Triple

T15779709
Position Surface form Disambiguated ID Type / Status
Subject Uşak Province E382580 entity
Predicate seat P75 FINISHED
Object Uşak E304022 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: Uşak | Statement: [Uşak Province, seat, Uşak]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uşak
Context triple: [Uşak Province, seat, Uşak]
  • A. Uşak chosen
    Uşak is a city in western Turkey known for its role in the Turkish War of Independence and its traditional carpet and textile production.
  • B. Karabük
    Karabük is an industrial city in northern Turkey best known for its historic iron and steel industry and its proximity to the UNESCO-listed Ottoman town of Safranbolu.
  • C. 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.
  • D. 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.
  • E. Suşehri
    Suşehri is a town and district in northeastern Turkey known for its location within Sivas Province and its surrounding mountainous landscape.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e053fea90081908e3fe4f91475bead completed April 16, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a001f7d79348190aba1889a7eb3d7c8 completed May 10, 2026, 6:02 a.m.
Created at: April 10, 2026, 4:48 a.m.