Triple
T8760700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasan Arat |
E208189
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Hasan Arat |
E208189
|
NE FINISHED |
How this triple was built (2 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: Hasan Arat | Statement: [Hasan Arat, name, Hasan Arat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hasan Arat Context triple: [Hasan Arat, name, Hasan Arat]
-
A.
Hasan Arat
chosen
Hasan Arat is a Turkish businessman and sports executive best known for leading the Istanbul-based football club Beşiktaş JK as its chairman.
-
B.
Selim Soydan
Selim Soydan is a former Turkish footballer and sports executive, known both for his career in Turkish football and his long marriage to acclaimed actress Hülya Koçyiğit.
-
C.
Vedat Dalokay
Vedat Dalokay was a prominent Turkish architect and politician best known internationally for designing Islamabad’s iconic Faisal Mosque.
-
D.
Ozan Tufan
Ozan Tufan is a Turkish professional footballer, primarily a midfielder, who has played for clubs such as Bursaspor and Fenerbahçe as well as the Turkey national team.
-
E.
Feridun Zaimoglu
Feridun Zaimoglu is a German-Turkish author and artist known for his influential novels, essays, and plays that explore migration, identity, and multicultural life in Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca835df7e08190ac875664cca8f9ca |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5df9729481908679151988b76d2f |
completed | March 31, 2026, 11:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf434bcac08190be07175a26804185 |
completed | April 3, 2026, 4:34 a.m. |
Created at: March 30, 2026, 6:40 p.m.