Triple
T15751703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiehkkevárri |
E381862
|
entity |
| Predicate | locatedInMunicipality |
P40
|
FINISHED |
| Object |
Lyngen
Lyngen is a municipality in Troms og Finnmark county in northern Norway, known for its dramatic fjord landscapes and surrounding alpine mountains.
|
E1275058
|
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: Lyngen | Statement: [Jiehkkevárri, locatedInMunicipality, Lyngen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyngen Context triple: [Jiehkkevárri, locatedInMunicipality, Lyngen]
-
A.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
-
B.
Ørskog
Ørskog is a village and former municipality in western Norway, located in the county of Møre og Romsdal.
-
C.
Fagernes
Fagernes is a small town in central Norway that serves as a regional hub and gateway to the mountainous Valdres district.
-
D.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
E.
Finnsnes
Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
- 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: Lyngen Triple: [Jiehkkevárri, locatedInMunicipality, Lyngen]
Generated description
Lyngen is a municipality in Troms og Finnmark county in northern Norway, known for its dramatic fjord landscapes and surrounding alpine mountains.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lyngen Target entity description: Lyngen is a municipality in Troms og Finnmark county in northern Norway, known for its dramatic fjord landscapes and surrounding alpine mountains.
-
A.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
-
B.
Ørskog
Ørskog is a village and former municipality in western Norway, located in the county of Møre og Romsdal.
-
C.
Fagernes
Fagernes is a small town in central Norway that serves as a regional hub and gateway to the mountainous Valdres district.
-
D.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
-
E.
Finnsnes
Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01d27210b4819099aed53a2820591a |
completed | May 11, 2026, 12:58 p.m. |
| NEDg | Description generation | batch_6a01d30961788190881f78d1989bd4ae |
completed | May 11, 2026, 1 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01d65ce2a48190a78e58b643de699a |
completed | May 11, 2026, 1:15 p.m. |
Created at: April 10, 2026, 4:47 a.m.