Triple
T5634709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Älvkarleby Municipality |
E147918
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Älvkarleby
Älvkarleby is a locality in Uppsala County, Sweden, known for its scenic location by the Dalälven river and its popular salmon fishing.
|
E147918
|
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: Älvkarleby | Statement: [Älvkarleby Municipality, hasSettlement, Älvkarleby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Älvkarleby Context triple: [Älvkarleby Municipality, hasSettlement, Älvkarleby]
-
A.
Tärnsjö
Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
-
B.
Älvkarleby Municipality
Älvkarleby Municipality is a local government area in east-central Sweden known for its hydroelectric power production and scenic location along the Dalälven River.
-
C.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
D.
Kårsta
Kårsta is a locality in Vallentuna Municipality, Sweden, known as the northern terminus of Stockholm’s Roslagsbanan narrow-gauge railway line.
-
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: Älvkarleby Triple: [Älvkarleby Municipality, hasSettlement, Älvkarleby]
Generated description
Älvkarleby is a locality in Uppsala County, Sweden, known for its scenic location by the Dalälven river and its popular salmon fishing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Älvkarleby Target entity description: Älvkarleby is a locality in Uppsala County, Sweden, known for its scenic location by the Dalälven river and its popular salmon fishing.
-
A.
Tärnsjö
Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
-
B.
Älvkarleby Municipality
chosen
Älvkarleby Municipality is a local government area in east-central Sweden known for its hydroelectric power production and scenic location along the Dalälven River.
-
C.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
D.
Kårsta
Kårsta is a locality in Vallentuna Municipality, Sweden, known as the northern terminus of Stockholm’s Roslagsbanan narrow-gauge railway line.
-
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.
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_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_69c097d0234881908f6716979a2ade3a |
completed | March 23, 2026, 1:30 a.m. |
| NEDg | Description generation | batch_69c09b9f72cc819089dd9bcde5158426 |
completed | March 23, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c09be6f6d081908daac4e1b1565ae4 |
completed | March 23, 2026, 1:48 a.m. |
Created at: March 22, 2026, 3:41 p.m.