Triple
T3441723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalarna |
E72579
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object |
Härjedalen
Härjedalen is a sparsely populated historical province in central Sweden known for its mountainous landscapes, wilderness areas, and outdoor recreation.
|
E356507
|
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: Härjedalen | Statement: [Dalarna, borders, Härjedalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Härjedalen Context triple: [Dalarna, borders, Härjedalen]
-
A.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
-
B.
Jämtland region
Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
-
C.
Närke
Närke is a historical province in central Sweden known for its Central Swedish dialects and its location around the city of Örebro.
-
D.
Dalarna
Dalarna is a historical province in central Sweden known for its distinct cultural traditions, including unique dialects, folk costumes, and the iconic Dala horse.
-
E.
Hälsingland
Hälsingland is a historical province in central Sweden known for its traditional decorated farmhouses, forests, and cultural heritage.
- 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: Härjedalen Triple: [Dalarna, borders, Härjedalen]
Generated description
Härjedalen is a sparsely populated historical province in central Sweden known for its mountainous landscapes, wilderness areas, and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Härjedalen Target entity description: Härjedalen is a sparsely populated historical province in central Sweden known for its mountainous landscapes, wilderness areas, and outdoor recreation.
-
A.
Dalsland
Dalsland is a historical province in western Sweden known for its forests, lakes, and rural landscapes.
-
B.
Jämtland region
Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
-
C.
Närke
Närke is a historical province in central Sweden known for its Central Swedish dialects and its location around the city of Örebro.
-
D.
Dalarna
Dalarna is a historical province in central Sweden known for its distinct cultural traditions, including unique dialects, folk costumes, and the iconic Dala horse.
-
E.
Hälsingland
Hälsingland is a historical province in central Sweden known for its traditional decorated farmhouses, forests, and cultural heritage.
- 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_69ad85af50288190a854b76653deee6f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adba276b708190949f294a8d09ec7b |
completed | March 8, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3548598088190907e13c88cb975fc |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b355a8da148190896dacf746630445 |
completed | March 13, 2026, 12:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3561132888190b0439cd3d8e7bf96 |
completed | March 13, 2026, 12:10 a.m. |
Created at: March 8, 2026, 3:16 p.m.