Triple
T15501369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Solna centrum |
E378960
|
entity |
| Predicate | adjacentStation |
P5707
|
FINISHED |
| Object |
Västra skogen
Västra skogen is a Stockholm metro station in Solna, Sweden, known for its deep underground platforms and distinctive cavern-style design.
|
E1160274
|
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: Västra skogen | Statement: [Solna centrum, adjacentStation, Västra skogen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Västra skogen Context triple: [Solna centrum, adjacentStation, Västra skogen]
-
A.
Finnskogen
Finnskogen is a forested region along the Norwegian-Swedish border known for its dense woodlands and historic Finnish immigrant culture.
-
B.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
C.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
-
D.
Kvamskogen
Kvamskogen is a popular mountainous recreational area in western Norway known for its ski resorts, cabins, and outdoor activities.
-
E.
Jämtland region
Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
- 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: Västra skogen Triple: [Solna centrum, adjacentStation, Västra skogen]
Generated description
Västra skogen is a Stockholm metro station in Solna, Sweden, known for its deep underground platforms and distinctive cavern-style design.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Västra skogen Target entity description: Västra skogen is a Stockholm metro station in Solna, Sweden, known for its deep underground platforms and distinctive cavern-style design.
-
A.
Finnskogen
Finnskogen is a forested region along the Norwegian-Swedish border known for its dense woodlands and historic Finnish immigrant culture.
-
B.
Hälsingland forests
Hälsingland forests are a vast, sparsely populated woodland region in central Sweden known for their boreal landscapes, wildlife, and traditional rural settlements.
-
C.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
-
D.
Kvamskogen
Kvamskogen is a popular mountainous recreational area in western Norway known for its ski resorts, cabins, and outdoor activities.
-
E.
Jämtland region
Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3669f908819087162b1b8a4e4320 |
completed | May 9, 2026, 1:28 p.m. |
| NEDg | Description generation | batch_69ff375856448190a61979dfff751f06 |
completed | May 9, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3830f0148190846bd24db1e0d754 |
completed | May 9, 2026, 1:35 p.m. |
Created at: April 10, 2026, 3:54 a.m.