Triple
T8341969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tjeldøya |
E195938
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Ramsund
Ramsund is a small coastal village in northern Norway known for its maritime setting and proximity to the Tjeldsundet strait.
|
E728421
|
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: Ramsund | Statement: [Tjeldøya, hasSettlement, Ramsund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ramsund Context triple: [Tjeldøya, hasSettlement, Ramsund]
-
A.
Sørreisa
Sørreisa is a small coastal municipality and village area in northern Norway known for its fjords and rural Arctic landscape.
-
B.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
-
C.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
D.
Snåsa
Snåsa is a rural municipality in Trøndelag county, Norway, known for its large lakes, forests, and strong South Sámi cultural heritage.
-
E.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
- 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: Ramsund Triple: [Tjeldøya, hasSettlement, Ramsund]
Generated description
Ramsund is a small coastal village in northern Norway known for its maritime setting and proximity to the Tjeldsundet strait.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ramsund Target entity description: Ramsund is a small coastal village in northern Norway known for its maritime setting and proximity to the Tjeldsundet strait.
-
A.
Sørreisa
Sørreisa is a small coastal municipality and village area in northern Norway known for its fjords and rural Arctic landscape.
-
B.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
-
C.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
-
D.
Snåsa
Snåsa is a rural municipality in Trøndelag county, Norway, known for its large lakes, forests, and strong South Sámi cultural heritage.
-
E.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fe9efec81908e0c9ded3963bac5 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc72bc43c81909d95c7eb6aefc403 |
completed | April 2, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69cdcb90bec88190a2c19681405aa13e |
completed | April 2, 2026, 1:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdcd0fc9488190a0a576c385b9bc1f |
completed | April 2, 2026, 1:57 a.m. |
Created at: March 30, 2026, 5:58 p.m.