Triple

T8572625
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Lolland-Falster E202963 entity
Predicate jurisdictionOver P808 FINISHED
Object Lolland E220370 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: Lolland | Statement: [Diocese of Lolland-Falster, jurisdictionOver, Lolland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lolland
Context triple: [Diocese of Lolland-Falster, jurisdictionOver, Lolland]
  • A. Lolland chosen
    Lolland is a large, predominantly agricultural island in southeastern Denmark known for its flat landscape and sugar beet production.
  • B. Bornholm
    Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
  • C. Djursland
    Djursland is a rural peninsula in eastern Jutland, Denmark, known for its varied coastline, beaches, and popular holiday and nature tourism.
  • D. Langeland
    Langeland is a Danish island in the South Funen Archipelago, known for its rural landscapes, coastal scenery, and historical villages.
  • E. Rømø
    Rømø is a Danish island in the Wadden Sea known for its expansive sandy beaches, coastal dunes, and popular holiday resorts.
  • 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_69ca8327b0a881908606ff860713964d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea43843c8190ac2224d427bb7a75 completed March 31, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebb8e8b9481908f7096acefaa0ffd completed April 2, 2026, 6:55 p.m.
Created at: March 30, 2026, 6:21 p.m.