Triple
T18502471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diriliş: Ertuğrul |
E452110
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Kaptan Gürman
Kaptan Gürman is an actor known for his role in the popular Turkish historical drama series "Diriliş: Ertuğrul."
|
E1328214
|
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: Kaptan Gürman | Statement: [Diriliş: Ertuğrul, castMember, Kaptan Gürman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaptan Gürman Context triple: [Diriliş: Ertuğrul, castMember, Kaptan Gürman]
-
A.
Serdar
Serdar is a town in western Turkmenistan that serves as an administrative and transport hub in the Balkan Region.
-
B.
Turgut
Turgut is a masculine Turkish given name most notably borne by the influential modernist poet Turgut Uyar.
-
C.
Astsubay Başçavuş
Astsubay Başçavuş is a senior non-commissioned officer rank in the Turkish Armed Forces, typically held by experienced enlisted leaders responsible for unit-level management and training.
-
D.
Bamsi Beyrek
Bamsi Beyrek is a legendary hero of the Oghuz Turkic epic tradition, celebrated for his bravery, loyalty, and romantic exploits in the Book of Dede Korkut.
-
E.
Celal
Celal is a central character in Orhan Pamuk’s novel "The Black Book," around whom much of the story’s mystery and identity exploration revolves.
- 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: Kaptan Gürman Triple: [Diriliş: Ertuğrul, castMember, Kaptan Gürman]
Generated description
Kaptan Gürman is an actor known for his role in the popular Turkish historical drama series "Diriliş: Ertuğrul."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kaptan Gürman Target entity description: Kaptan Gürman is an actor known for his role in the popular Turkish historical drama series "Diriliş: Ertuğrul."
-
A.
Serdar
Serdar is a town in western Turkmenistan that serves as an administrative and transport hub in the Balkan Region.
-
B.
Turgut
Turgut is a masculine Turkish given name most notably borne by the influential modernist poet Turgut Uyar.
-
C.
Astsubay Başçavuş
Astsubay Başçavuş is a senior non-commissioned officer rank in the Turkish Armed Forces, typically held by experienced enlisted leaders responsible for unit-level management and training.
-
D.
Bamsi Beyrek
Bamsi Beyrek is a legendary hero of the Oghuz Turkic epic tradition, celebrated for his bravery, loyalty, and romantic exploits in the Book of Dede Korkut.
-
E.
Celal
Celal is a central character in Orhan Pamuk’s novel "The Black Book," around whom much of the story’s mystery and identity exploration revolves.
- 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532c535908190bdc90c58fc5bdaf7 |
completed | April 19, 2026, 7:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a047143903c8190979d96780689a068 |
completed | May 13, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_6a0486327b6c8190ac089fcc834582ff |
completed | May 13, 2026, 2:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0486d31e2c81908ca95db7baec64a0 |
completed | May 13, 2026, 2:12 p.m. |
Created at: April 10, 2026, 11:36 a.m.