Triple
T2061715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mehmet Akif Ersoy |
E45804
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mehmet
Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
|
E230556
|
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 | Statement: [Mehmet Akif Ersoy, givenName, Mehmet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mehmet Context triple: [Mehmet Akif Ersoy, givenName, Mehmet]
-
A.
Ahmet
Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
-
B.
Mustafa
Mustafa is the given birth name of Mustafa Kemal Atatürk, the founder and first president of the Republic of Turkey.
-
C.
Orhan
Orhan was the second ruler of the early Ottoman state who significantly expanded its territories in northwestern Anatolia and laid foundations for its future imperial structure.
-
D.
Eyüp
Eyüp is a historic district on Istanbul’s Golden Horn, known for its important Ottoman-era mosque complex and traditional neighborhoods.
-
E.
Kerim Bey
Kerim Bey is a charismatic and resourceful MI6 ally in the James Bond series, best known for assisting Bond in Istanbul in the film and novel "From Russia, with Love."
- 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 Triple: [Mehmet Akif Ersoy, givenName, Mehmet]
Generated description
Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mehmet Target entity description: Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
-
A.
Ahmet
Ahmet is a common male given name of Arabic origin, widely used in Turkey and other Muslim-majority countries as a variant of Ahmed.
-
B.
Mustafa
Mustafa is the given birth name of Mustafa Kemal Atatürk, the founder and first president of the Republic of Turkey.
-
C.
Orhan
Orhan was the second ruler of the early Ottoman state who significantly expanded its territories in northwestern Anatolia and laid foundations for its future imperial structure.
-
D.
Eyüp
Eyüp is a historic district on Istanbul’s Golden Horn, known for its important Ottoman-era mosque complex and traditional neighborhoods.
-
E.
Kerim Bey
Kerim Bey is a charismatic and resourceful MI6 ally in the James Bond series, best known for assisting Bond in Istanbul in the film and novel "From Russia, with Love."
- 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9d0ecf08190aec20338a6ba9911 |
completed | March 7, 2026, 5:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae271cb26c81908664f4c26f4fb5ea |
completed | March 9, 2026, 1:49 a.m. |
| NEDg | Description generation | batch_69ae28155ec8819097741dcfd3d81110 |
completed | March 9, 2026, 1:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae2889503c8190bc72e786c656edf3 |
completed | March 9, 2026, 1:55 a.m. |
Created at: March 4, 2026, 7:40 p.m.