Triple
T13398192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moskenesøya |
E319755
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Moskenes
Moskenes is a coastal municipality in Nordland county, Norway, known for its dramatic Lofoten archipelago landscapes, fishing villages, and tourism.
|
E1039697
|
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: Moskenes | Statement: [Moskenesøya, hasMunicipality, Moskenes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moskenes Context triple: [Moskenesøya, hasMunicipality, Moskenes]
-
A.
Ulriksdal
Ulriksdal is a district in Solna, Sweden, known for the historic Ulriksdal Palace and its surrounding parklands along the Edsviken inlet.
-
B.
Hvalsey
Hvalsey is the best-preserved Norse ruin site in Greenland, known for its stone church and remnants of a medieval farming settlement.
-
C.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
D.
Nesodden
Nesodden is a municipality and peninsula in southeastern Norway, situated across the Oslofjord from the capital city of Oslo.
-
E.
Kvænangen
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
- 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: Moskenes Triple: [Moskenesøya, hasMunicipality, Moskenes]
Generated description
Moskenes is a coastal municipality in Nordland county, Norway, known for its dramatic Lofoten archipelago landscapes, fishing villages, and tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moskenes Target entity description: Moskenes is a coastal municipality in Nordland county, Norway, known for its dramatic Lofoten archipelago landscapes, fishing villages, and tourism.
-
A.
Ulriksdal
Ulriksdal is a district in Solna, Sweden, known for the historic Ulriksdal Palace and its surrounding parklands along the Edsviken inlet.
-
B.
Hvalsey
Hvalsey is the best-preserved Norse ruin site in Greenland, known for its stone church and remnants of a medieval farming settlement.
-
C.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
D.
Nesodden
Nesodden is a municipality and peninsula in southeastern Norway, situated across the Oslofjord from the capital city of Oslo.
-
E.
Kvænangen
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d9e7348190844e11dd6cbd13b0 |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73071c0b88190bca3b15ea11c7491 |
completed | May 3, 2026, 11:24 a.m. |
| NEDg | Description generation | batch_69f731c912708190af0249952e8824fb |
completed | May 3, 2026, 11:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f732c14f5c8190afd989200d250783 |
completed | May 3, 2026, 11:34 a.m. |
Created at: April 9, 2026, 9:34 p.m.