Triple

T7141763
Position Surface form Disambiguated ID Type / Status
Subject Tekirdağ Province E166458 entity
Predicate hasWineRegion P285 FINISHED
Object Şarköy E644679 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: Şarköy | Statement: [Tekirdağ Province, hasWineRegion, Şarköy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Şarköy
Context triple: [Tekirdağ Province, hasWineRegion, Şarköy]
  • A. Şarköy chosen
    Şarköy is a coastal town and district in northwestern Turkey, known for its beaches, vineyards, and wine production along the Sea of Marmara.
  • B. Şirinköy
    Şirinköy is a village located on Gökçeada, Turkey’s largest Aegean island in the Çanakkale Province.
  • C. Dereköy
    Dereköy is a village on the Aegean island of Gökçeada (historically known as Imbros/İmroz) in Turkey, noted for its traditional stone houses and Greek heritage.
  • D. Karaköy
    Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
  • E. Muratpaşa
    Muratpaşa is a central district and municipality of the city of Antalya in southern Turkey, known for its coastal location and urban, touristic character.
  • 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_69c6888579d481909e05a8d6b81bf733 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e778875c8190a5202d3efe5a842d completed March 27, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7ad940bd88190abec876e2d2369bf completed March 28, 2026, 10:29 a.m.
Created at: March 27, 2026, 2:45 p.m.