Triple
T18078921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pablo Batalla |
E432632
|
entity |
| Predicate | club |
P8194
|
FINISHED |
| Object | Bursaspor |
—
|
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: Bursaspor | Statement: [Pablo Batalla, club, Bursaspor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bursaspor Context triple: [Pablo Batalla, club, Bursaspor]
-
A.
Bursaspor
chosen
Bursaspor is a professional Turkish sports club best known for its football team, which has competed in the country’s top leagues and is based in the city of Bursa.
-
B.
Boluspor
Boluspor is a Turkish professional football club based in the city of Bolu that competes in the country’s football league system.
-
C.
Kayserispor
Kayserispor is a professional Turkish football club based in Kayseri that competes in the country’s top leagues.
-
D.
Denizlispor
Denizlispor is a professional Turkish football club based in the city of Denizli that competes in the national league system.
-
E.
Alanyaspor
Alanyaspor is a professional Turkish football club based in Alanya that competes in the country’s top leagues and is known for its regional rivalry with Antalyaspor.
- 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4d9f6a85481909894c39c8be98d5d |
completed | April 19, 2026, 1:34 p.m. |
Created at: April 10, 2026, 10:27 a.m.