Triple
T10428974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sigdal |
E245858
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Eggedal
Eggedal is a valley and rural area in Viken county, Norway, known for its traditional farming landscape, outdoor recreation, and cultural heritage.
|
E862871
|
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: Eggedal | Statement: [Sigdal, hasSettlement, Eggedal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eggedal Context triple: [Sigdal, hasSettlement, Eggedal]
-
A.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
B.
Nadderud
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
-
C.
Orkdal
Orkdal was a former municipality in Trøndelag county, Norway, known for its central location in the Orkdalen valley and later incorporation into the larger Orkland municipality.
-
D.
Gausdal
Gausdal is a rural municipality in southeastern Norway known for its agricultural landscape, forests, and outdoor recreational opportunities.
-
E.
Eidskog
Eidskog is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and location along the Swedish border.
- 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: Eggedal Triple: [Sigdal, hasSettlement, Eggedal]
Generated description
Eggedal is a valley and rural area in Viken county, Norway, known for its traditional farming landscape, outdoor recreation, and cultural heritage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Eggedal Target entity description: Eggedal is a valley and rural area in Viken county, Norway, known for its traditional farming landscape, outdoor recreation, and cultural heritage.
-
A.
Engerdal
Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
-
B.
Nadderud
Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
-
C.
Orkdal
Orkdal was a former municipality in Trøndelag county, Norway, known for its central location in the Orkdalen valley and later incorporation into the larger Orkland municipality.
-
D.
Gausdal
Gausdal is a rural municipality in southeastern Norway known for its agricultural landscape, forests, and outdoor recreational opportunities.
-
E.
Eidskog
Eidskog is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and location along the Swedish border.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea4b4b5881908ae23f8efeea482b |
completed | April 7, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87ea554888190bf2ef31e33c0ff14 |
completed | April 10, 2026, 4:37 a.m. |
| NEDg | Description generation | batch_69d8837e70508190b03e8983b2617eac |
completed | April 10, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d889cc40648190a1d80b955e676ea5 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 6, 2026, 12:13 p.m.