Triple

T13121847
Position Surface form Disambiguated ID Type / Status
Subject Bornova district E311741 entity
Predicate adjacentTo P224 FINISHED
Object Buca district E304441 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: Buca district | Statement: [Bornova district, adjacentTo, Buca district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buca district
Context triple: [Bornova district, adjacentTo, Buca district]
  • A. Buca district chosen
    Buca district is a populous residential and educational area of İzmir, Turkey, known for its large student population and urban neighborhoods.
  • B. Maçka district
    Maçka district is a mountainous area in Turkey’s Trabzon Province, known for its lush forests and as the home of the historic Sumela Monastery.
  • C. Yesil District
    Yesil District is a central administrative district of Astana, Kazakhstan, known for hosting major landmarks and modern developments in the capital.
  • D. Siyazan District
    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.
  • 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_69d806a9fe888190b081e2d9ea665d6c completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9819840b881909b76022b4c4dcaed completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e286de608190bf46af2eb656bb79 completed May 3, 2026, 5:52 a.m.
Created at: April 9, 2026, 9:06 p.m.