Triple
T22657531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şenol Güneş |
E559267
|
entity |
| Predicate | managedTeam |
P3234
|
FINISHED |
| Object | Sakaryaspor |
—
|
NE NERFINISHED |
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: Sakaryaspor | Statement: [Şenol Güneş, managedTeam, Sakaryaspor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakaryaspor Context triple: [Şenol Güneş, managedTeam, Sakaryaspor]
-
A.
Sakaryaspor
chosen
Sakaryaspor is a Turkish professional football club known for developing notable talents such as legendary striker Hakan Şükür.
-
B.
Konyaspor
Konyaspor is a professional Turkish football club based in Konya that competes in the country’s top leagues and has a passionate regional fan base.
-
C.
Kocaelispor
Kocaelispor is a Turkish professional football club based in İzmit, known for its passionate fan base and regional rivalries in the Marmara region.
-
D.
Kayserispor
Kayserispor is a professional Turkish football club based in Kayseri that competes in the country’s top leagues.
-
E.
Boluspor
Boluspor is a Turkish professional football club based in the city of Bolu that competes in the country’s football league system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765c62bc8190b3fcde76d6b6dfb6 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:06 p.m.