Triple

T10436252
Position Surface form Disambiguated ID Type / Status
Subject Fenerbahçe Ülker E246046 entity
Predicate sponsor P67 FINISHED
Object Ülker
Ülker is a major Turkish food company best known for its wide range of confectionery and snack products.
 E864042 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: Ülker | Statement: [Fenerbahçe Ülker, sponsor, Ülker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ülker
Context triple: [Fenerbahçe Ülker, sponsor, Ülker]
  • A. Dursunbey
    Dursunbey is a town and district in western Turkey known for its forestry, timber production, and rural character within Balıkesir Province.
  • B. Tevfikiye
    Tevfikiye is a village in northwestern Turkey located close to the archaeological site of Hisarlik, widely identified with ancient Troy.
  • C. Nişantaşı
    Nişantaşı is an upscale neighborhood in Istanbul known for its luxury shopping streets, stylish cafes, and elegant residential buildings.
  • D. Kandilli
    Kandilli is a historic neighborhood on Istanbul’s Asian shore of the Bosphorus, known for its waterfront residences, scenic views, and role as a stop on local ferry routes.
  • E. Beylerbeyi
    Beylerbeyi is a historic neighborhood on Istanbul’s Asian shore of the Bosphorus, known for its waterfront mansions and the 19th-century Beylerbeyi Palace.
  • 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: Ülker
Triple: [Fenerbahçe Ülker, sponsor, Ülker]
Generated description
Ülker is a major Turkish food company best known for its wide range of confectionery and snack products.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ülker
Target entity description: Ülker is a major Turkish food company best known for its wide range of confectionery and snack products.
  • A. Dursunbey
    Dursunbey is a town and district in western Turkey known for its forestry, timber production, and rural character within Balıkesir Province.
  • B. Tevfikiye
    Tevfikiye is a village in northwestern Turkey located close to the archaeological site of Hisarlik, widely identified with ancient Troy.
  • C. Nişantaşı
    Nişantaşı is an upscale neighborhood in Istanbul known for its luxury shopping streets, stylish cafes, and elegant residential buildings.
  • D. Kandilli
    Kandilli is a historic neighborhood on Istanbul’s Asian shore of the Bosphorus, known for its waterfront residences, scenic views, and role as a stop on local ferry routes.
  • E. Beylerbeyi
    Beylerbeyi is a historic neighborhood on Istanbul’s Asian shore of the Bosphorus, known for its waterfront mansions and the 19th-century Beylerbeyi Palace.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea843f1c8190afca4a42bc364468 completed April 7, 2026, 11:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69d87ec1a0908190b5369ad55cf2bcb1 completed April 10, 2026, 4:38 a.m.
NEDg Description generation batch_69d886c3fdcc8190a67a7f7788b8a2e8 completed April 10, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d88dc15ab481909011c5de93bbab14 completed April 10, 2026, 5:42 a.m.
Created at: April 6, 2026, 12:14 p.m.