Triple

T12739260
Position Surface form Disambiguated ID Type / Status
Subject İzmir urban transport network E304444 entity
Predicate connects P390 FINISHED
Object Menemen E311742 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: Menemen | Statement: [İzmir urban transport network, connects, Menemen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Menemen
Context triple: [İzmir urban transport network, connects, Menemen]
  • A. Menemen chosen
    Menemen is a district and town in İzmir Province, Turkey, known for its agricultural production and as part of the greater İzmir metropolitan area.
  • B. Menemen
    Menemen is a traditional Turkish dish of eggs softly scrambled with tomatoes, peppers, and olive oil, often enjoyed for breakfast with bread.
  • C. Güzelyurt
    Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
  • D. Güzelyurt
    Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
  • 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_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_69f7c6f404888190b7bb47bff1a7c1e1 completed May 3, 2026, 10:06 p.m.
Created at: April 9, 2026, 5:26 p.m.