Triple
T16027258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vågsøy |
E388746
|
entity |
| Predicate | hasMountain |
P10602
|
FINISHED |
| Object |
Veten
Veten is a notable mountain located on the island of Vågsøy in Vestland county, Norway, known for its scenic views over the surrounding coastal landscape.
|
E1189218
|
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: Veten | Statement: [Vågsøy, hasMountain, Veten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Veten Context triple: [Vågsøy, hasMountain, Veten]
-
A.
Veitvet
Veitvet is a residential neighborhood in Oslo, Norway, known for its apartment blocks, local shopping center, and multicultural community.
-
B.
Vangteh
Vangteh is a dialect of the Tedim Chin language spoken by a subgroup of the Chin people in Myanmar.
-
C.
Väsman
Väsman is a lake in central Sweden known for its scenic surroundings near the town of Ludvika in Dalarna County.
-
D.
Vitlycke
Vitlycke is a renowned Bronze Age rock carving site in Tanum, Sweden, noted for its extensive petroglyphs and archaeological significance.
-
E.
Vekoma
Vekoma is a Dutch roller coaster and amusement ride manufacturer known worldwide for designing and building a wide range of thrill and family attractions for theme parks.
- 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: Veten Triple: [Vågsøy, hasMountain, Veten]
Generated description
Veten is a notable mountain located on the island of Vågsøy in Vestland county, Norway, known for its scenic views over the surrounding coastal landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Veten Target entity description: Veten is a notable mountain located on the island of Vågsøy in Vestland county, Norway, known for its scenic views over the surrounding coastal landscape.
-
A.
Veitvet
Veitvet is a residential neighborhood in Oslo, Norway, known for its apartment blocks, local shopping center, and multicultural community.
-
B.
Vangteh
Vangteh is a dialect of the Tedim Chin language spoken by a subgroup of the Chin people in Myanmar.
-
C.
Väsman
Väsman is a lake in central Sweden known for its scenic surroundings near the town of Ludvika in Dalarna County.
-
D.
Vitlycke
Vitlycke is a renowned Bronze Age rock carving site in Tanum, Sweden, noted for its extensive petroglyphs and archaeological significance.
-
E.
Vekoma
Vekoma is a Dutch roller coaster and amusement ride manufacturer known worldwide for designing and building a wide range of thrill and family attractions for theme parks.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18328707c8190b9a444c78faaaa04 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf33c6a881909284933ea3b7dd6e |
completed | May 10, 2026, 12:20 a.m. |
| NEDg | Description generation | batch_69ffd01d545c8190a96cd888223c7fa9 |
completed | May 10, 2026, 12:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffd0b3d4b08190b1be30954d5d76c0 |
completed | May 10, 2026, 12:26 a.m. |
Created at: April 10, 2026, 4:56 a.m.