Triple
T10536720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Västmanland |
E248581
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Skinnskatteberg
Skinnskatteberg is a small Swedish locality and municipal seat in central Sweden, known for its forested landscape and historical mining industry.
|
E868735
|
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: Skinnskatteberg | Statement: [Västmanland, hasTown, Skinnskatteberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skinnskatteberg Context triple: [Västmanland, hasTown, Skinnskatteberg]
-
A.
Kjerkeberget
Kjerkeberget is a forested hill in Norway that marks the highest natural point within Oslo’s municipal boundaries.
-
B.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
C.
Ytterhogdal
Ytterhogdal is a small locality in Härjedalen, central Sweden, known for its rural setting and traditional Swedish countryside character.
-
D.
Mariaberget
Mariaberget is a historic, picturesque area on the western side of Södermalm in central Stockholm, known for its well-preserved old buildings and panoramic views over the city and Lake Mälaren.
-
E.
Spiterstulen
Spiterstulen is a mountain lodge in Norway’s Jotunheimen region that serves as a key base for hikers and climbers exploring nearby peaks.
- 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: Skinnskatteberg Triple: [Västmanland, hasTown, Skinnskatteberg]
Generated description
Skinnskatteberg is a small Swedish locality and municipal seat in central Sweden, known for its forested landscape and historical mining industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Skinnskatteberg Target entity description: Skinnskatteberg is a small Swedish locality and municipal seat in central Sweden, known for its forested landscape and historical mining industry.
-
A.
Kjerkeberget
Kjerkeberget is a forested hill in Norway that marks the highest natural point within Oslo’s municipal boundaries.
-
B.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
C.
Ytterhogdal
Ytterhogdal is a small locality in Härjedalen, central Sweden, known for its rural setting and traditional Swedish countryside character.
-
D.
Mariaberget
Mariaberget is a historic, picturesque area on the western side of Södermalm in central Stockholm, known for its well-preserved old buildings and panoramic views over the city and Lake Mälaren.
-
E.
Spiterstulen
Spiterstulen is a mountain lodge in Norway’s Jotunheimen region that serves as a key base for hikers and climbers exploring nearby peaks.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a554fb4819081e9618bab051dc6 |
completed | April 7, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90e507b408190ae3538d02536ef4c |
completed | April 10, 2026, 2:50 p.m. |
| NEDg | Description generation | batch_69d91233a5d081908a8c9f6a3177f40e |
completed | April 10, 2026, 3:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d912d18d2081909a6ebce1af1fb96a |
completed | April 10, 2026, 3:10 p.m. |
Created at: April 6, 2026, 12:31 p.m.