Triple
T17654448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circuit Gilles-Villeneuve |
E429583
|
entity |
| Predicate | brakingCharacteristic |
P109345
|
FINISHED |
| Object | heavy braking zones |
—
|
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: heavy braking zones | Statement: [Circuit Gilles-Villeneuve, brakingCharacteristic, heavy braking zones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brakingCharacteristic Context triple: [Circuit Gilles-Villeneuve, brakingCharacteristic, heavy braking zones]
-
A.
brakeFeature
chosen
Indicates that an entity possesses or is equipped with a particular braking-related feature or capability.
-
B.
hasBraking
Indicates that an entity possesses or is equipped with a braking capability or braking system.
-
C.
brakeType
Indicates the specific kind or system of brakes associated with an entity.
-
D.
brakingTestsPerformed
Indicates that one entity has carried out braking tests on another entity or system.
-
E.
brakeDemand
Indicates that an entity is requesting or applying a braking action, specifying the needed braking force or intensity.
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46e3fc6e8819080098a3cd0183811 |
completed | April 19, 2026, 5:55 a.m. |
| PD | Predicate disambiguation | batch_69e3cddc87188190ac2f049b86038676 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 6:05 a.m.