Triple

T23439049
Position Surface form Disambiguated ID Type / Status
Subject Bornholm Airport E565344 entity
Predicate locatedIn P40 FINISHED
Object Rønne NE NERFINISHED

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 Airport, locatedIn, Rønne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rønne
Context triple: [Bornholm Airport, locatedIn, 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. Tranekær
    Tranekær is a historic village on the Danish island of Langeland, known for its castle and scenic rural surroundings.
  • C. Søllerød
    Søllerød is a locality in Rudersdal Municipality, north of Copenhagen in eastern Denmark, known for its affluent residential areas and scenic natural surroundings.
  • D. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • E. Rødby
    Rødby is a small town on the Danish island of Lolland, known historically as a ferry port linking Denmark and Germany across the Baltic Sea.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24584f9488190bb32730bd2ce023e completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f1a5de713c8190b35bfa66dddbd5af completed April 29, 2026, 6:31 a.m.
Created at: April 17, 2026, 5:50 p.m.