Triple
T2301452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanum |
E51740
|
entity |
| Predicate | hasNotableSettlement |
P14082
|
FINISHED |
| Object |
Fjällbacka
Fjällbacka is a picturesque coastal village in western Sweden, known for its fishing heritage, granite cliffs, and as the setting of Camilla Läckberg’s crime novels.
|
E253915
|
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: Fjällbacka | Statement: [Tanum, hasNotableSettlement, Fjällbacka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fjällbacka Context triple: [Tanum, hasNotableSettlement, Fjällbacka]
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
C.
Gustavsberg
Gustavsberg is a locality in Sweden best known for its historic porcelain factory and role as a suburban community in the Stockholm archipelago.
-
D.
Forsbacka
Forsbacka is a small locality in east-central Sweden known historically for its ironworks and its location within Gävleborg County.
-
E.
Bollnäs
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
- 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: Fjällbacka Triple: [Tanum, hasNotableSettlement, Fjällbacka]
Generated description
Fjällbacka is a picturesque coastal village in western Sweden, known for its fishing heritage, granite cliffs, and as the setting of Camilla Läckberg’s crime novels.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fjällbacka Target entity description: Fjällbacka is a picturesque coastal village in western Sweden, known for its fishing heritage, granite cliffs, and as the setting of Camilla Läckberg’s crime novels.
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
C.
Gustavsberg
Gustavsberg is a locality in Sweden best known for its historic porcelain factory and role as a suburban community in the Stockholm archipelago.
-
D.
Forsbacka
Forsbacka is a small locality in east-central Sweden known historically for its ironworks and its location within Gävleborg County.
-
E.
Bollnäs
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abd0d6b0e48190aee9131ca182e52f |
completed | March 7, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f31356c81909c563d88d472e05f |
completed | March 9, 2026, 8:05 a.m. |
| NEDg | Description generation | batch_69ae7fd78ee48190990fc7b5034b662b |
completed | March 9, 2026, 8:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae80dadf208190913211329a40b4ee |
completed | March 9, 2026, 8:12 a.m. |
Created at: March 4, 2026, 7:49 p.m.