Triple
T15751586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyngen Alps |
E381858
|
entity |
| Predicate | hasPeak |
P8205
|
FINISHED |
| Object |
Storgalten
Storgalten is a prominent mountain peak in Norway’s Lyngen Alps, known for its striking alpine scenery and challenging ski touring routes.
|
E1183937
|
NE FINISHED |
How this triple was built (4 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: Storgalten | Statement: [Lyngen Alps, hasPeak, Storgalten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Storgalten Context triple: [Lyngen Alps, hasPeak, Storgalten]
-
A.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
B.
Svingvoll
Svingvoll is a small village in Innlandet county, Norway, known for its rural setting and proximity to skiing and outdoor recreation areas.
-
C.
Bålsta
Bålsta is a locality in Uppsala County, Sweden, known as the main urban center of Håbo Municipality and a commuter town within the Greater Stockholm region.
-
D.
Storå
Storå is a small coastal settlement located on the shores of the fjord in the former municipality of Tysfjord in Nordland county, Norway.
-
E.
Storå
Storå is a river in western Jutland, Denmark, known for flowing through the town of Holstebro and contributing to the region’s natural and cultural landscape.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Storgalten Triple: [Lyngen Alps, hasPeak, Storgalten]
Generated description
Storgalten is a prominent mountain peak in Norway’s Lyngen Alps, known for its striking alpine scenery and challenging ski touring routes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Storgalten Target entity description: Storgalten is a prominent mountain peak in Norway’s Lyngen Alps, known for its striking alpine scenery and challenging ski touring routes.
-
A.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
B.
Svingvoll
Svingvoll is a small village in Innlandet county, Norway, known for its rural setting and proximity to skiing and outdoor recreation areas.
-
C.
Bålsta
Bålsta is a locality in Uppsala County, Sweden, known as the main urban center of Håbo Municipality and a commuter town within the Greater Stockholm region.
-
D.
Storå
Storå is a small coastal settlement located on the shores of the fjord in the former municipality of Tysfjord in Nordland county, Norway.
-
E.
Storå
Storå is a river in western Jutland, Denmark, known for flowing through the town of Holstebro and contributing to the region’s natural and cultural landscape.
- F. None of above. chosen
Provenance (5 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb59779788190a393237f5293fe8d |
completed | May 9, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69ffb62f3d8881908ede4a9a4b53bef2 |
completed | May 9, 2026, 10:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb6f3154481909632913f4d7cfdba |
completed | May 9, 2026, 10:36 p.m. |
Created at: April 10, 2026, 4:47 a.m.