Triple

T27032727
Position Surface form Disambiguated ID Type / Status
Subject Selvi Boylum Al Yazmalım E680965 entity
Predicate leadActressRole P9616 FINISHED
Object Türkan Şoray as Asya
Türkan Şoray as Asya is the iconic central character in the classic Turkish film "Selvi Boylum Al Yazmalım," embodying a powerful, emotional portrayal of love, sacrifice, and resilience.
E1752846 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: Türkan Şoray as Asya | Statement: [Selvi Boylum Al Yazmalım, leadActressRole, Türkan Şoray as Asya]
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: Türkan Şoray as Asya
Triple: [Selvi Boylum Al Yazmalım, leadActressRole, Türkan Şoray as Asya]
Generated description
Türkan Şoray as Asya is the iconic central character in the classic Turkish film "Selvi Boylum Al Yazmalım," embodying a powerful, emotional portrayal of love, sacrifice, and resilience.

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_69eeeb5566f08190813daf896fa3da04 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f6223766208190a606c293dd7bb250 completed May 2, 2026, 4:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123abc4008819086c50a92d54db375 completed May 23, 2026, 11:39 p.m.
NEDg Description generation batch_6a123ba9aea081909f20ff78ab91747e completed May 23, 2026, 11:43 p.m.
NED2 Entity disambiguation (via description) batch_6a123c56268c81909d0e71dad0aeab01 completed May 23, 2026, 11:46 p.m.
Created at: April 27, 2026, 7:14 a.m.