Triple

T9182669
Position Surface form Disambiguated ID Type / Status
Subject Lolland E220370 entity
Predicate hasSettlement P1068 FINISHED
Object Nakskov E823073 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: Nakskov | Statement: [Lolland, hasSettlement, Nakskov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nakskov
Context triple: [Lolland, hasSettlement, Nakskov]
  • A. Nakskov chosen
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • B. Vadsø
    Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
  • C. Oksbøl
    Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
  • D. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • 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_69ca83e589948190ac9907819db11ddf completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccc2553e548190898434aeda517407 completed April 1, 2026, 6:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1d58be8a4819088c419888a879e3e completed April 5, 2026, 3:22 a.m.
Created at: March 30, 2026, 7:23 p.m.