Triple

T2207089
Position Surface form Disambiguated ID Type / Status
Subject Marker E50824 entity
Predicate borderWith P224 FINISHED
Object Rakkestad E277705 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: Rakkestad | Statement: [Marker, borderWith, Rakkestad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rakkestad
Context triple: [Marker, borderWith, Rakkestad]
  • A. Rakkestad chosen
    Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
  • B. Ringerike
    Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
  • C. Larvik
    Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
  • D. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • E. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial 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_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_69af834e87b48190b3299c70a0679b47 completed March 10, 2026, 2:34 a.m.
Created at: March 4, 2026, 7:46 p.m.