Triple

T11615788
Position Surface form Disambiguated ID Type / Status
Subject Haldenvassdraget E275502 entity
Predicate hasPart P35 FINISHED
Object Øgderen
Øgderen is a lake in southeastern Norway that forms part of the Haldenvassdraget watercourse system.
 E935619 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: Øgderen | Statement: [Haldenvassdraget, hasPart, Øgderen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Øgderen
Context triple: [Haldenvassdraget, hasPart, Øgderen]
  • A. Vinge
    Vinge is a surname most notably associated with Vernor Vinge, the American science fiction author and mathematician known for popularizing the concept of the technological singularity.
  • B. Essing
    Essing is a small Bavarian municipality known for its picturesque setting along the Altmühl River and historic architecture, including a notable wooden bridge.
  • C. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • D. Bragernes
    Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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: Øgderen
Triple: [Haldenvassdraget, hasPart, Øgderen]
Generated description
Øgderen is a lake in southeastern Norway that forms part of the Haldenvassdraget watercourse system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Øgderen
Target entity description: Øgderen is a lake in southeastern Norway that forms part of the Haldenvassdraget watercourse system.
  • A. Vinge
    Vinge is a surname most notably associated with Vernor Vinge, the American science fiction author and mathematician known for popularizing the concept of the technological singularity.
  • B. Essing
    Essing is a small Bavarian municipality known for its picturesque setting along the Altmühl River and historic architecture, including a notable wooden bridge.
  • C. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • D. Bragernes
    Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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_69d6aaf84b548190ac072e4fb89ae18f completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a04675e08190837a3717242fd0f9 completed April 10, 2026, 7:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69e8a84924a0819084c43aeb7c57ac10 completed April 22, 2026, 10:51 a.m.
NEDg Description generation batch_69e8af972e90819096568e7ec2a34059 completed April 22, 2026, 11:23 a.m.
NED2 Entity disambiguation (via description) batch_69e8b0aa21e0819090157b11309a84f6 completed April 22, 2026, 11:27 a.m.
Created at: April 8, 2026, 9:38 p.m.