Triple
T21233046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muhteşem Yüzyıl |
E523271
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Mehmet Günsür
Mehmet Günsür is a Turkish actor and former model best known internationally for his roles in popular Turkish television series and films.
|
E1475660
|
NE FINISHED |
How this triple was built (4 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: Mehmet Günsür | Statement: [Muhteşem Yüzyıl, hasCastMember, Mehmet Günsür]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mehmet Günsür Context triple: [Muhteşem Yüzyıl, hasCastMember, Mehmet Günsür]
-
A.
Mehmet Selçuk
Mehmet Selçuk is a Turkish professional footballer known for playing as a midfielder in Turkey’s top leagues.
-
B.
Mehmet Bozdağ
Mehmet Bozdağ is a Turkish screenwriter and producer best known for creating popular historical television dramas centered on the early Ottoman period.
-
C.
Mehmet Çevik
Mehmet Çevik is a Turkish actor best known for his roles in popular historical television dramas.
-
D.
Mehmet Dalman
Mehmet Dalman is a British-Turkish investment banker and football executive best known for serving as chairman of Cardiff City Football Club.
-
E.
Ahmet Üzümcü
Ahmet Üzümcü is a Turkish diplomat best known for leading the Organisation for the Prohibition of Chemical Weapons during its Nobel Peace Prize–winning efforts to eliminate chemical weapons.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mehmet Günsür Triple: [Muhteşem Yüzyıl, hasCastMember, Mehmet Günsür]
Generated description
Mehmet Günsür is a Turkish actor and former model best known internationally for his roles in popular Turkish television series and films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mehmet Günsür Target entity description: Mehmet Günsür is a Turkish actor and former model best known internationally for his roles in popular Turkish television series and films.
-
A.
Mehmet Selçuk
Mehmet Selçuk is a Turkish professional footballer known for playing as a midfielder in Turkey’s top leagues.
-
B.
Mehmet Bozdağ
Mehmet Bozdağ is a Turkish screenwriter and producer best known for creating popular historical television dramas centered on the early Ottoman period.
-
C.
Mehmet Çevik
Mehmet Çevik is a Turkish actor best known for his roles in popular historical television dramas.
-
D.
Mehmet Dalman
Mehmet Dalman is a British-Turkish investment banker and football executive best known for serving as chairman of Cardiff City Football Club.
-
E.
Ahmet Üzümcü
Ahmet Üzümcü is a Turkish diplomat best known for leading the Organisation for the Prohibition of Chemical Weapons during its Nobel Peace Prize–winning efforts to eliminate chemical weapons.
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0997f2f2148190bda96ebfd52c046c |
completed | May 17, 2026, 10:26 a.m. |
| NEDg | Description generation | batch_6a09986fabd08190b2b017aceb9827f6 |
completed | May 17, 2026, 10:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0998e8f2488190800c67f13fb6b1a4 |
completed | May 17, 2026, 10:31 a.m. |
Created at: April 16, 2026, 3:45 p.m.