Triple
T22820585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Årvoll |
E565216
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Lillomarka 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: Lillomarka forest | Statement: [Årvoll, near, Lillomarka forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lillomarka forest Context triple: [Årvoll, near, Lillomarka forest]
-
A.
Lillomarka forest
chosen
Lillomarka forest is a popular recreational woodland area in Oslo, Norway, known for its hiking, skiing, and outdoor activities.
-
B.
Lappwald forest
Lappwald forest is a wooded hill range in Lower Saxony, Germany, known for its mixed woodlands, hiking trails, and proximity to the town of Helmstedt.
-
C.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
D.
Taunus forests
Taunus forests are extensive, hilly woodlands in the Taunus mountain range of Hesse, Germany, known for their mixed forests, hiking trails, and spa-town surroundings.
-
E.
Leirskogen
Leirskogen is a small rural village located in the municipality of Sør-Aurdal in Innlandet county, 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_69e2458426188190b58b8ab4844fe420 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17dcffdd88190a5f9c780c71b5053 |
completed | April 29, 2026, 3:41 a.m. |
Created at: April 17, 2026, 3:33 p.m.