Triple
T18156240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circuit de Monaco |
E434637
|
entity |
| Predicate | firstUsedForFormulaOneWorldChampionship |
P130664
|
FINISHED |
| Object | 1950 |
—
|
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: 1950 | Statement: [Circuit de Monaco, firstUsedForFormulaOneWorldChampionship, 1950]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstUsedForFormulaOneWorldChampionship Context triple: [Circuit de Monaco, firstUsedForFormulaOneWorldChampionship, 1950]
-
A.
grandPrixDebutYear
Indicates the year in which an entity first participated in a Grand Prix event.
-
B.
firstFormulaOneWin
Indicates that the subject achieved their first victory in a Formula One race in relation to the specified event or context.
-
C.
firstF1Season
Indicates the Formula 1 season in which an entity (typically a driver or team) first competed.
-
D.
ageAtFirstFormulaOneWin
Indicates the age a person was when they achieved their first Formula One race victory.
-
E.
ageAtF1Debut
Indicates the age a driver was when they made their debut in Formula 1.
- 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_69d8b90aac308190801e2c57d8c5bfe5 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4debe27a88190bd76c6f78fcf1bd1 |
completed | April 19, 2026, 1:55 p.m. |
| PD | Predicate disambiguation | batch_69e43317d11c81908d1dc14921566b47 |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f5ae2c8190b11dee46534fa5a9 |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:30 a.m.