Triple

T10429592
Position Surface form Disambiguated ID Type / Status
Subject Sarpsfossen E245873 entity
Predicate near P350 FINISHED
Object Sarpsborg city centre E50822 NE FINISHED

How this triple was built (2 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: Sarpsborg city centre | Statement: [Sarpsfossen, near, Sarpsborg city centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarpsborg city centre
Context triple: [Sarpsfossen, near, Sarpsborg city centre]
  • A. Sarpsborg chosen
    Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
  • B. Fredrikstad/Sarpsborg urban area
    The Fredrikstad/Sarpsborg urban area is a major contiguous metropolitan region in southeastern Norway that encompasses the twin cities of Fredrikstad and Sarpsborg.
  • C. Torshov
    Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
  • D. Smestad
    Smestad is a residential neighborhood in Oslo, Norway, known for its affluent housing and proximity to green areas and good public transport.
  • E. Sentrum, Oslo
    Sentrum is the central borough of Oslo, Norway, encompassing the city’s main downtown area, key commercial districts, and major transport hubs.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea4b4b5881908ae23f8efeea482b completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d89f8a5b00819080c303bb0fc82f5a completed April 10, 2026, 6:58 a.m.
Created at: April 6, 2026, 12:13 p.m.