Triple

T9747674
Position Surface form Disambiguated ID Type / Status
Subject Shabran District E236355 entity
Predicate borderedBy P224 FINISHED
Object Siyazan District E814156 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: Siyazan District | Statement: [Shabran District, borderedBy, Siyazan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siyazan District
Context triple: [Shabran District, borderedBy, Siyazan District]
  • A. Siyazan District chosen
    Siyazan District is an administrative region in northeastern Azerbaijan known for its location along the Caspian Sea coast and its role in the country’s oil and agricultural sectors.
  • B. Gizab District
    Gizab District is an administrative district located within Daykundi Province in central Afghanistan.
  • C. Gizab District
    Gizab District is an administrative district located within Uruzgan Province in central Afghanistan, known for its mountainous terrain and history of conflict.
  • D. Ismayilli District
    Ismayilli District is an administrative region in northern Azerbaijan known for its mountainous landscapes, forests, and historical villages.
  • E. Atabey District
    Atabey District is an administrative district in Turkey’s Isparta Province, known as the birthplace of former Turkish President Süleyman Demirel.
  • 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_69ca84d3e24481908a476e2231123cf9 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f677830819096d388b9c798ecd5 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc4321cc8190a5197d87ebfe38fb completed April 5, 2026, 2:43 a.m.
Created at: March 30, 2026, 8:23 p.m.