Triple
T15276372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sogn og Fjordane |
E365150
|
entity |
| Predicate | containsPart |
P35
|
FINISHED |
| Object |
Gulen
Gulen is a coastal municipality in western Norway known for its fjords, islands, and historic role as a regional meeting place in the Viking Age.
|
E1148833
|
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: Gulen | Statement: [Sogn og Fjordane, containsPart, Gulen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gulen Context triple: [Sogn og Fjordane, containsPart, Gulen]
-
A.
Edvarda
Edvarda is a central fictional character in Knut Hamsun’s novel "Pan," known for her complex and tumultuous relationship with the protagonist.
-
B.
Slagsvold
Slagsvold is a Norwegian surname borne by various notable individuals, including figures in academia, politics, and public life.
-
C.
Svea
Svea is a patriotic poem by Swedish poet Esaias Tegnér that celebrates Sweden and helped establish his reputation in early 19th-century Swedish literature.
-
D.
Henrike
Henrike is a feminine given name of German origin, serving as the female form of Heinrich.
-
E.
Ludvika
Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
- 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: Gulen Triple: [Sogn og Fjordane, containsPart, Gulen]
Generated description
Gulen is a coastal municipality in western Norway known for its fjords, islands, and historic role as a regional meeting place in the Viking Age.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gulen Target entity description: Gulen is a coastal municipality in western Norway known for its fjords, islands, and historic role as a regional meeting place in the Viking Age.
-
A.
Edvarda
Edvarda is a central fictional character in Knut Hamsun’s novel "Pan," known for her complex and tumultuous relationship with the protagonist.
-
B.
Slagsvold
Slagsvold is a Norwegian surname borne by various notable individuals, including figures in academia, politics, and public life.
-
C.
Svea
Svea is a patriotic poem by Swedish poet Esaias Tegnér that celebrates Sweden and helped establish his reputation in early 19th-century Swedish literature.
-
D.
Henrike
Henrike is a feminine given name of German origin, serving as the female form of Heinrich.
-
E.
Ludvika
Ludvika is a small industrial town in central Sweden known for its engineering and manufacturing industries, particularly in the power and electrical sectors.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00953bc848190b83919f39d5ee37b |
completed | April 15, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef7186d481909067f8088f3ea497 |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef3374a34819094e0a4ac7bf89059 |
completed | May 9, 2026, 8:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef41c898881908ed3520643918445 |
completed | May 9, 2026, 8:45 a.m. |
Created at: April 10, 2026, 3:14 a.m.