Triple

T7791146
Position Surface form Disambiguated ID Type / Status
Subject Yılmaz Büyükerşen E180181 entity
Predicate familyName P18 FINISHED
Object Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
 E693833 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: Büyükerşen | Statement: [Yılmaz Büyükerşen, familyName, Büyükerşen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Büyükerşen
Context triple: [Yılmaz Büyükerşen, familyName, Büyükerşen]
  • A. Malkara
    Malkara is a town and district in Turkey’s European region of Thrace, known for its agricultural economy and location within Tekirdağ Province.
  • B. Gürbulak
    Gürbulak is a Turkish border village and crossing point on the frontier with Iran, serving as a key gateway between the two countries.
  • C. Muratpaşa
    Muratpaşa is a central district and municipality of the city of Antalya in southern Turkey, known for its coastal location and urban, touristic character.
  • D. Muratlı
    Muratlı is a town and district in Turkey’s Thrace region, located within Tekirdağ Province and known for its agricultural and industrial activities.
  • E. Gölbaşı
    Gölbaşı is a district and suburban area of Ankara in central Turkey, known for its lakes, recreational areas, and proximity to the capital city.
  • 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: Büyükerşen
Triple: [Yılmaz Büyükerşen, familyName, Büyükerşen]
Generated description
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Büyükerşen
Target entity description: Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
  • A. Malkara
    Malkara is a town and district in Turkey’s European region of Thrace, known for its agricultural economy and location within Tekirdağ Province.
  • B. Gürbulak
    Gürbulak is a Turkish border village and crossing point on the frontier with Iran, serving as a key gateway between the two countries.
  • C. Muratpaşa
    Muratpaşa is a central district and municipality of the city of Antalya in southern Turkey, known for its coastal location and urban, touristic character.
  • D. Muratlı
    Muratlı is a town and district in Turkey’s Thrace region, located within Tekirdağ Province and known for its agricultural and industrial activities.
  • E. Gölbaşı
    Gölbaşı is a district and suburban area of Ankara in central Turkey, known for its lakes, recreational areas, and proximity to the capital city.
  • 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_69ca827d22208190b4dc5aa680edcf5d completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cae9375dcc8190a6cb696c02aeceb7 completed March 30, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb13b38e708190a688ce4effbf7c48 completed March 31, 2026, 12:22 a.m.
NEDg Description generation batch_69cb1636b0d48190a57c2d3a7b3b41ed completed March 31, 2026, 12:32 a.m.
NED2 Entity disambiguation (via description) batch_69cb1a29d2988190bb64aada0d2ef463 completed March 31, 2026, 12:49 a.m.
Created at: March 30, 2026, 4:30 p.m.