Triple

T2061367
Position Surface form Disambiguated ID Type / Status
Subject Welfare Party E45796 entity
Predicate notableMember P10 FINISHED
Object Bülent Arınç E174001 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: Bülent Arınç | Statement: [Welfare Party, notableMember, Bülent Arınç]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bülent Arınç
Context triple: [Welfare Party, notableMember, Bülent Arınç]
  • A. Bülent Arınç chosen
    Bülent Arınç is a Turkish politician and lawyer who served as Speaker of the Grand National Assembly and as a prominent member of the Justice and Development Party (AKP).
  • B. Süleyman Demirel
    Süleyman Demirel was a prominent Turkish politician who served multiple terms as Turkey’s prime minister and later as its ninth president.
  • C. Ahmet Necdet Sezer
    Ahmet Necdet Sezer is a Turkish jurist and politician who served as the 10th President of Turkey from 2000 to 2007.
  • D. Bülent Korkmaz
    Bülent Korkmaz is a former Turkish central defender best known as a one-club legend and long-time captain of Galatasaray, with whom he won numerous domestic titles and the 2000 UEFA Cup.
  • E. Tarık Akan
    Tarık Akan was a prominent Turkish film actor and producer known for his leading roles in 1970s–80s Turkish cinema and his later work in socially conscious films.
  • 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_69a8891b38288190abd572ccad9b6928 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9d0ecf08190aec20338a6ba9911 completed March 7, 2026, 5:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69af989af058819082c35bee706d0ea0 completed March 10, 2026, 4:05 a.m.
Created at: March 4, 2026, 7:40 p.m.