Triple

T13647636
Position Surface form Disambiguated ID Type / Status
Subject Xanthos E326145 entity
Predicate locatedNear P294 FINISHED
Object Kınık E696152 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: Kınık | Statement: [Xanthos, locatedNear, Kınık]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kınık
Context triple: [Xanthos, locatedNear, Kınık]
  • A. Kınık chosen
    Kınık is a town and district in western Turkey known for its agricultural activities and location within İzmir Province.
  • B. Kırcaali
    Kırcaali (Kardzhali) is a city in southern Bulgaria known as a regional center in the Eastern Rhodope Mountains with a significant historical Turkish population.
  • C. Malkara
    Malkara is a town and district in Turkey’s European region of Thrace, known for its agricultural economy and location within Tekirdağ Province.
  • D. Kurukdere
    Kurukdere is a locality in present-day eastern Turkey known primarily as the site of a significant battle during the Crimean War between Russian and Ottoman forces.
  • E. Büyükerşen
    Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc6073e888190965456a639839749 completed April 12, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7b061305881909a9a9bfaa5922225 completed May 3, 2026, 8:30 p.m.
Created at: April 9, 2026, 9:52 p.m.