Triple
T21545514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kringsjå student village |
E531612
|
entity |
| Predicate | hasProximityTo |
P2064
|
FINISHED |
| Object | Nordmarka 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: Nordmarka forest | Statement: [Kringsjå student village, hasProximityTo, Nordmarka forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nordmarka forest Context triple: [Kringsjå student village, hasProximityTo, Nordmarka forest]
-
A.
Kvamskogen
Kvamskogen is a popular mountainous recreational area in western Norway known for its ski resorts, cabins, and outdoor activities.
-
B.
Leirskogen
Leirskogen is a small rural village located in the municipality of Sør-Aurdal in Innlandet county, Norway.
-
C.
Nordmarka
chosen
Nordmarka is a large forested recreational area north of Oslo, Norway, popular for hiking, skiing, and outdoor activities.
-
D.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
E.
Håhellerskarvet
Håhellerskarvet is a prominent mountain or ridge feature located within the Mühlig-Hofmann Mountains of Queen Maud Land in Antarctica.
- 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb58e38808190888f3501cf4fff7c |
completed | April 27, 2026, 1:02 a.m. |
Created at: April 16, 2026, 6:28 p.m.