Triple
T22219729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Västervik Municipality |
E549176
|
entity |
| Predicate | includesLocality |
P45140
|
FINISHED |
| Object |
Verkebäck
Verkebäck is a small locality in southeastern Sweden situated within Västervik Municipality in Kalmar County.
|
E1527141
|
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: Verkebäck | Statement: [Västervik Municipality, includesLocality, Verkebäck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verkebäck Context triple: [Västervik Municipality, includesLocality, Verkebäck]
-
A.
Såtenäs
Såtenäs is a locality in western Sweden best known as a major Swedish Air Force base and home of the F 7 Wing.
-
B.
Kungsbacka
Kungsbacka is a town in southwestern Sweden known for its coastal location, historic wooden center, and role as a commuter hub for nearby Gothenburg.
-
C.
Sysslebäck
Sysslebäck is a small village in Värmland County, Sweden, known for its scenic location along the Klarälven river and outdoor recreational opportunities.
-
D.
Fagersjö
Fagersjö is a residential district in southern Stockholm, Sweden, known for its proximity to lakes and green areas.
-
E.
Blackeberg
Blackeberg is a suburban district in western Stockholm, Sweden, best known internationally as the bleak, wintry backdrop of the Swedish vampire novel and film "Let the Right One In."
- 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: Verkebäck Triple: [Västervik Municipality, includesLocality, Verkebäck]
Generated description
Verkebäck is a small locality in southeastern Sweden situated within Västervik Municipality in Kalmar County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Verkebäck Target entity description: Verkebäck is a small locality in southeastern Sweden situated within Västervik Municipality in Kalmar County.
-
A.
Såtenäs
Såtenäs is a locality in western Sweden best known as a major Swedish Air Force base and home of the F 7 Wing.
-
B.
Kungsbacka
Kungsbacka is a town in southwestern Sweden known for its coastal location, historic wooden center, and role as a commuter hub for nearby Gothenburg.
-
C.
Sysslebäck
Sysslebäck is a small village in Värmland County, Sweden, known for its scenic location along the Klarälven river and outdoor recreational opportunities.
-
D.
Fagersjö
Fagersjö is a residential district in southern Stockholm, Sweden, known for its proximity to lakes and green areas.
-
E.
Blackeberg
Blackeberg is a suburban district in western Stockholm, Sweden, best known internationally as the bleak, wintry backdrop of the Swedish vampire novel and film "Let the Right One In."
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b8fa3d081908db0a0556b009d8f |
completed | April 28, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ab65050e8819098e827decba3b93c |
completed | May 18, 2026, 6:48 a.m. |
| NEDg | Description generation | batch_6a0ab71a626c8190bb4cf0afe7bd5255 |
completed | May 18, 2026, 6:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ab7ec494c819099e2f4ab7954d542 |
completed | May 18, 2026, 6:55 a.m. |
Created at: April 16, 2026, 8:37 p.m.