Triple

T6138151
Position Surface form Disambiguated ID Type / Status
Subject Bombardier Cityrunner E136887 entity
Predicate usedIn P98 FINISHED
Object Eskişehir E344696 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: Eskişehir | Statement: [Bombardier Cityrunner, usedIn, Eskişehir]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eskişehir
Context triple: [Bombardier Cityrunner, usedIn, Eskişehir]
  • A. Eskişehir chosen
    Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
  • B. 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.
  • C. Konya
    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.
  • D. Bursa
    Bursa is a major city in northwestern Turkey known historically as the first capital of the Ottoman Empire and today as an important industrial and cultural center.
  • E. Aksaray
    Aksaray is a historic city in central Turkey known for its location on the ancient Silk Road and its proximity to the Cappadocia region.
  • 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_69c008a179388190a3b5a081bbf46d55 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05c83aefc8190b0e250e96f2b10b4 completed March 22, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7421871888190aab99c6c5f6c147d completed March 28, 2026, 2:51 a.m.
Created at: March 22, 2026, 4:15 p.m.