Triple
T6512293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stirling Moss |
E150163
|
entity |
| Predicate | numberOfF1GrandsPrixWins |
P52118
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Stirling Moss, numberOfF1GrandsPrixWins, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfF1GrandsPrixWins Context triple: [Stirling Moss, numberOfF1GrandsPrixWins, 16]
-
A.
totalFormulaOneWins
chosen
Indicates the total number of Formula One race victories achieved by a given driver, team, or other relevant entity.
-
B.
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).
-
C.
totalPolePositions
Indicates the total number of times an entity has achieved pole position in qualifying or starting order across all relevant events.
-
D.
totalFormulaOnePodiums
Indicates the total number of times an entity has finished on the podium (top three positions) in Formula One races.
-
E.
numberOfOpenChampionshipsWon
Indicates the total count of Open Championships that 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f3c5eb88190a56723acd8096dd8 |
completed | March 27, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69c68ab98c78819081743e614df04e1d |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:44 p.m.