Triple
T32424780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese Grand Prix |
E828547
|
entity |
| Predicate | safetyIncidentNotable |
P18677
|
FINISHED |
| Object | Jules Bianchi accident at Suzuka in 2014 |
—
|
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: Jules Bianchi accident at Suzuka in 2014 | Statement: [Japanese Grand Prix, safetyIncidentNotable, Jules Bianchi accident at Suzuka in 2014]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyIncidentNotable Context triple: [Japanese Grand Prix, safetyIncidentNotable, Jules Bianchi accident at Suzuka in 2014]
-
A.
notableSafety
Indicates that an entity is recognized for having significant safety characteristics, performance, or impact relative to others.
-
B.
notableProtectiveIncident
Indicates that a significant event occurred in which one entity protected or defended another in a notable or remarkable way.
-
C.
notableIncidentType
Indicates the specific category or kind of significant event or incident associated with an entity.
-
D.
hasNotableIncident
chosen
Indicates that an entity is associated with a significant or noteworthy event, occurrence, or incident.
-
E.
incidentWith
Indicates that one entity is involved in, affected by, or associated with a particular incident or event together with another entity.
- 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_69f3491b28bc8190b75cea7a507f337b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 12:54 a.m.