Triple

T23478899
Position Surface form Disambiguated ID Type / Status
Subject Zeytinburnu E570346 entity
Predicate borders P224 FINISHED
Object Bayrampaşa 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: Bayrampaşa | Statement: [Zeytinburnu, borders, Bayrampaşa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bayrampaşa
Context triple: [Zeytinburnu, borders, Bayrampaşa]
  • A. Bayrampaşa chosen
    Bayrampaşa is a densely populated working- and middle-class district on Istanbul’s European side, known for its major transport links, industrial areas, and large bus terminal.
  • B. Kasımpaşa
    Kasımpaşa is a historic waterfront neighborhood in Istanbul, Turkey, known for its maritime heritage, working-class character, and proximity to the Golden Horn.
  • C. 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.
  • D. Sarayönü
    Sarayönü is a rural district and town in central Turkey, located within Konya Province and known for its agricultural activities on the Central Anatolian plateau.
  • E. Orhangazi
    Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
  • 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_69e245af8a88819084f2704f6d265a92 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a74e7e648190b89006dce7d7ce05 completed April 29, 2026, 6:38 a.m.
Created at: April 17, 2026, 6:02 p.m.