Triple
T22657529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Şenol Güneş |
E559267
|
entity |
| Predicate | managedTeam |
P3234
|
FINISHED |
| Object | FC Seoul |
—
|
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: FC Seoul | Statement: [Şenol Güneş, managedTeam, FC Seoul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FC Seoul Context triple: [Şenol Güneş, managedTeam, FC Seoul]
-
A.
FC Seoul
chosen
FC Seoul is a professional South Korean football club based in Seoul that competes in the K League 1 and is regarded as one of the country's most successful and popular teams.
-
B.
Suwon FC
Suwon FC is a professional South Korean football club based in the city of Suwon that competes in the K League.
-
C.
Seongnam FC
Seongnam FC is a professional South Korean football club based in Seongnam, known as one of the K League’s historically most successful teams.
-
D.
Daegu FC
Daegu FC is a professional South Korean football club based in Daegu that competes in the K League.
-
E.
Incheon United FC
Incheon United FC is a professional South Korean football club based in the city of Incheon that competes in the K League.
- 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.