Triple

T9517702
Position Surface form Disambiguated ID Type / Status
Subject Greater Oslo Region E229565 entity
Predicate contains P35 FINISHED
Object Nesodden E394792 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: Nesodden | Statement: [Greater Oslo Region, contains, Nesodden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nesodden
Context triple: [Greater Oslo Region, contains, Nesodden]
  • A. Nesodden chosen
    Nesodden is a municipality and peninsula in southeastern Norway, situated across the Oslofjord from the capital city of Oslo.
  • B. 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.
  • C. Sundbyvester
    Sundbyvester is a district of Copenhagen located on Amager Island, known primarily as a residential urban area.
  • D. Notodden
    Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
  • E. Grenaa
    Grenaa is a coastal town in eastern Jutland, Denmark, known for its ferry connections to the island of Anholt and its role as a regional commercial and educational center.
  • 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_69ca84777560819084cddd999badc1aa completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9880417c819097dde277988df36d completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a4aeb008190ae5d54367efe2722 completed April 4, 2026, 4:20 p.m.
Created at: March 30, 2026, 7:59 p.m.