Triple

T4526862
Position Surface form Disambiguated ID Type / Status
Subject Konya Province E106200 entity
Predicate borderedBy P224 FINISHED
Object Mersin Province E288878 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: Mersin Province | Statement: [Konya Province, borderedBy, Mersin Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mersin Province
Context triple: [Konya Province, borderedBy, Mersin Province]
  • A. Mersin Province chosen
    Mersin Province is a coastal region in southern Turkey on the Mediterranean Sea, known for its major port city of Mersin and its rich historical and agricultural significance.
  • B. Antalya Province
    Antalya Province is a large Mediterranean coastal region in southwestern Turkey known for its major tourist resorts, beaches, and historical sites.
  • C. Hatay Province
    Hatay Province is a southern Turkish province on the Mediterranean coast, known for its multicultural heritage, ancient cities, and strategic location bordering Syria.
  • D. Kahramanmaraş Province
    Kahramanmaraş Province is an administrative region in southern Turkey known for its historic city of Kahramanmaraş and its famous Maraş ice cream.
  • E. Gaziantep Province
    Gaziantep Province is a region in southeastern Turkey known for its historic city of Gaziantep, rich culinary traditions, and strong industrial and educational development.
  • 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57760f4481908f69ce82be63d7f8 completed March 20, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf185bce7c8190ad94ab3f848a0040 completed March 21, 2026, 10:14 p.m.
Created at: March 20, 2026, 1:03 p.m.