Triple

T2314578
Position Surface form Disambiguated ID Type / Status
Subject Zehra Zümrüt Selçuk E51033 entity
Predicate familyName P18 FINISHED
Object Selçuk
Selçuk is a Turkish surname borne by various notable figures in Turkey, including politicians and public officials.
 E255736 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: Selçuk | Statement: [Zehra Zümrüt Selçuk, familyName, Selçuk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Selçuk
Context triple: [Zehra Zümrüt Selçuk, familyName, Selçuk]
  • A. Toprakkale
    Toprakkale is an ancient fortress and archaeological site in eastern Turkey that served as a significant center of the Iron Age Kingdom of Urartu.
  • B. Boğazköy
    Boğazköy is an important archaeological site in central Turkey best known as the location of Hattusa, the former capital of the Hittite Empire.
  • C. Beştepe
    Beştepe is a neighborhood in Ankara, Turkey, best known as the site of the Turkish Presidential Complex.
  • D. Söğüt
    Söğüt is a historic town in northwestern Turkey renowned as the early center of the Ottoman beylik and the birthplace of the Ottoman Empire.
  • E. Karaköy
    Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
  • 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: Selçuk
Triple: [Zehra Zümrüt Selçuk, familyName, Selçuk]
Generated description
Selçuk is a Turkish surname borne by various notable figures in Turkey, including politicians and public officials.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Selçuk
Target entity description: Selçuk is a Turkish surname borne by various notable figures in Turkey, including politicians and public officials.
  • A. Toprakkale
    Toprakkale is an ancient fortress and archaeological site in eastern Turkey that served as a significant center of the Iron Age Kingdom of Urartu.
  • B. Boğazköy
    Boğazköy is an important archaeological site in central Turkey best known as the location of Hattusa, the former capital of the Hittite Empire.
  • C. Beştepe
    Beştepe is a neighborhood in Ankara, Turkey, best known as the site of the Turkish Presidential Complex.
  • D. Söğüt
    Söğüt is a historic town in northwestern Turkey renowned as the early center of the Ottoman beylik and the birthplace of the Ottoman Empire.
  • E. Karaköy
    Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
  • 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc61d41f88190983f8947667b4c7a completed March 7, 2026, 6:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae896236f08190b3874854279bbdf7 completed March 9, 2026, 8:48 a.m.
NEDg Description generation batch_69ae8b0b27cc819099a5df60d678d3e2 completed March 9, 2026, 8:55 a.m.
NED2 Entity disambiguation (via description) batch_69ae8b79633881908acf94f8db389c0f completed March 9, 2026, 8:57 a.m.
Created at: March 4, 2026, 7:49 p.m.