Triple
T6512297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stirling Moss |
E150163
|
entity |
| Predicate | bestF1ChampionshipPositionYear |
P8063
|
FINISHED |
| Object | 1955 |
—
|
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: 1955 | Statement: [Stirling Moss, bestF1ChampionshipPositionYear, 1955]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestF1ChampionshipPositionYear Context triple: [Stirling Moss, bestF1ChampionshipPositionYear, 1955]
-
A.
F1LapRecordYear
Indicates the year in which a specific Formula 1 lap record was set.
-
B.
totalFormulaOnePodiums
Indicates the total number of times an entity has finished on the podium (top three positions) in Formula One races.
-
C.
F1LapRecordHolder
Indicates that the subject holds the fastest lap record in a Formula 1 race or at a specific Formula 1 circuit.
-
D.
yearOfBestOpenFinish
chosen
Indicates the year in which an entity achieved its best (highest) finishing position in an Open tournament or championship.
-
E.
finalLeaguePosition
Indicates the finishing rank or place an entity achieved in a league or season-long competition.
- 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.