Triple
T35752382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 Spanish Grand Prix |
E1033350
|
entity |
| Predicate | teamInvolvedInCollision |
P32122
|
FINISHED |
| Object | Mercedes |
—
|
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: Mercedes | Statement: [2016 Spanish Grand Prix, teamInvolvedInCollision, Mercedes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamInvolvedInCollision Context triple: [2016 Spanish Grand Prix, teamInvolvedInCollision, Mercedes]
-
A.
playerInvolved
Indicates that a specific player participates in, is associated with, or takes part in a particular event, action, or situation.
-
B.
crewInvolvedIn
Indicates that a crew (as a group or unit) participates in, contributes to, or is otherwise involved in a specified event, activity, or operation.
-
C.
involvedInAccident
chosen
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
D.
coachInvolved
Indicates that a coach participates in, influences, or is actively engaged in a particular event, activity, or interaction involving others.
-
E.
consequenceOfCollision
Indicates that one event, state, or condition occurs as a direct result of a collision between entities.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a198e24881909cc292e420269a8c |
completed | May 3, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:06 p.m.