Triple

T11003599
Position Surface form Disambiguated ID Type / Status
Subject Matching Networks for One Shot Learning E260058 entity
Predicate hasAuthor P4244 FINISHED
Object Koray Kavukcuoglu E41248 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: Koray Kavukcuoglu | Statement: [Matching Networks for One Shot Learning, hasAuthor, Koray Kavukcuoglu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Koray Kavukcuoglu
Context triple: [Matching Networks for One Shot Learning, hasAuthor, Koray Kavukcuoglu]
  • A. Koray Kavukcuoglu chosen
    Koray Kavukcuoglu is a prominent computer scientist and machine learning researcher known for his leadership in deep learning and artificial intelligence at DeepMind.
  • B. Oktay Caglar
    Oktay Caglar is an entrepreneur best known as one of the co-founders of the online learning platform Udemy.
  • C. Cüneyt Arcayürek
    Cüneyt Arcayürek was a prominent Turkish journalist and political columnist known for his in-depth coverage and analysis of Turkish politics.
  • D. Kerem Bürsin
    Kerem Bürsin is a Turkish-American actor best known for his leading roles in popular Turkish television dramas and romantic comedies.
  • E. Erdem Cansever
    Erdem Cansever is known primarily as the child of renowned Turkish modernist poet Edip Cansever.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797546f448190946ee6442d657dc5 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.