Triple
T21233058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nur Fettahoğlu |
E523272
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nur Fettahoğlu |
—
|
NE NERFINISHED |
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: Nur Fettahoğlu | Statement: [Nur Fettahoğlu, name, Nur Fettahoğlu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nur Fettahoğlu Context triple: [Nur Fettahoğlu, name, Nur Fettahoğlu]
-
A.
Nur Fettahoğlu
chosen
Nur Fettahoğlu is a Turkish-German actress best known for her role in the historical television series "Muhteşem Yüzyıl" ("Magnificent Century").
-
B.
Mümtaz Turhan
Mümtaz Turhan was a prominent Turkish social psychologist and academic known for his influential work on cultural change, modernization, and education in Turkey.
-
C.
Nurettin Sönmez
Nurettin Sönmez is a Turkish actor best known internationally for his role as the warrior Bamsı Beyrek in the historical television series "Diriliş: Ertuğrul."
-
D.
Tahsin Banguoğlu
Tahsin Banguoğlu was a Turkish linguist, academic, and politician known for his influential work on Turkish language and grammar.
-
E.
Metin Feyzioğlu
Metin Feyzioğlu is a prominent Turkish lawyer, academic, and former president of the Union of Turkish Bar Associations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b512ad94819087942b2ed925185f |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e734b1524c8190a77eaf2fabd601c3 |
completed | April 21, 2026, 8:26 a.m. |
Created at: April 16, 2026, 3:45 p.m.