Triple
T4994738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Dunlop |
E112216
|
entity |
| Predicate | riskAssociatedWithProfession |
P15871
|
FINISHED |
| Object | high-risk motorsport |
—
|
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: high-risk motorsport | Statement: [Michael Dunlop, riskAssociatedWithProfession, high-risk motorsport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riskAssociatedWithProfession Context triple: [Michael Dunlop, riskAssociatedWithProfession, high-risk motorsport]
-
A.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
B.
careerSafeties
Indicates the total number of safeties a player has recorded over the course of their career.
-
C.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
D.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
E.
riskType
chosen
Indicates the category or nature of risk associated with an entity, event, or relationship.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:34 p.m.