Triple

T12739471
Position Surface form Disambiguated ID Type / Status
Subject Balçova E304450 entity
Predicate administrativeCenter P1474 FINISHED
Object Balçova (town) E304450 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: Balçova (town) | Statement: [Balçova, administrativeCenter, Balçova (town)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Balçova (town)
Context triple: [Balçova, administrativeCenter, Balçova (town)]
  • A. Balçova chosen
    Balçova is a coastal district of İzmir, Turkey, known for its thermal springs, residential areas, and proximity to the city center.
  • B. Ovacık
    Ovacık is a small district and town in eastern Turkey known for its mountainous terrain and location within Tunceli Province.
  • C. Kavacık
    Kavacık is a neighborhood in Istanbul’s Beykoz district, known as a key residential and commercial area on the Asian side near the Fatih Sultan Mehmet Bridge.
  • D. Çayırova
    Çayırova is a district and rapidly growing industrial-urban area located in Kocaeli Province in northwestern Turkey.
  • E. Havza
    Havza is a district and town in northern Turkey known for its thermal springs and location within Samsun Province in the Black Sea 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_69d7bdf1426c8190a4402e1c4cdec33a completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9646dfc908190bc398935d1d23537 completed April 10, 2026, 8:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c8ff57c8190a935b5c9f4bb5aa3 completed May 2, 2026, 10:37 p.m.
Created at: April 9, 2026, 5:26 p.m.