Triple

T2986634
Position Surface form Disambiguated ID Type / Status
Subject Ankara Province E80640 entity
Predicate contains P35 FINISHED
Object Çankaya E301865 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: Çankaya | Statement: [Ankara Province, contains, Çankaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Çankaya
Context triple: [Ankara Province, contains, Çankaya]
  • A. Çankaya chosen
    Çankaya is a central district of Ankara, Turkey, known for housing key government institutions, foreign embassies, and major national landmarks.
  • B. Sultanbeyli
    Sultanbeyli is a densely populated, predominantly residential district on the Asian side of Istanbul, known for its rapid urbanization and working-class character.
  • C. Çekmeköy
    Çekmeköy is a residential district on the Asian side of Istanbul, known for its rapidly developing housing areas and proximity to forested green spaces.
  • D. Üsküdar
    Üsküdar is a historic and densely populated district of Istanbul known for its waterfront along the Bosphorus, Ottoman-era mosques, and traditional neighborhoods.
  • E. Bayraklı
    Bayraklı is a coastal district of İzmir, Turkey, known for its modern business centers, residential areas, and proximity to the city’s central urban core.
  • 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_69ad8b16c3488190b47b6aa7a59a335b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99c76dfc8190b08bd6110ffabf25 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f38a1548190bdddf65853632db1 completed March 12, 2026, 12:56 a.m.
Created at: March 8, 2026, 2:59 p.m.