Triple
T19232293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mondial de l’Automobile 1984 |
E480899
|
entity |
| Predicate | frequencyOfParentEvent |
P53048
|
FINISHED |
| Object | biennial |
—
|
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: biennial | Statement: [Mondial de l’Automobile 1984, frequencyOfParentEvent, biennial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyOfParentEvent Context triple: [Mondial de l’Automobile 1984, frequencyOfParentEvent, biennial]
-
A.
hasEventFrequency
Indicates how often a particular event occurs within a given time period.
-
B.
parentEvent
Indicates that one event serves as the higher-level or originating event from which another event is derived, contained, or logically dependent.
-
C.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
-
D.
frequencyDependsOn
Indicates that the frequency of one event, action, or state is determined or influenced by another factor or condition.
-
E.
frequencyClass
chosen
Indicates how often an event, action, or relation occurs, typically by assigning it to a predefined frequency category or class.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa9db56081908f50d318d7fc9eaa |
completed | April 20, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_69e4dcfae6f081909cc173cf71a5005c |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:25 p.m.