Triple
T20018156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rauma |
E494776
|
entity |
| Predicate | hasMountain |
P10602
|
FINISHED |
| Object | Romsdalshornet |
—
|
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: Romsdalshornet | Statement: [Rauma, hasMountain, Romsdalshornet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romsdalshornet Context triple: [Rauma, hasMountain, Romsdalshornet]
-
A.
Romsdalshornet
chosen
Romsdalshornet is a prominent and steep-sided mountain peak in Norway’s Romsdal valley, popular with climbers and hikers for its dramatic alpine scenery and classic climbing routes.
-
B.
Hermannsdalstinden
Hermannsdalstinden is a prominent mountain peak in Norway’s Lofoten archipelago, renowned for its dramatic alpine scenery and challenging hiking routes.
-
C.
Tallkrogen
Tallkrogen is a residential district in southern Stockholm, Sweden, known for its small-scale housing and garden-city character.
-
D.
Kolåstinden
Kolåstinden is a prominent alpine peak in Norway’s Sunnmøre Alps, renowned among hikers and ski mountaineers for its steep slopes and panoramic fjord views.
-
E.
Høybuktmoen
Høybuktmoen is an area in Sør-Varanger, Norway, known primarily for hosting Kirkenes Airport and military installations.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623d76808190988990a8dc263ef7 |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.