Triple

T17287721
Position Surface form Disambiguated ID Type / Status
Subject Mediterranean Region E419701 entity
Predicate touristDestination P530 FINISHED
Object Belek E419535 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: Belek | Statement: [Mediterranean Region, touristDestination, Belek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belek
Context triple: [Mediterranean Region, touristDestination, Belek]
  • A. Belek chosen
    Belek is a popular resort town on Turkey’s Mediterranean coast, known for its beaches, luxury hotels, and championship golf courses.
  • B. Kanık
    Kanık is the surname of the influential Turkish poet Orhan Veli Kanık, a leading figure in modern Turkish literature and the Garip movement.
  • C. Seydikemer
    Seydikemer is a rural district and town in southwestern Turkey known for its agricultural landscape and proximity to popular coastal and historical sites in Muğla Province.
  • D. Güzelyurt
    Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
  • E. Güzelyurt
    Güzelyurt is a historic town in Turkey’s Cappadocia region, known for its rock-cut churches, underground cities, and scenic valleys.
  • 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_69d886db32608190a61e18862c5a8af6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e43780fad88190b82193c8335e2f11 completed April 19, 2026, 2:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195488d2c81908ac6c19f54f61796 completed May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:40 a.m.