Triple
T13578607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz W196 |
E324349
|
entity |
| Predicate | totalWorldChampionshipGrandsPrixWins |
P52118
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Mercedes-Benz W196, totalWorldChampionshipGrandsPrixWins, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalWorldChampionshipGrandsPrixWins Context triple: [Mercedes-Benz W196, totalWorldChampionshipGrandsPrixWins, 9]
-
A.
grandPrixRaceWins
Indicates the number of Grand Prix races that an entity has won.
-
B.
grandPrixNumberInHistory
Indicates the ordinal position of a particular Grand Prix within the overall historical sequence of all Grand Prix events.
-
C.
totalFormulaOneWins
chosen
Indicates the total number of Formula One race victories achieved by a given driver, team, or other relevant entity.
-
D.
numberOfGrandTourOverallVictories
Indicates the total count of times an entity has won the overall classification in any of cycling’s Grand Tours (Tour de France, Giro d’Italia, or Vuelta a España).
-
E.
totalPolePositions
Indicates the total number of times an entity has achieved pole position in qualifying or starting order across all relevant events.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02de1988190af2d473973ecd529 |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.