Triple
T17732225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osen |
E442614
|
entity |
| Predicate | administrativeCentre |
P1474
|
FINISHED |
| Object |
Steinsdalen
Steinsdalen is a small Norwegian village that serves as the local hub of administration and services for the surrounding Osen municipality in Trøndelag county.
|
E1357281
|
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: Steinsdalen | Statement: [Osen, administrativeCentre, Steinsdalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steinsdalen Context triple: [Osen, administrativeCentre, Steinsdalen]
-
A.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
B.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
C.
Ottosdal
Ottosdal is a small agricultural town in South Africa’s North West province, known for its grain farming and rural character.
-
D.
Vangsnes
Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
-
E.
Nydalen
Nydalen is a modern riverside neighborhood in Oslo, Norway, known for its business district, educational institutions, and redeveloped industrial areas.
- 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: Steinsdalen Triple: [Osen, administrativeCentre, Steinsdalen]
Generated description
Steinsdalen is a small Norwegian village that serves as the local hub of administration and services for the surrounding Osen municipality in Trøndelag county.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steinsdalen Target entity description: Steinsdalen is a small Norwegian village that serves as the local hub of administration and services for the surrounding Osen municipality in Trøndelag county.
-
A.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
B.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
C.
Ottosdal
Ottosdal is a small agricultural town in South Africa’s North West province, known for its grain farming and rural character.
-
D.
Vangsnes
Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
-
E.
Nydalen
Nydalen is a modern riverside neighborhood in Oslo, Norway, known for its business district, educational institutions, and redeveloped industrial areas.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e478e7773081909dadb90ff5cb0906 |
completed | April 19, 2026, 6:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05d32f1ee88190b0a215dc78024d62 |
completed | May 14, 2026, 1:50 p.m. |
| NEDg | Description generation | batch_6a05d56219508190baead365509166a3 |
completed | May 14, 2026, 2 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05d6537b9c8190918a56b74c675f7e |
completed | May 14, 2026, 2:04 p.m. |
Created at: April 10, 2026, 10:08 a.m.