Triple
T8871493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ljusdal Municipality |
E211166
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object |
Ljusdal
Ljusdal is a locality in Gävleborg County, Sweden, known as a regional service and transport hub in the northern Hälsingland area.
|
E211166
|
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: Ljusdal | Statement: [Ljusdal Municipality, administrativeCenter, Ljusdal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ljusdal Context triple: [Ljusdal Municipality, administrativeCenter, Ljusdal]
-
A.
Ljusdal Municipality
Ljusdal Municipality is a local government area in central Sweden known for its forests, rivers, and rural communities within Gävleborg County.
-
B.
Nässjö
Nässjö is a small Swedish town in Jönköping County known as a regional railway hub and service center in southern Sweden.
-
C.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
D.
Eidskog
Eidskog is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and location along the Swedish border.
-
E.
Molndal
Mölndal is a Swedish city in Västra Götaland County, just south of Gothenburg, known for its industrial heritage and proximity to major research and technology hubs.
- 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: Ljusdal Triple: [Ljusdal Municipality, administrativeCenter, Ljusdal]
Generated description
Ljusdal is a locality in Gävleborg County, Sweden, known as a regional service and transport hub in the northern Hälsingland area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ljusdal Target entity description: Ljusdal is a locality in Gävleborg County, Sweden, known as a regional service and transport hub in the northern Hälsingland area.
-
A.
Ljusdal Municipality
chosen
Ljusdal Municipality is a local government area in central Sweden known for its forests, rivers, and rural communities within Gävleborg County.
-
B.
Nässjö
Nässjö is a small Swedish town in Jönköping County known as a regional railway hub and service center in southern Sweden.
-
C.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
D.
Eidskog
Eidskog is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and location along the Swedish border.
-
E.
Molndal
Mölndal is a Swedish city in Västra Götaland County, just south of Gothenburg, known for its industrial heritage and proximity to major research and technology hubs.
- 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_69ca838d3c7c8190a849566d5afd2b11 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc61281e888190a32b08980310979f |
completed | April 1, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfab9e87cc8190ae3c8c683aa0921e |
completed | April 3, 2026, 11:59 a.m. |
| NEDg | Description generation | batch_69cfad35141081908033585378bf0a25 |
completed | April 3, 2026, 12:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfadb323988190960dd933f752c456 |
completed | April 3, 2026, 12:08 p.m. |
Created at: March 30, 2026, 6:51 p.m.