Triple
T13290878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Formula One season |
E316555
|
entity |
| Predicate | regulationsType |
P14058
|
FINISHED |
| Object | technical regulations |
—
|
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: technical regulations | Statement: [2015 Formula One season, regulationsType, technical regulations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulationsType Context triple: [2015 Formula One season, regulationsType, technical regulations]
-
A.
regulatoryType
chosen
Indicates the specific kind or category of regulatory control, rule, or oversight that applies in the given relationship.
-
B.
worksOnRegulationType
Indicates that an entity is involved in work or activities related to a specific type or category of regulation.
-
C.
hasRegulations
Indicates that one entity imposes, contains, or is associated with rules or regulatory requirements that govern the behavior or operation of another entity.
-
D.
relatedRegulation
Indicates that there exists a regulatory rule, law, or directive that is associated with, governs, or is otherwise relevant to the referenced entity or activity.
-
E.
regulationAtIssue
Indicates that a specific regulation is the subject of concern, dispute, or analysis in the given context.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6893708190aeebf4c47386cff7 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:27 p.m.