Triple
T7281638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yıldız Kenter |
E163162
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Yıldız Kenter |
E163162
|
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: Yıldız Kenter | Statement: [Yıldız Kenter, name, Yıldız Kenter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yıldız Kenter Context triple: [Yıldız Kenter, name, Yıldız Kenter]
-
A.
Yıldız Kenter
chosen
Yıldız Kenter was a renowned Turkish stage and film actress, acclaimed theater educator, and one of the leading figures of modern Turkish performing arts.
-
B.
Kassar
Kassar is a surname most notably associated with Mario F. Kassar, a prominent film producer known for major Hollywood action blockbusters.
-
C.
Shaye
Shaye is a surname most notably associated with Robert Shaye, the American film executive and founder of New Line Cinema.
-
D.
Turei Zahav
Turei Zahav is a classic 17th-century halachic commentary by Rabbi David HaLevi Segal, primarily on the Shulchan Aruch and widely studied in traditional Jewish law.
-
E.
Faris
Faris is the surname of American actress and comedian Anna Faris, known for her roles in the Scary Movie film series and various comedy projects.
- 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_69c6885c5964819085b209701769877f |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb34fe0c8190a642fd3339f0cacd |
completed | March 27, 2026, 8:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db379e1c81908ebd4c44504ce5fb |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:59 p.m.