Triple

T23001444
Position Surface form Disambiguated ID Type / Status
Subject Sakarya, Turkey E572641 entity
Predicate hasDistrict P459 FINISHED
Object Akyazı 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: Akyazı | Statement: [Sakarya, Turkey, hasDistrict, Akyazı]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Akyazı
Context triple: [Sakarya, Turkey, hasDistrict, Akyazı]
  • A. Akyazı chosen
    Akyazı is a town and district in northwestern Turkey known for its agricultural activities and proximity to natural attractions such as forests and thermal springs.
  • B. Alacakaya
    Alacakaya is a small town and district in eastern Turkey known for its marble quarries and location within Elazığ Province.
  • C. Cayma
    Cayma is a district capital in the Arequipa region of southern Peru, known for its colonial-era architecture and scenic views of nearby volcanoes.
  • D. Yassıada
    Yassıada is one of Istanbul’s Princes' Islands in the Sea of Marmara, historically known for its use as a place of exile and for hosting the 1960–61 trials of Turkish political leaders.
  • E. Aksay
    Aksay is a small industrial city in western Kazakhstan known for its role in the regional oil and gas sector.
  • 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_69e245b6a3ac81908087599eefe3e365 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18353d05481909abacb48a14ef21e completed April 29, 2026, 4:04 a.m.
Created at: April 17, 2026, 3:50 p.m.