Triple

T4843426
Position Surface form Disambiguated ID Type / Status
Subject Moss, Norway E108229 entity
Predicate hasForestArea P11107 FINISHED
Object Mossemarka
Mossemarka is a forested recreational area near the town of Moss in southeastern Norway, popular for outdoor activities such as hiking and skiing.
 E473998 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: Mossemarka | Statement: [Moss, Norway, hasForestArea, Mossemarka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mossemarka
Context triple: [Moss, Norway, hasForestArea, Mossemarka]
  • A. Mosen
    Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
  • B. Maarkedal
    Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
  • C. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • D. Moudon
    Moudon is a historic town and former district capital in the canton of Vaud, Switzerland, known for its medieval old town and location in the Broye valley.
  • E. Hedesunda
    Hedesunda is a small locality in east-central Sweden known for its rural character and proximity to forests, lakes, and the Dalälven River.
  • 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: Mossemarka
Triple: [Moss, Norway, hasForestArea, Mossemarka]
Generated description
Mossemarka is a forested recreational area near the town of Moss in southeastern Norway, popular for outdoor activities such as hiking and skiing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mossemarka
Target entity description: Mossemarka is a forested recreational area near the town of Moss in southeastern Norway, popular for outdoor activities such as hiking and skiing.
  • A. Mosen
    Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
  • B. Maarkedal
    Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
  • C. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • D. Moudon
    Moudon is a historic town and former district capital in the canton of Vaud, Switzerland, known for its medieval old town and location in the Broye valley.
  • E. Hedesunda
    Hedesunda is a small locality in east-central Sweden known for its rural character and proximity to forests, lakes, and the Dalälven River.
  • 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_69bd4409b264819085ab855f3eb5381a completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d0078388190a74a9ee38e1ade4b completed March 20, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5cd29c9c8190ab4ca5463ef99c15 completed March 21, 2026, 8:54 a.m.
NEDg Description generation batch_69be5efdf88481908165609068de9273 completed March 21, 2026, 9:03 a.m.
NED2 Entity disambiguation (via description) batch_69be5f63d5d881909c2f8bf29152903f completed March 21, 2026, 9:05 a.m.
Created at: March 20, 2026, 1:25 p.m.