Triple

T7862150
Position Surface form Disambiguated ID Type / Status
Subject Istanbul Province E182524 entity
Predicate hasDistrict P459 FINISHED
Object Beyoğlu E161445 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: Beyoğlu | Statement: [Istanbul Province, hasDistrict, Beyoğlu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beyoğlu
Context triple: [Istanbul Province, hasDistrict, Beyoğlu]
  • A. Beyoğlu district chosen
    Beyoğlu district is a historic and vibrant central area of Istanbul, Turkey, known for its cultural landmarks, nightlife, and cosmopolitan atmosphere.
  • B. Üsküdar
    Üsküdar is a historic and densely populated district of Istanbul known for its waterfront along the Bosphorus, Ottoman-era mosques, and traditional neighborhoods.
  • C. Bakırköy
    Bakırköy is a coastal district on the European side of Istanbul, Turkey, known for its residential neighborhoods, shopping centers, and seaside recreation areas.
  • D. Kadıköy
    Kadıköy is a historic district on the Asian side of Istanbul, Turkey, known for its ancient roots (including the site of the Council of Chalcedon), vibrant cultural life, and bustling waterfront.
  • E. Şişli district
    Şişli district is a central and densely populated area on Istanbul’s European side, known for its commercial centers, business districts, and historic neighborhoods.
  • 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_69ca82887fd48190975896bf38c4596b completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb36be5f408190b82a097b0825c57a completed March 31, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc934b8fb88190b84e5d6317c966b2 completed April 1, 2026, 3:38 a.m.
Created at: March 30, 2026, 4:53 p.m.