Triple

T7303647
Position Surface form Disambiguated ID Type / Status
Subject Tarık Akan E167919 entity
Predicate child P120 FINISHED
Object Yaprak Üregül
Yaprak Üregül is known as the child of the late Turkish cinema icon Tarık Akan.
 E659902 NE FINISHED

How this triple was built (4 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: Yaprak Üregül | Statement: [Tarık Akan, child, Yaprak Üregül]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yaprak Üregül
Context triple: [Tarık Akan, child, Yaprak Üregül]
  • A. Şükran Güngör
    Şükran Güngör was a prominent Turkish stage and film actor, celebrated for his work in modern Turkish theatre and his long association with the Kenter Theatre.
  • B. Yasemin Erkut
    Yasemin Erkut is known as the spouse of the late Turkish actor and prominent cinema figure Tarık Akan.
  • C. Melih Gökçek
    Melih Gökçek is a Turkish politician best known for his long tenure as the mayor of Ankara and his prominent role in conservative Islamist politics.
  • D. Kubra Balik
    Kubra Balik is a powerful and ruthless international drug trafficker who serves as a major antagonist in the television series "Orange Is the New Black."
  • E. Canan Karatay
    Canan Karatay is a Turkish cardiologist and academic known for her influential and often controversial views on nutrition and healthy living.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yaprak Üregül
Triple: [Tarık Akan, child, Yaprak Üregül]
Generated description
Yaprak Üregül is known as the child of the late Turkish cinema icon Tarık Akan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yaprak Üregül
Target entity description: Yaprak Üregül is known as the child of the late Turkish cinema icon Tarık Akan.
  • A. Şükran Güngör
    Şükran Güngör was a prominent Turkish stage and film actor, celebrated for his work in modern Turkish theatre and his long association with the Kenter Theatre.
  • B. Yasemin Erkut
    Yasemin Erkut is known as the spouse of the late Turkish actor and prominent cinema figure Tarık Akan.
  • C. Melih Gökçek
    Melih Gökçek is a Turkish politician best known for his long tenure as the mayor of Ankara and his prominent role in conservative Islamist politics.
  • D. Kubra Balik
    Kubra Balik is a powerful and ruthless international drug trafficker who serves as a major antagonist in the television series "Orange Is the New Black."
  • E. Canan Karatay
    Canan Karatay is a Turkish cardiologist and academic known for her influential and often controversial views on nutrition and healthy living.
  • F. None of above. chosen

Provenance (5 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb352ec8190846eff044e08805e completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c802aa1c608190a58e7be2c12fcf7e completed March 28, 2026, 4:32 p.m.
NEDg Description generation batch_69c803ebdc208190b8a026887aefb177 completed March 28, 2026, 4:38 p.m.
NED2 Entity disambiguation (via description) batch_69c804ad01fc819094b6646c3eaa5647 completed March 28, 2026, 4:41 p.m.
Created at: March 27, 2026, 3:01 p.m.