Triple
T5634710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Älvkarleby Municipality |
E147918
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Skutskär |
E538542
|
NE FINISHED |
How this triple was built (2 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: Skutskär | Statement: [Älvkarleby Municipality, hasSettlement, Skutskär]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skutskär Context triple: [Älvkarleby Municipality, hasSettlement, Skutskär]
-
A.
Skutskär
chosen
Skutskär is a locality in Uppsala County, Sweden, known historically for its pulp and paper industry.
-
B.
Skärholmen
Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
-
C.
Västerljung
Västerljung is a small locality in eastern Sweden situated within Trosa Municipality in Södermanland County.
-
D.
Skarpö
Skarpö is an island in the Stockholm archipelago of Sweden, situated within Vaxholm Municipality and known for its coastal scenery and residential character.
-
E.
Rindö
Rindö is an island in Sweden’s Stockholm archipelago, known for its coastal scenery and strategic location near the town of Vaxholm.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c00907bc8881909ed760d3ed73ef35 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0226118548190877793dadf6cacba |
completed | March 22, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a1666d88190af4c1890247f897d |
completed | March 22, 2026, 9:07 p.m. |
Created at: March 22, 2026, 3:41 p.m.