Triple
T17202453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkan Şen |
E417510
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Şen
Şen is a Turkish surname borne by various notable individuals, including professional footballer Volkan Şen.
|
E1256158
|
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: Şen | Statement: [Volkan Şen, familyName, Şen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Şen Context triple: [Volkan Şen, familyName, Şen]
-
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.
Senyavin
Senyavin is a Russian surname most notably associated with a family of naval officers and admirals in the Imperial Russian Navy.
-
C.
Siverek
Siverek is a large district and town in southeastern Turkey known for its predominantly Kurdish population and its location within the historical region of Upper Mesopotamia.
-
D.
Gülveren
Gülveren is a neighborhood located within the Altındağ district of Ankara, Turkey.
-
E.
Oğuzeli
Oğuzeli is a town and district in Gaziantep Province in southeastern Turkey, known for its proximity to Gaziantep Oğuzeli International Airport and its role in the region’s agricultural and local trade activities.
- 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: Şen Triple: [Volkan Şen, familyName, Şen]
Generated description
Şen is a Turkish surname borne by various notable individuals, including professional footballer Volkan Şen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Şen Target entity description: Şen is a Turkish surname borne by various notable individuals, including professional footballer Volkan Şen.
-
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.
Senyavin
Senyavin is a Russian surname most notably associated with a family of naval officers and admirals in the Imperial Russian Navy.
-
C.
Siverek
Siverek is a large district and town in southeastern Turkey known for its predominantly Kurdish population and its location within the historical region of Upper Mesopotamia.
-
D.
Gülveren
Gülveren is a neighborhood located within the Altındağ district of Ankara, Turkey.
-
E.
Oğuzeli
Oğuzeli is a town and district in Gaziantep Province in southeastern Turkey, known for its proximity to Gaziantep Oğuzeli International Airport and its role in the region’s agricultural and local trade activities.
- 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_69d886d6ba8c819093215917b3d01689 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42db014b08190b88a5001e9f7811b |
completed | April 19, 2026, 1:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fdc13d88190bbf9e6d1272814d2 |
completed | May 11, 2026, 4:49 a.m. |
| NEDg | Description generation | batch_6a01614239bc819082691853905595ba |
completed | May 11, 2026, 4:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0161b8569c819088f0fdd673bb2d03 |
completed | May 11, 2026, 4:57 a.m. |
Created at: April 10, 2026, 5:38 a.m.