Triple
T3068700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu Dhabi Grand Prix |
E62166
|
entity |
| Predicate | safetyCar |
P25553
|
FINISHED |
| Object | Mercedes-AMG safety car (various models over the years) |
—
|
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: Mercedes-AMG safety car (various models over the years) | Statement: [Abu Dhabi Grand Prix, safetyCar, Mercedes-AMG safety car (various models over the years)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyCar Context triple: [Abu Dhabi Grand Prix, safetyCar, Mercedes-AMG safety car (various models over the years)]
-
A.
safetyCarPossible
Indicates that conditions are such that deploying a safety car is a valid or allowable option.
-
B.
safetyCarFrequency
Indicates how often a safety car is deployed or appears within a given context or time frame.
-
C.
safetyCarSupplier
chosen
Indicates that one entity serves as the provider or manufacturer of safety cars for another entity or event.
-
D.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
E.
safetyGoal
Indicates that an entity is associated with a specific safety objective or target condition intended to prevent harm or reduce risk.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0ffcc208190962cc9edcbf43c31 |
completed | March 8, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69ad9624b7a0819091d255614f5819ea |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:02 p.m.