Triple
T21045756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topdalsfjorden |
E518443
|
entity |
| Predicate | hasSettlementOnShore |
P16159
|
FINISHED |
| Object |
Kjevik
Kjevik is a locality in southern Norway best known for hosting Kristiansand Airport, a key regional air transport hub.
|
E1463302
|
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: Kjevik | Statement: [Topdalsfjorden, hasSettlementOnShore, Kjevik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjevik Context triple: [Topdalsfjorden, hasSettlementOnShore, Kjevik]
-
A.
Kilkea
Kilkea is a small village in County Kildare, Ireland, best known for its historic medieval castle and surrounding rural landscape.
-
B.
Ivalo
Ivalo is a village in northern Finnish Lapland known as a key transport hub and gateway to the surrounding Arctic wilderness and tourism areas.
-
C.
Kholmsk
Kholmsk is a port town on the western coast of Sakhalin Island in Russia, serving as an important maritime transport hub in the Sea of Japan.
-
D.
Lieksa
Lieksa is a small town and municipality in eastern Finland known for its forests, lakes, and proximity to Koli National Park.
-
E.
Lapua
Lapua is a small town in western Finland known for its historical significance, including a former state cartridge factory and its role in the Lapua Movement.
- 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: Kjevik Triple: [Topdalsfjorden, hasSettlementOnShore, Kjevik]
Generated description
Kjevik is a locality in southern Norway best known for hosting Kristiansand Airport, a key regional air transport hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kjevik Target entity description: Kjevik is a locality in southern Norway best known for hosting Kristiansand Airport, a key regional air transport hub.
-
A.
Kilkea
Kilkea is a small village in County Kildare, Ireland, best known for its historic medieval castle and surrounding rural landscape.
-
B.
Ivalo
Ivalo is a village in northern Finnish Lapland known as a key transport hub and gateway to the surrounding Arctic wilderness and tourism areas.
-
C.
Kholmsk
Kholmsk is a port town on the western coast of Sakhalin Island in Russia, serving as an important maritime transport hub in the Sea of Japan.
-
D.
Lieksa
Lieksa is a small town and municipality in eastern Finland known for its forests, lakes, and proximity to Koli National Park.
-
E.
Lapua
Lapua is a small town in western Finland known for its historical significance, including a former state cartridge factory and its role in the Lapua Movement.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf3cac081909915a440fbb5c084 |
completed | April 21, 2026, 4:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09475512b081908ceb38a118b0f026 |
completed | May 17, 2026, 4:43 a.m. |
| NEDg | Description generation | batch_6a09489e80288190b401d11ccde39dc1 |
completed | May 17, 2026, 4:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a094995d5bc8190b25d328dbf06b8b1 |
completed | May 17, 2026, 4:52 a.m. |
Created at: April 16, 2026, 2:32 p.m.