Triple
T24766097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vuyani Bungu |
E619586
|
entity |
| Predicate | numberOfSuccessfulTitleDefences |
P33242
|
FINISHED |
| Object | 13 |
—
|
LITERAL 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: 13 | Statement: [Vuyani Bungu, numberOfSuccessfulTitleDefences, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSuccessfulTitleDefences Context triple: [Vuyani Bungu, numberOfSuccessfulTitleDefences, 13]
-
A.
numberOfTitleDefenses
chosen
Indicates the number of times an entity has successfully defended a previously won title or championship.
-
B.
numberOfChampionTitles
Indicates the total count of championship titles that an entity has won.
-
C.
defendingChampionRetainedTitle
Indicates that the current defending champion successfully kept their title in the subsequent competition or event.
-
D.
numberOfTimesWorldHeavyweightChampion
Indicates the number of times an entity has held the World Heavyweight Champion title.
-
E.
worldChampionshipTitles
Indicates the number of world championship titles an entity has won.
- F. None of above.
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_69e2fabbea94819092ed41348909622f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 18, 2026, 4:28 a.m.