Triple
T7510301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sooner Schooner |
E177500
|
entity |
| Predicate | safetyChangeAfter |
P33946
|
FINISHED |
| Object | 2019 tip-over incident |
—
|
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: 2019 tip-over incident | Statement: [Sooner Schooner, safetyChangeAfter, 2019 tip-over incident]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyChangeAfter Context triple: [Sooner Schooner, safetyChangeAfter, 2019 tip-over incident]
-
A.
safetyChangesImplemented
chosen
Indicates that specific safety-related modifications or measures have been put into effect.
-
B.
securityChangesAfter
Indicates that the security status or configuration of one entity is modified as a consequence of a prior event or change in another entity.
-
C.
safetyRegulationChange
Indicates a modification, update, or revision to existing safety regulations governing how something must be designed, operated, or managed.
-
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.
safetyPoints
Indicates a relationship where an entity is assigned or associated with a measure of safety, typically quantified as points reflecting its safety level or performance.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5d1e1a4819090bf4cbacdbdc7d0 |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:45 p.m.