøen is a popular Norwegian cross-country skiing destination and mountain village known for its extensive trail network and scenic highland landscapes near Lillehammer.
-
E.
Funnsjøen
Funnsjøen is a lake located in the municipality of Meråker in Trøndelag county, central Norway.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c67e06881908d24d6267bb49553 |
completed | April 29, 2026, 4:43 a.m. |
Created at: April 17, 2026, 3:56 p.m.