Triple

T19220361
Position Surface form Disambiguated ID Type / Status
Subject Kongsvinger municipality E480595 entity
Predicate hasSettlement P1068 FINISHED
Object Lundersæter
Lundersæter is a small village in Innlandet county, Norway, situated in a rural forested area and administratively belonging to Kongsvinger municipality.
E1381898 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: Lundersæter | Statement: [Kongsvinger municipality, hasSettlement, Lundersæter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lundersæter
Context triple: [Kongsvinger municipality, hasSettlement, Lundersæter]
  • A. Fagerbakke
    Fagerbakke is the surname of American actor and voice actor Bill Fagerbakke, best known for voicing Patrick Star on the animated series "SpongeBob SquarePants."
  • B. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • C. Østervrå
    Østervrå is a small town in the Vendsyssel region of northern Denmark.
  • D. Kværndrup
    Kværndrup is a small Danish village on the island of Funen, known for its proximity to the historic Egeskov Castle.
  • E. Nadderud
    Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
  • 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: Lundersæter
Triple: [Kongsvinger municipality, hasSettlement, Lundersæter]
Generated description
Lundersæter is a small village in Innlandet county, Norway, situated in a rural forested area and administratively belonging to Kongsvinger municipality.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lundersæter
Target entity description: Lundersæter is a small village in Innlandet county, Norway, situated in a rural forested area and administratively belonging to Kongsvinger municipality.
  • A. Fagerbakke
    Fagerbakke is the surname of American actor and voice actor Bill Fagerbakke, best known for voicing Patrick Star on the animated series "SpongeBob SquarePants."
  • B. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • C. Østervrå
    Østervrå is a small town in the Vendsyssel region of northern Denmark.
  • D. Kværndrup
    Kværndrup is a small Danish village on the island of Funen, known for its proximity to the historic Egeskov Castle.
  • E. Nadderud
    Nadderud is a residential and sports-focused area in Bærum, Norway, known for its stadium and athletic facilities.
  • 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_69d8e8cb8c348190b52075823911c869 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fa3e8d488190a93fb743dabd0ffb completed April 20, 2026, 10:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a074e6357788190a26cd9fdda205db5 completed May 15, 2026, 4:48 p.m.
NEDg Description generation batch_6a074fb48c348190ab6cb5571617b942 completed May 15, 2026, 4:54 p.m.
NED2 Entity disambiguation (via description) batch_6a075092eaa08190bc8ec203bb8460a1 completed May 15, 2026, 4:57 p.m.
Created at: April 10, 2026, 1:24 p.m.