Triple

T2207025
Position Surface form Disambiguated ID Type / Status
Subject Sarpsborg E50822 entity
Predicate hasAttraction P105 FINISHED
Object Tunevannet
Tunevannet is a lake and recreational area near Sarpsborg in Østfold, Norway, known for outdoor activities such as swimming, fishing, and hiking.
 E245874 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: Tunevannet | Statement: [Sarpsborg, hasAttraction, Tunevannet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tunevannet
Context triple: [Sarpsborg, hasAttraction, Tunevannet]
  • A. Tullistes
    Tullistes are the inhabitants of the French city of Tulle, located in the Corrèze department in central France.
  • B. Lågen
    Lågen is a major river in southeastern Norway that flows through the Gudbrandsdalen valley before joining the Mjøsa lake.
  • C. Mo i Rana
    Mo i Rana is an industrial town in Nordland county, Norway, known for its steel industry, proximity to the Arctic Circle, and role as a regional hub in Northern Norway.
  • D. Vitasta
    Vitasta is the ancient Sanskrit name for the Jhelum River, a historically significant river of the Kashmir region frequently mentioned in Vedic and classical Indian texts.
  • E. Vallentuna
    Vallentuna is a locality in Stockholm County, Sweden, known as a suburban community within the Stockholm metropolitan area.
  • 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: Tunevannet
Triple: [Sarpsborg, hasAttraction, Tunevannet]
Generated description
Tunevannet is a lake and recreational area near Sarpsborg in Østfold, Norway, known for outdoor activities such as swimming, fishing, and hiking.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tunevannet
Target entity description: Tunevannet is a lake and recreational area near Sarpsborg in Østfold, Norway, known for outdoor activities such as swimming, fishing, and hiking.
  • A. Tullistes
    Tullistes are the inhabitants of the French city of Tulle, located in the Corrèze department in central France.
  • B. Lågen
    Lågen is a major river in southeastern Norway that flows through the Gudbrandsdalen valley before joining the Mjøsa lake.
  • C. Mo i Rana
    Mo i Rana is an industrial town in Nordland county, Norway, known for its steel industry, proximity to the Arctic Circle, and role as a regional hub in Northern Norway.
  • D. Vitasta
    Vitasta is the ancient Sanskrit name for the Jhelum River, a historically significant river of the Kashmir region frequently mentioned in Vedic and classical Indian texts.
  • E. Vallentuna
    Vallentuna is a locality in Stockholm County, Sweden, known as a suburban community within the Stockholm metropolitan area.
  • 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_69a88b06709c8190978fb2418470d1b6 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abbfcbb83081908d5b2f1603c7b4d2 completed March 7, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae654b89b081908f8c8b9bfc0b6579 completed March 9, 2026, 6:14 a.m.
NEDg Description generation batch_69ae667dede88190b3d1f8bb8866e19e completed March 9, 2026, 6:19 a.m.
NED2 Entity disambiguation (via description) batch_69ae66f12c648190a146de7b2bfdb541 completed March 9, 2026, 6:21 a.m.
Created at: March 4, 2026, 7:46 p.m.