Triple
T2912285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Froland |
E63709
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Osedalen
Osedalen is a village in Froland municipality in Agder county in southern Norway.
|
E308948
|
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: Osedalen | Statement: [Froland, hasSettlement, Osedalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osedalen Context triple: [Froland, hasSettlement, Osedalen]
-
A.
Rødenes
Rødenes is a small village and former municipality in southeastern Norway, known for its rural landscape and historic church.
-
B.
Elverum
Elverum is a town and municipality in Innlandet county in eastern Norway, known for its forestry, military camp, and role in Norwegian World War II history.
-
C.
Skedsmo
Skedsmo is a former municipality in Viken county, Norway, located northeast of Oslo and known for its suburban communities and historical ties to the Oslo region.
-
D.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
-
E.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
- 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: Osedalen Triple: [Froland, hasSettlement, Osedalen]
Generated description
Osedalen is a village in Froland municipality in Agder county in southern Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Osedalen Target entity description: Osedalen is a village in Froland municipality in Agder county in southern Norway.
-
A.
Rødenes
Rødenes is a small village and former municipality in southeastern Norway, known for its rural landscape and historic church.
-
B.
Elverum
Elverum is a town and municipality in Innlandet county in eastern Norway, known for its forestry, military camp, and role in Norwegian World War II history.
-
C.
Skedsmo
Skedsmo is a former municipality in Viken county, Norway, located northeast of Oslo and known for its suburban communities and historical ties to the Oslo region.
-
D.
Bjug Harstad
Bjug Harstad was a Norwegian-American Lutheran minister and educator best known for establishing Pacific Lutheran University in Washington State.
-
E.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
- 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_69ab4c44ab448190b9411324e8a1fc1d |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe0eb77708190b745b887f3b9a618 |
completed | March 7, 2026, 8:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0562014fc8190b7b702fa40682382 |
completed | March 10, 2026, 5:34 p.m. |
| NEDg | Description generation | batch_69b05f7e78e8819095185f170ca26bda |
completed | March 10, 2026, 6:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0617a21a881909a0f52268a2494a6 |
completed | March 10, 2026, 6:22 p.m. |
Created at: March 6, 2026, 10:11 p.m.