Triple
T16196612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lysebotn |
E393076
|
entity |
| Predicate | hasNearbyAttraction |
P2064
|
FINISHED |
| Object | Kjerag |
E365817
|
NE FINISHED |
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: Kjerag | Statement: [Lysebotn, hasNearbyAttraction, Kjerag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjerag Context triple: [Lysebotn, hasNearbyAttraction, Kjerag]
-
A.
Kjerag
chosen
Kjerag is a famous mountain in Norway’s Lysefjord known for its towering cliffs, popular hiking routes, and the iconic Kjeragbolten boulder wedged between two rock faces.
-
B.
Kjerkeberget
Kjerkeberget is a forested hill in Norway that marks the highest natural point within Oslo’s municipal boundaries.
-
C.
Namdalseid
Namdalseid is a former rural municipality in Trøndelag county, Norway, known for its forests, agriculture, and coastal landscape along the Namsenfjorden.
-
D.
Namsskogan
Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
-
E.
Skaaren
Skaaren is a surname most notably associated with Warren Skaaren, an American screenwriter and film producer.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222dace848190b1a98e47333b922b |
completed | April 17, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0f352081908324783743e47029 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.