Triple

T17731990
Position Surface form Disambiguated ID Type / Status
Subject Namsos Airport, Høknesøra E442608 entity
Predicate locatedOn P40 FINISHED
Object Høknesøra
Høknesøra is a locality in Namsos, Norway, known for hosting Namsos Airport and serving as part of the town’s suburban area.
E1364383 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: Høknesøra | Statement: [Namsos Airport, Høknesøra, locatedOn, Høknesøra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Høknesøra
Context triple: [Namsos Airport, Høknesøra, locatedOn, Høknesøra]
  • A. Hemnessjøen
    Hemnessjøen is a lake in southeastern Norway that forms part of the Haldenvassdraget watercourse system.
  • B. Hurdalssjøen
    Hurdalssjøen is a large freshwater lake in eastern Norway, known for its scenic surroundings and recreational activities such as swimming, fishing, and boating.
  • C. Frøysjøen
    Frøysjøen is a coastal fjord or sea area in western Norway, situated below the towering cliff of Hornelen.
  • D. Sandnessjøen
    Sandnessjøen is a coastal town in northern Norway known as a regional hub for the Helgeland area, with strong ties to maritime industries and access to the surrounding archipelago and mountains.
  • E. Sjusjøen
    Sjusjøen is a popular Norwegian cross-country skiing destination and mountain village known for its extensive trail network and scenic highland landscapes near Lillehammer.
  • 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: Høknesøra
Triple: [Namsos Airport, Høknesøra, locatedOn, Høknesøra]
Generated description
Høknesøra is a locality in Namsos, Norway, known for hosting Namsos Airport and serving as part of the town’s suburban area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Høknesøra
Target entity description: Høknesøra is a locality in Namsos, Norway, known for hosting Namsos Airport and serving as part of the town’s suburban area.
  • A. Hemnessjøen
    Hemnessjøen is a lake in southeastern Norway that forms part of the Haldenvassdraget watercourse system.
  • B. Hurdalssjøen
    Hurdalssjøen is a large freshwater lake in eastern Norway, known for its scenic surroundings and recreational activities such as swimming, fishing, and boating.
  • C. Frøysjøen
    Frøysjøen is a coastal fjord or sea area in western Norway, situated below the towering cliff of Hornelen.
  • D. Sandnessjøen
    Sandnessjøen is a coastal town in northern Norway known as a regional hub for the Helgeland area, with strong ties to maritime industries and access to the surrounding archipelago and mountains.
  • E. Sjusjøen
    Sjusjøen is a popular Norwegian cross-country skiing destination and mountain village known for its extensive trail network and scenic highland landscapes near Lillehammer.
  • 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_69d8b9ec79688190b86bdcef85a7b3aa completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e478e7773081909dadb90ff5cb0906 completed April 19, 2026, 6:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0700c341c08190962c16d616a6e31a completed May 15, 2026, 11:17 a.m.
NEDg Description generation batch_6a0701325a28819090367843e04f2f3f completed May 15, 2026, 11:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0701d18c5c819091427fc872dd18cb completed May 15, 2026, 11:21 a.m.
Created at: April 10, 2026, 10:08 a.m.