Triple
T16903702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirkenes Airport Høybuktmoen |
E424502
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Høybuktmoen
Høybuktmoen is an area in Sør-Varanger, Norway, known primarily for hosting Kirkenes Airport and military installations.
|
E1239397
|
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: Høybuktmoen | Statement: [Kirkenes Airport Høybuktmoen, locatedNear, Høybuktmoen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Høybuktmoen Context triple: [Kirkenes Airport Høybuktmoen, locatedNear, Høybuktmoen]
-
A.
Høgefjellet
Høgefjellet is a mountain located on the island of Vågsøy in Vestland county, western Norway.
-
B.
Hermannsdalstinden
Hermannsdalstinden is a prominent mountain peak in Norway’s Lofoten archipelago, renowned for its dramatic alpine scenery and challenging hiking routes.
-
C.
Hovdetoppen
Hovdetoppen is a mountain in Gjøvik, Norway, notable for housing the underground Gjøvik Olympic Cavern Hall built for the 1994 Winter Olympics.
-
D.
Kolåstinden
Kolåstinden is a prominent alpine peak in Norway’s Sunnmøre Alps, renowned among hikers and ski mountaineers for its steep slopes and panoramic fjord views.
-
E.
Tallkrogen
Tallkrogen is a residential district in southern Stockholm, Sweden, known for its small-scale housing and garden-city character.
- 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: Høybuktmoen Triple: [Kirkenes Airport Høybuktmoen, locatedNear, Høybuktmoen]
Generated description
Høybuktmoen is an area in Sør-Varanger, Norway, known primarily for hosting Kirkenes Airport and military installations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Høybuktmoen Target entity description: Høybuktmoen is an area in Sør-Varanger, Norway, known primarily for hosting Kirkenes Airport and military installations.
-
A.
Høgefjellet
Høgefjellet is a mountain located on the island of Vågsøy in Vestland county, western Norway.
-
B.
Hermannsdalstinden
Hermannsdalstinden is a prominent mountain peak in Norway’s Lofoten archipelago, renowned for its dramatic alpine scenery and challenging hiking routes.
-
C.
Hovdetoppen
Hovdetoppen is a mountain in Gjøvik, Norway, notable for housing the underground Gjøvik Olympic Cavern Hall built for the 1994 Winter Olympics.
-
D.
Kolåstinden
Kolåstinden is a prominent alpine peak in Norway’s Sunnmøre Alps, renowned among hikers and ski mountaineers for its steep slopes and panoramic fjord views.
-
E.
Tallkrogen
Tallkrogen is a residential district in southern Stockholm, Sweden, known for its small-scale housing and garden-city character.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8de3070819085bfe9696bc887ea |
completed | April 18, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7b47a6081909d8609c2bce96d1a |
completed | May 10, 2026, 6 p.m. |
| NEDg | Description generation | batch_6a00c84074b0819095853775625b320d |
completed | May 10, 2026, 6:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00c8cdcda88190ba4f05a9035668f9 |
completed | May 10, 2026, 6:05 p.m. |
Created at: April 10, 2026, 5:30 a.m.