Triple
T23912756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stirling Craufurd Moss |
E601987
|
entity |
| Predicate | numberOfTimesF1RunnerUp |
P154350
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Stirling Craufurd Moss, numberOfTimesF1RunnerUp, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTimesF1RunnerUp Context triple: [Stirling Craufurd Moss, numberOfTimesF1RunnerUp, 4]
-
A.
numberOfF1WorldChampionships
Indicates the number of Formula 1 World Championship titles that an entity has won.
-
B.
totalFormulaOnePodiums
Indicates the total number of times an entity has finished on the podium (top three positions) in Formula One races.
-
C.
gamesWonByRunnerUp
Indicates the number of games won by the runner-up in a competition or match.
-
D.
achievedPolePositionAt
Indicates that an entity secured the top starting position (pole position) at a specified event or location.
-
E.
EuropeanChampionshipRunnerUpIn
Indicates that an entity finished in second place in a specified European Championship competition or edition.
- 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_69e2953a187081908346a9f36e85fc98 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ce96c47881908ccb17ef9f750676 |
completed | April 29, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69f16151ebdc819086e9e1d7cc1f4f3c |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f16e348b548190b76e50f9b611f76d |
completed | April 29, 2026, 2:34 a.m. |
Created at: April 17, 2026, 8:39 p.m.