Triple

T7957447
Position Surface form Disambiguated ID Type / Status
Subject Uğur Dündar E184774 entity
Predicate employer P7 FINISHED
Object Sözcü TV E654078 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: Sözcü TV | Statement: [Uğur Dündar, employer, Sözcü TV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sözcü TV
Context triple: [Uğur Dündar, employer, Sözcü TV]
  • A. Mashriq TV
    Mashriq TV is a Pashto-language television channel known for broadcasting news, current affairs, and cultural programming to Pashto-speaking audiences.
  • B. CNN Türk
    CNN Türk is a major Turkish television news channel that provides 24-hour national and international news coverage.
  • C. NTV
    NTV is a major Japanese commercial television network known for its wide range of news, entertainment, and sports programming.
  • D. NTV chosen
    NTV is a Turkish television news channel known for its 24-hour news coverage and influential role in Turkey’s media landscape.
  • E. Wasafi TV
    Wasafi TV is a Tanzanian entertainment television channel closely associated with musician and entrepreneur Diamond Platnumz, known for music, lifestyle, and pop culture programming.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca8293a2388190aace944d7ed9c0c0 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b7ebb24819094bc011d51ef63fb completed March 31, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe072ef4c8190a8e078c5280913db completed March 31, 2026, 2:55 p.m.
Created at: March 30, 2026, 5:11 p.m.