Triple

T4065052
Position Surface form Disambiguated ID Type / Status
Subject Konya High-Speed Train Station E86304 entity
Predicate serves P98 FINISHED
Object city of Konya E14759 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: city of Konya | Statement: [Konya High-Speed Train Station, serves, city of Konya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: city of Konya
Context triple: [Konya High-Speed Train Station, serves, city of Konya]
  • A. Konya chosen
    Konya is a major city in central Anatolia known for its rich Seljuk heritage and as the home of the Sufi mystic Rumi and the Whirling Dervishes.
  • B. Kütahya
    Kütahya is a historic city in western Turkey known for its Ottoman-era architecture and traditional ceramic and tile production.
  • C. Kayseri
    Kayseri is a historic city in central Turkey, known for its Seljuk and Ottoman architectural heritage and its role as a major commercial and cultural center in Anatolia.
  • D. Eskişehir
    Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
  • E. Uşak
    Uşak is a city in western Turkey known for its role in the Turkish War of Independence and its traditional carpet and textile production.
  • 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_69aed93c69208190a4efac0efe3cd69b completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbf44c888190b5746d93e9f8e3a3 completed March 9, 2026, 4:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b55b5388190a90551c43388f3fc completed March 14, 2026, 2:06 p.m.
Created at: March 9, 2026, 3:38 p.m.