Triple

T11744014
Position Surface form Disambiguated ID Type / Status
Subject Starogard Gdański E279225 entity
Predicate hasTwinTown P919 FINISHED
Object Viborg E740334 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: Viborg | Statement: [Starogard Gdański, hasTwinTown, Viborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viborg
Context triple: [Starogard Gdański, hasTwinTown, Viborg]
  • A. Viborg chosen
    Viborg is one of Denmark’s oldest cities, historically significant as a medieval political and religious center on the Jutland peninsula.
  • B. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • C. Vordingborg
    Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
  • D. Fredericia
    Fredericia is a Danish coastal town in Jutland known for its historic 17th-century fortress and well-preserved ramparts.
  • E. Esbjerg
    Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4f2a38c8190a682d8dae1ab9415 completed April 10, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69f280d8f604819095823c3650adbad7 completed April 29, 2026, 10:06 p.m.
Created at: April 8, 2026, 9:41 p.m.