Triple
T3593332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hjørundfjord |
E76077
|
entity |
| Predicate | municipality |
P852
|
FINISHED |
| Object |
Volda
Volda is a municipality in Møre og Romsdal county, Norway, known for its fjord landscape, cultural life, and Volda University College.
|
E376225
|
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: Volda | Statement: [Hjørundfjord, municipality, Volda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Volda Context triple: [Hjørundfjord, municipality, Volda]
-
A.
Numedal
Numedal is a valley in southeastern Norway known for its traditional wooden architecture, medieval stave churches, and scenic river landscape.
-
B.
Tjøme
Tjøme is a scenic island and former municipality in Vestfold, Norway, known for its coastal landscapes, summer cabins, and popular seaside recreation areas.
-
C.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
D.
Hadeland
Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
-
E.
Troms
Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
- 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: Volda Triple: [Hjørundfjord, municipality, Volda]
Generated description
Volda is a municipality in Møre og Romsdal county, Norway, known for its fjord landscape, cultural life, and Volda University College.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Volda Target entity description: Volda is a municipality in Møre og Romsdal county, Norway, known for its fjord landscape, cultural life, and Volda University College.
-
A.
Numedal
Numedal is a valley in southeastern Norway known for its traditional wooden architecture, medieval stave churches, and scenic river landscape.
-
B.
Tjøme
Tjøme is a scenic island and former municipality in Vestfold, Norway, known for its coastal landscapes, summer cabins, and popular seaside recreation areas.
-
C.
Verdal
Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
-
D.
Hadeland
Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
-
E.
Troms
Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc15bbbcc81908d6cf95f8e70c6ca |
completed | March 8, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44efeacf481908dd9a26f348f9977 |
completed | March 13, 2026, 5:53 p.m. |
| NEDg | Description generation | batch_69b453c225b481908cc06090bcd0ed64 |
completed | March 13, 2026, 6:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b45f6aefd081909dfd1740f233dbf3 |
completed | March 13, 2026, 7:03 p.m. |
Created at: March 8, 2026, 3:22 p.m.