Triple
T13521499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kvæfjord |
E322907
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Vikeland
Vikeland is a small settlement located within the municipality of Kvæfjord in Troms og Finnmark county, Norway.
|
E1044744
|
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: Vikeland | Statement: [Kvæfjord, hasSettlement, Vikeland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vikeland Context triple: [Kvæfjord, hasSettlement, Vikeland]
-
A.
Reiderland
Reiderland was a former municipality in the province of Groningen in the northeastern Netherlands, later incorporated into the municipality of Oldambt.
-
B.
Velzyland
Velzyland is a well-known surf break and beach area on the North Shore of Oʻahu in Hawaii, popular for its consistent waves and local surf culture.
-
C.
Modelland
Modelland is a young adult fantasy novel by supermodel Tyra Banks that satirically explores the world of modeling through a magical, dystopian academy.
-
D.
Palmland
Palmland was a named passenger train operated by the Seaboard Air Line Railroad that provided long-distance service in the southeastern United States.
-
E.
Nutopia
Nutopia is a production company known for creating visually striking, narrative-driven documentary series and films that blend science, history, and storytelling.
- 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: Vikeland Triple: [Kvæfjord, hasSettlement, Vikeland]
Generated description
Vikeland is a small settlement located within the municipality of Kvæfjord in Troms og Finnmark county, Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vikeland Target entity description: Vikeland is a small settlement located within the municipality of Kvæfjord in Troms og Finnmark county, Norway.
-
A.
Reiderland
Reiderland was a former municipality in the province of Groningen in the northeastern Netherlands, later incorporated into the municipality of Oldambt.
-
B.
Velzyland
Velzyland is a well-known surf break and beach area on the North Shore of Oʻahu in Hawaii, popular for its consistent waves and local surf culture.
-
C.
Modelland
Modelland is a young adult fantasy novel by supermodel Tyra Banks that satirically explores the world of modeling through a magical, dystopian academy.
-
D.
Palmland
Palmland was a named passenger train operated by the Seaboard Air Line Railroad that provided long-distance service in the southeastern United States.
-
E.
Nutopia
Nutopia is a production company known for creating visually striking, narrative-driven documentary series and films that blend science, history, and storytelling.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafa3df0c8190804174695587f0ea |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7549a215c8190a0b18c505d8ca504 |
completed | May 3, 2026, 1:58 p.m. |
| NEDg | Description generation | batch_69f755e7ad2c81908cdd2405c3f28d8b |
completed | May 3, 2026, 2:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f756a24c248190867f2a3aa33bbac8 |
completed | May 3, 2026, 2:07 p.m. |
Created at: April 9, 2026, 9:44 p.m.