Triple
T7614961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cem Karaca |
E172339
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Cem
Cem is a masculine given name of Turkish origin, commonly used in Turkey and among Turkish communities worldwide.
|
E676004
|
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: Cem | Statement: [Cem Karaca, givenName, Cem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cem Context triple: [Cem Karaca, givenName, Cem]
-
A.
Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
-
B.
Gazi
Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
-
C.
Gaziosmanpaşa
Gaziosmanpaşa is a densely populated residential and commercial district on the European side of Istanbul, known for its rapid urbanization and diverse working- and middle-class communities.
-
D.
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.
-
E.
Ziya
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
- 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: Cem Triple: [Cem Karaca, givenName, Cem]
Generated description
Cem is a masculine given name of Turkish origin, commonly used in Turkey and among Turkish communities worldwide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cem Target entity description: Cem is a masculine given name of Turkish origin, commonly used in Turkey and among Turkish communities worldwide.
-
A.
Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
-
B.
Gazi
Gazi is an honorific title in Turkey, historically bestowed for distinguished military valor and sacrifice in war.
-
C.
Gaziosmanpaşa
Gaziosmanpaşa is a densely populated residential and commercial district on the European side of Istanbul, known for its rapid urbanization and diverse working- and middle-class communities.
-
D.
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.
-
E.
Ziya
Ziya is a masculine given name of Turkish origin, historically associated with notable figures such as sociologist and nationalist thinker Ziya Gökalp.
- 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_69c6994f50808190ba228764bb422417 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fa4392e881908ed1ab3f64b41600 |
completed | March 27, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8686d16808190bc431c43c0928f6e |
completed | March 28, 2026, 11:46 p.m. |
| NEDg | Description generation | batch_69c8691bf25881909585bb04404f90da |
completed | March 28, 2026, 11:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8698f70a081909633b3b6d7fd45e1 |
completed | March 28, 2026, 11:51 p.m. |
Created at: March 27, 2026, 3:55 p.m.