Triple
T23126936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Time |
E577057
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Kvernaland
Kvernaland is a village in Rogaland county, Norway, known for its agricultural machinery industry and proximity to the city of Stavanger.
|
E1571060
|
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: Kvernaland | Statement: [Time, hasSettlement, Kvernaland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kvernaland Context triple: [Time, hasSettlement, Kvernaland]
-
A.
Skaland
Skaland is a small coastal village on the island of Senja in northern Norway, known for its scenic fjord landscape and proximity to rugged mountains.
-
B.
Tivland
Tivland is the traditional homeland and cultural region of the Tiv people, primarily located in central Nigeria.
-
C.
Austurland
Austurland is a sparsely populated region in eastern Iceland known for its dramatic fjords, coastal villages, and rugged natural landscapes.
-
D.
Skånland
Skånland was a former municipality in Troms county, Norway, known for its coastal landscapes and small communities before being merged into Tjeldsund.
-
E.
Bördeland
Bördeland is a municipality in the German state of Saxony-Anhalt, known for its rural character and location within the fertile Magdeburg Börde region.
- 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: Kvernaland Triple: [Time, hasSettlement, Kvernaland]
Generated description
Kvernaland is a village in Rogaland county, Norway, known for its agricultural machinery industry and proximity to the city of Stavanger.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kvernaland Target entity description: Kvernaland is a village in Rogaland county, Norway, known for its agricultural machinery industry and proximity to the city of Stavanger.
-
A.
Skaland
Skaland is a small coastal village on the island of Senja in northern Norway, known for its scenic fjord landscape and proximity to rugged mountains.
-
B.
Tivland
Tivland is the traditional homeland and cultural region of the Tiv people, primarily located in central Nigeria.
-
C.
Austurland
Austurland is a sparsely populated region in eastern Iceland known for its dramatic fjords, coastal villages, and rugged natural landscapes.
-
D.
Skånland
Skånland was a former municipality in Troms county, Norway, known for its coastal landscapes and small communities before being merged into Tjeldsund.
-
E.
Bördeland
Bördeland is a municipality in the German state of Saxony-Anhalt, known for its rural character and location within the fertile Magdeburg Börde region.
- 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_69e245f7b0e481909c473ff4e6a54e2c |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e5482588190b95b36075ecc7f24 |
completed | April 29, 2026, 4:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23f444d08190aa7fd967fc9c1d0a |
completed | May 19, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_6a0c271f3c24819093f04fc69ae90160 |
completed | May 19, 2026, 9:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c27e86440819089fae3350b29d722 |
completed | May 19, 2026, 9:05 a.m. |
Created at: April 17, 2026, 3:59 p.m.