Triple

T14364839
Position Surface form Disambiguated ID Type / Status
Subject Çankırı E356203 entity
Predicate roadConnection P385 FINISHED
Object Kastamonu E404659 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: Kastamonu | Statement: [Çankırı, roadConnection, Kastamonu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kastamonu
Context triple: [Çankırı, roadConnection, Kastamonu]
  • A. Balıkesir
    Balıkesir is a city in northwestern Turkey known for its agricultural economy, proximity to both the Marmara and Aegean seas, and role as the administrative center of Balıkesir Province.
  • B. Zonguldak
    Zonguldak is a port city on Turkey’s Black Sea coast known historically for its coal mining industry.
  • C. Giresun
    Giresun is a coastal city in northeastern Turkey known for its hazelnut production and scenic location along the Black Sea.
  • D. Afyon
    Afyon is the former name of Afyonkarahisar, a city in western Turkey known for its strategic location, thermal springs, and opium poppy production.
  • E. Kastamonu Province chosen
    Kastamonu Province is a historically rich, mountainous region in northern Turkey, known for its traditional architecture, forests, and cultural heritage.
  • 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_69d8279163a081908aec45c0e3f1e02f completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8fad48748190a0f34ca4d02f9a3c completed April 14, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff82dcec0c8190a5e4ac0aafd34504 completed May 9, 2026, 6:54 p.m.
Created at: April 10, 2026, 1:15 a.m.