Triple

T7637923
Position Surface form Disambiguated ID Type / Status
Subject Altın Boynuz E172926 entity
Predicate cityDistrict P2709 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: [Altın Boynuz, cityDistrict, Beyoğlu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beyoğlu
Context triple: [Altın Boynuz, cityDistrict, 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_69c69952849881908fdcea7a93bfc307 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fac953e08190a2f50bf783c49faf completed March 27, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9953aff988190a3224050e5706589 completed March 29, 2026, 9:10 p.m.
Created at: March 27, 2026, 3:57 p.m.