Triple

T16151140
Position Surface form Disambiguated ID Type / Status
Subject European side of Istanbul E391910 entity
Predicate hasPart P35 FINISHED
Object Arnavutköy E238391 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: Arnavutköy | Statement: [European side of Istanbul, hasPart, Arnavutköy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arnavutköy
Context triple: [European side of Istanbul, hasPart, Arnavutköy]
  • A. Arnavutköy chosen
    Arnavutköy is a district on the European side of Istanbul, Turkey, known for its rapidly developing urban areas and hosting the city’s main international airport.
  • B. Silivri
    Silivri is a coastal district and popular seaside resort area on the European side of Istanbul, known for its beaches, agriculture, and large high-security prison complex.
  • C. Bayındır
    Bayındır is a town and district in western Turkey known for its agricultural production and location within İzmir Province.
  • D. Ayvacık
    Ayvacık is a small town and district in Turkey’s Çanakkale Province, known for its traditional stone houses and proximity to the Aegean coast and ancient sites like Assos.
  • E. Ayvacık
    Ayvacık is a rural district and town in Turkey’s Black Sea region, located within Samsun Province and known for its natural landscapes and agricultural 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_69d87f1c65e48190aa2b4c472e9bafc4 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21d981950819087fdacc7879dca97 completed April 17, 2026, 11:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f39008c819095ad8512eb119ee8 completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:01 a.m.