Triple
T4599295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juan Manuel Fangio |
E100282
|
entity |
| Predicate | championshipWinRate |
P57363
|
FINISHED |
| Object | 5 titles in 8 full seasons |
—
|
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: 5 titles in 8 full seasons | Statement: [Juan Manuel Fangio, championshipWinRate, 5 titles in 8 full seasons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championshipWinRate Context triple: [Juan Manuel Fangio, championshipWinRate, 5 titles in 8 full seasons]
-
A.
allTimeWinningPercentage
Indicates the proportion of contests or games an entity has won over its entire recorded history, typically expressed as a percentage.
-
B.
championRegularSeasonWins
Indicates that an entity is the champion based on having the highest number of regular season wins.
-
C.
seasonWins
Indicates the number of games or competitions a team or individual has won during a specific season.
-
D.
surfaceWinRateLeader
Indicates that the subject has the highest win rate among peers on a specific playing surface.
-
E.
mostOverallWinsRecord
Indicates that the subject holds the record for having the greatest total number of wins compared to all others in the relevant context.
- F. None of above. chosen
Provenance (4 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_69bd43cbc014819098b45f435908f88a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd59707f8c8190b8368f1de887ff2a |
completed | March 20, 2026, 2:28 p.m. |
| PD | Predicate disambiguation | batch_69bd522c811c81909aae4feadae33174 |
completed | March 20, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69bd56b5f4648190834eafa666d53caa |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:11 p.m.