Triple

T6754033
Position Surface form Disambiguated ID Type / Status
Subject Sivas E154408 entity
Predicate locatedIn P40 FINISHED
Object Sivas Province E267227 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: Sivas Province | Statement: [Sivas, locatedIn, Sivas Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sivas Province
Context triple: [Sivas, locatedIn, Sivas Province]
  • A. Sivas Province chosen
    Sivas Province is a large, historically significant region in central Turkey known for its diverse ethnic makeup, including Zazaki-speaking communities, and its role as a cultural and transportation crossroads in Anatolia.
  • B. Muş Province
    Muş Province is an eastern Turkish province known for its mountainous terrain, harsh continental climate, and predominantly Kurdish population.
  • C. Elazığ Province
    Elazığ Province is an eastern Turkish province known for its significant Zaza-speaking population, rich Anatolian history, and mountainous landscapes.
  • D. Erzurum Province
    Erzurum Province is a large, historically significant region in eastern Turkey known for its mountainous terrain, harsh winters, and the city of Erzurum as its administrative center.
  • E. Bingöl Province
    Bingöl Province is an eastern Turkish province known for its mountainous landscape, Kurdish and Zaza populations, and rich local culture.
  • 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_69c6880fd5808190be684854081e27dd completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1f32fa08190bb23dc24fef14c8d completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c761727ed4819086c2686490420b76 completed March 28, 2026, 5:04 a.m.
Created at: March 27, 2026, 2:11 p.m.