Triple
T15302646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vøringsfossen |
E365825
|
entity |
| Predicate | watercourse |
P415
|
FINISHED |
| Object |
Bjoreio
Bjoreio is a river in Norway that feeds the famous Vøringsfossen waterfall, one of the country’s most visited natural attractions.
|
E1147739
|
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: Bjoreio | Statement: [Vøringsfossen, watercourse, Bjoreio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bjoreio Context triple: [Vøringsfossen, watercourse, Bjoreio]
-
A.
Bjorli
Bjorli is a Norwegian village known for its ski resort and scenic mountain surroundings in Innlandet county.
-
B.
Borre
Borre is a small village on the Danish island of Møn, known for its rural charm and proximity to the island’s natural and historical attractions.
-
C.
Bjerke
Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
-
D.
Bjugn
Bjugn is a former municipality and coastal community in Trøndelag county, Norway, known for its fishing, agriculture, and location on the Fosen peninsula.
-
E.
Djøf
Djøf is a Danish professional organization and trade union representing academics and professionals in law, economics, political science, and related social sciences.
- 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: Bjoreio Triple: [Vøringsfossen, watercourse, Bjoreio]
Generated description
Bjoreio is a river in Norway that feeds the famous Vøringsfossen waterfall, one of the country’s most visited natural attractions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bjoreio Target entity description: Bjoreio is a river in Norway that feeds the famous Vøringsfossen waterfall, one of the country’s most visited natural attractions.
-
A.
Bjorli
Bjorli is a Norwegian village known for its ski resort and scenic mountain surroundings in Innlandet county.
-
B.
Borre
Borre is a small village on the Danish island of Møn, known for its rural charm and proximity to the island’s natural and historical attractions.
-
C.
Bjerke
Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
-
D.
Bjugn
Bjugn is a former municipality and coastal community in Trøndelag county, Norway, known for its fishing, agriculture, and location on the Fosen peninsula.
-
E.
Djøf
Djøf is a Danish professional organization and trade union representing academics and professionals in law, economics, political science, and related social sciences.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccd575c8190aa43262d3b73ef3c |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef86b8cc81909969098b1766f6b7 |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69feeff83f3c8190b9ae0f7ce724b3e2 |
completed | May 9, 2026, 8:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef0825e7c8190bb74e2e3de202883 |
completed | May 9, 2026, 8:29 a.m. |
Created at: April 10, 2026, 3:15 a.m.