Triple
T23080700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bøler |
E575460
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Østmarka forest |
—
|
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: Østmarka forest | Statement: [Bøler, adjacentTo, Østmarka forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Østmarka forest Context triple: [Bøler, adjacentTo, Østmarka forest]
-
A.
Østmarka forest
chosen
Østmarka forest is a large, hilly woodland and recreational area on the eastern outskirts of Oslo, Norway, known for its lakes, hiking trails, and outdoor activities.
-
B.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
C.
Kvamskogen
Kvamskogen is a popular mountainous recreational area in western Norway known for its ski resorts, cabins, and outdoor activities.
-
D.
Leirskogen
Leirskogen is a small rural village located in the municipality of Sør-Aurdal in Innlandet county, Norway.
-
E.
Marselisborg Forests
Marselisborg Forests is a large recreational woodland area near Aarhus, Denmark, known for its scenic trails, coastal views, and outdoor leisure activities.
- 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.