Triple
T33045381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1970 Dutch Grand Prix |
E845579
|
entity |
| Predicate | fatalAccidentDriver |
P175736
|
FINISHED |
| Object | Piers Courage |
—
|
NE NERFINISHED |
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: Piers Courage | Statement: [1970 Dutch Grand Prix, fatalAccidentDriver, Piers Courage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fatalAccidentDriver Context triple: [1970 Dutch Grand Prix, fatalAccidentDriver, Piers Courage]
-
A.
fatalAccident
Indicates that an accident resulted in at least one death.
-
B.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
C.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
D.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
E.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
- 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_69f3495242e48190996a2cb2beab5455 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d27120988190aacec621cf2bf0e8 |
completed | May 3, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69f6d6a482fc8190b526291cd99b8696 |
completed | May 3, 2026, 5:01 a.m. |
Created at: May 1, 2026, 1:24 a.m.