Triple
T8849386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierre Sermanet |
E210596
|
entity |
| Predicate | coAuthorWith |
P398
|
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: [Pierre Sermanet, coAuthorWith, Koray Kavukcuoglu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koray Kavukcuoglu Context triple: [Pierre Sermanet, coAuthorWith, 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_69ca838a424c8190b1ecac115c2927e7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60abb0748190af41d4e1f419e39c |
completed | April 1, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf89c6788881908d6f5c49434b556d |
completed | April 3, 2026, 9:35 a.m. |
Created at: March 30, 2026, 6:49 p.m.