Triple

T2795698
Position Surface form Disambiguated ID Type / Status
Subject Fredrikstad E53030 entity
Predicate borderedBy P224 FINISHED
Object Sarpsborg 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 | Statement: [Fredrikstad, borderedBy, Sarpsborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarpsborg
Context triple: [Fredrikstad, borderedBy, Sarpsborg]
  • 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. Porsgrunn
    Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
  • C. Lørenskog
    Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
  • D. Risør
    Risør is a small coastal town in southern Norway known for its well-preserved wooden houses, maritime heritage, and annual wooden boat festival.
  • E. Elverum
    Elverum is a town and municipality in Innlandet county in eastern Norway, known for its forestry, military camp, and role in Norwegian World War II history.
  • 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_69ab495a90788190941b6917e1eca3a6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abddef754081908e6218dc2208e0fd completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6646c2c81908157d8f03cb8376d completed March 10, 2026, 7:21 a.m.
Created at: March 6, 2026, 9:58 p.m.