Triple
T18797481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1957 Formula One season |
E459673
|
entity |
| Predicate | FangioTitleCount |
P118787
|
FINISHED |
| Object | 5 |
—
|
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 | Statement: [1957 Formula One season, FangioTitleCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FangioTitleCount Context triple: [1957 Formula One season, FangioTitleCount, 5]
-
A.
numberOfF1WorldChampionships
chosen
Indicates the number of Formula 1 World Championship titles that an entity has won.
-
B.
numberOfSerieATitles
Indicates the number of Serie A championship titles that an entity has won.
-
C.
totalPolePositions
Indicates the total number of times an entity has achieved pole position in qualifying or starting order across all relevant events.
-
D.
numberOfFormulaOneTTWorldTitles
Indicates the count of Formula One TT World Championship titles that an entity has won.
-
E.
totalFormulaOneWins
Indicates the total number of Formula One race victories achieved by a given driver, team, or other relevant entity.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a020821881909749f6a1c6cd195b |
completed | April 20, 2026, 3:40 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:53 a.m.