Triple

T1073448
Position Surface form Disambiguated ID Type / Status
Subject Bornholm E23381 entity
Predicate largestCity P235 FINISHED
Object Rønne E135602 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: Rønne | Statement: [Bornholm, largestCity, Rønne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rønne
Context triple: [Bornholm, largestCity, Rønne]
  • A. Rønne chosen
    Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
  • B. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • C. 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.
  • D. Knudstrup
    Knudstrup is a small locality in present-day Sweden historically notable as the birthplace of the astronomer Tycho Brahe.
  • E. Hillerød
    Hillerød is a Danish town on the island of Zealand, known for the historic Frederiksborg Castle and its role as a regional administrative and cultural 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_69a493ee1f908190992b5f0d1b04459b completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b92afad88190b7705923f71fc760 completed March 1, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac763a7cd481909bf83a2e67c0d9f5 completed March 7, 2026, 7:02 p.m.
Created at: March 1, 2026, 7:42 p.m.