Triple
T9812667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hälsingland |
E238313
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Dalarna |
E72579
|
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: Dalarna | Statement: [Hälsingland, borders, Dalarna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalarna Context triple: [Hälsingland, borders, Dalarna]
-
A.
Dalarna
chosen
Dalarna is a historical province in central Sweden known for its distinct cultural traditions, including unique dialects, folk costumes, and the iconic Dala horse.
-
B.
Bohuslän
Bohuslän is a coastal province in western Sweden known for its rugged granite shoreline, fishing villages, and archipelago along the Skagerrak.
-
C.
Ångermanland
Ångermanland is a historical province in northern Sweden known for its deep river valleys, forested landscapes, and coastal areas along the Gulf of Bothnia.
-
D.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
-
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.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb222ba788190a9085272a3de7852 |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5b264c88190bf16c8c32c360878 |
completed | April 5, 2026, 3:23 a.m. |
Created at: March 30, 2026, 8:30 p.m.