Triple
T9556540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abdüllatif Şener |
E230553
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Şener
Şener is a Turkish surname borne by various notable figures in Turkish politics, arts, and public life.
|
E806763
|
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: Şener | Statement: [Abdüllatif Şener, familyName, Şener]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Şener Context triple: [Abdüllatif Şener, familyName, Şener]
-
A.
Seyhun
Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
-
B.
Sezer
Sezer is a Turkish surname most prominently associated with Ahmet Necdet Sezer, the 10th President of Turkey.
-
C.
Dursunbey
Dursunbey is a town and district in western Turkey known for its forestry, timber production, and rural character within Balıkesir Province.
-
D.
Gündoğmuş
Gündoğmuş is a small inland district and town in Turkey known for its mountainous terrain and location within Antalya Province in the Mediterranean region.
-
E.
Şahinbey
Şahinbey is a central district and municipality of Gaziantep in southeastern Turkey, known as a major urban and commercial area of the city.
- 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: Şener Triple: [Abdüllatif Şener, familyName, Şener]
Generated description
Şener is a Turkish surname borne by various notable figures in Turkish politics, arts, and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Şener Target entity description: Şener is a Turkish surname borne by various notable figures in Turkish politics, arts, and public life.
-
A.
Seyhun
Seyhun is the historical name used in Islamic and Central Asian sources for the Syr Darya River, one of the major rivers of Central Asia.
-
B.
Sezer
Sezer is a Turkish surname most prominently associated with Ahmet Necdet Sezer, the 10th President of Turkey.
-
C.
Dursunbey
Dursunbey is a town and district in western Turkey known for its forestry, timber production, and rural character within Balıkesir Province.
-
D.
Gündoğmuş
Gündoğmuş is a small inland district and town in Turkey known for its mountainous terrain and location within Antalya Province in the Mediterranean region.
-
E.
Şahinbey
Şahinbey is a central district and municipality of Gaziantep in southeastern Turkey, known as a major urban and commercial area of the city.
- 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9946c7b8819082f3a4ec4fc979e6 |
completed | April 1, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d152913df0819089ec9656dcdc9fa1 |
completed | April 4, 2026, 6:04 p.m. |
| NEDg | Description generation | batch_69d153c26b8481908298b32918666562 |
completed | April 4, 2026, 6:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d15445e7a88190accd19c53a6170bb |
completed | April 4, 2026, 6:11 p.m. |
Created at: March 30, 2026, 8:03 p.m.