Triple
T8338827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabahattin Eyüboğlu |
E195856
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Mualla Eyüboğlu
Mualla Eyüboğlu was a pioneering Turkish architect and one of the first prominent female figures in the field of architecture in Turkey.
|
E737001
|
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: Mualla Eyüboğlu | Statement: [Sabahattin Eyüboğlu, spouse, Mualla Eyüboğlu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mualla Eyüboğlu Context triple: [Sabahattin Eyüboğlu, spouse, Mualla Eyüboğlu]
-
A.
Tahsin Banguoğlu
Tahsin Banguoğlu was a Turkish linguist, academic, and politician known for his influential work on Turkish language and grammar.
-
B.
Mümtaz Turhan
Mümtaz Turhan was a prominent Turkish social psychologist and academic known for his influential work on cultural change, modernization, and education in Turkey.
-
C.
Niyazi Akdaş
Niyazi Akdaş is a Turkish football executive best known for serving as chairman of the Ankara-based club Gençlerbirliği S.K.
-
D.
Metin Feyzioğlu
Metin Feyzioğlu is a prominent Turkish lawyer, academic, and former president of the Union of Turkish Bar Associations.
-
E.
Nur Fettahoğlu
Nur Fettahoğlu is a Turkish-German actress best known for her role in the historical television series "Muhteşem Yüzyıl" ("Magnificent Century").
- 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: Mualla Eyüboğlu Triple: [Sabahattin Eyüboğlu, spouse, Mualla Eyüboğlu]
Generated description
Mualla Eyüboğlu was a pioneering Turkish architect and one of the first prominent female figures in the field of architecture in Turkey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mualla Eyüboğlu Target entity description: Mualla Eyüboğlu was a pioneering Turkish architect and one of the first prominent female figures in the field of architecture in Turkey.
-
A.
Tahsin Banguoğlu
Tahsin Banguoğlu was a Turkish linguist, academic, and politician known for his influential work on Turkish language and grammar.
-
B.
Mümtaz Turhan
Mümtaz Turhan was a prominent Turkish social psychologist and academic known for his influential work on cultural change, modernization, and education in Turkey.
-
C.
Niyazi Akdaş
Niyazi Akdaş is a Turkish football executive best known for serving as chairman of the Ankara-based club Gençlerbirliği S.K.
-
D.
Metin Feyzioğlu
Metin Feyzioğlu is a prominent Turkish lawyer, academic, and former president of the Union of Turkish Bar Associations.
-
E.
Nur Fettahoğlu
Nur Fettahoğlu is a Turkish-German actress best known for her role in the historical television series "Muhteşem Yüzyıl" ("Magnificent Century").
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fd68e348190a7cb8639a263b50f |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce394f782881908310d7ca1b7dc36c |
completed | April 2, 2026, 9:39 a.m. |
| NEDg | Description generation | batch_69ce3ad9a3a88190929a6c6ae7ee58cf |
completed | April 2, 2026, 9:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3cc54d888190ae86b787afc38f7b |
completed | April 2, 2026, 9:54 a.m. |
Created at: March 30, 2026, 5:57 p.m.