Triple
T1047203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oslo Airport, Gardermoen |
E22608
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Gardermoen
Gardermoen is an area in Ullensaker, Norway, best known as the site of Oslo’s main international airport.
|
E22608
|
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: Gardermoen | Statement: [Oslo Airport, Gardermoen, locatedIn, Gardermoen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gardermoen Context triple: [Oslo Airport, Gardermoen, locatedIn, Gardermoen]
-
A.
Oslo
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
B.
Oslo Airport, Gardermoen
Oslo Airport, Gardermoen is Norway’s main international airport and the primary aviation hub serving the Oslo region.
-
C.
Bergen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
-
D.
Stavanger
Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
-
E.
Porsgrunn
Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
- 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: Gardermoen Triple: [Oslo Airport, Gardermoen, locatedIn, Gardermoen]
Generated description
Gardermoen is an area in Ullensaker, Norway, best known as the site of Oslo’s main international airport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gardermoen Target entity description: Gardermoen is an area in Ullensaker, Norway, best known as the site of Oslo’s main international airport.
-
A.
Oslo
Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
-
B.
Oslo Airport, Gardermoen
chosen
Oslo Airport, Gardermoen is Norway’s main international airport and the primary aviation hub serving the Oslo region.
-
C.
Bergen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
-
D.
Stavanger
Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
-
E.
Porsgrunn
Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
- F. None of above.
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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b84d30888190b66f7245d781957d |
completed | March 1, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bcb65d08190b2ea04b6de3bb39b |
completed | March 7, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69ac3ce6228881908f429cb0a016a17a |
completed | March 7, 2026, 2:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac3d3ed140819087ede15c555e2f4d |
completed | March 7, 2026, 2:59 p.m. |
Created at: March 1, 2026, 7:42 p.m.