Triple
T13147430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senja Municipality |
E312375
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object |
Hålogaland
Hålogaland is a historical region in northern Norway traditionally encompassing parts of what are now Troms and Nordland counties.
|
E1049634
|
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: Hålogaland | Statement: [Senja Municipality, region, Hålogaland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hålogaland Context triple: [Senja Municipality, region, Hålogaland]
-
A.
Nordland
Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
-
B.
Rogaland
Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
-
C.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
D.
Troms og Finnmark
Troms og Finnmark is Norway’s northernmost and largest county, known for its Arctic landscapes, Sami culture, and phenomena like the midnight sun and northern lights.
-
E.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
- 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: Hålogaland Triple: [Senja Municipality, region, Hålogaland]
Generated description
Hålogaland is a historical region in northern Norway traditionally encompassing parts of what are now Troms and Nordland counties.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hålogaland Target entity description: Hålogaland is a historical region in northern Norway traditionally encompassing parts of what are now Troms and Nordland counties.
-
A.
Nordland
Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
-
B.
Rogaland
Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
-
C.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
D.
Troms og Finnmark
Troms og Finnmark is Norway’s northernmost and largest county, known for its Arctic landscapes, Sami culture, and phenomena like the midnight sun and northern lights.
-
E.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bd0f5b08190ab700c5de1c8e138 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76b98a7c48190a2ca0ec7b9bd8d19 |
completed | May 3, 2026, 3:36 p.m. |
| NEDg | Description generation | batch_69f7763fc23c819098d46ab0906b8764 |
completed | May 3, 2026, 4:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f779178dc48190bb0de790de30d8b0 |
completed | May 3, 2026, 4:34 p.m. |
Created at: April 9, 2026, 9:10 p.m.