Triple
T17542394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1998 Paris Motor Show |
E427229
|
entity |
| Predicate | frequencyOfEvent |
P29070
|
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: [1998 Paris Motor Show, frequencyOfEvent, biennial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyOfEvent Context triple: [1998 Paris Motor Show, frequencyOfEvent, biennial]
-
A.
hasEventFrequency
chosen
Indicates how often a particular event occurs within a given time period.
-
B.
frequencyContent
Indicates that one entity specifies or characterizes the rate or frequency with which the content or occurrence of another entity takes place.
-
C.
frequencyInHistory
Indicates how often a particular event, state, or relationship has occurred over time within a given historical context.
-
D.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
-
E.
frequencyClass
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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4545f1fa08190870c9244d06cf5f6 |
completed | April 19, 2026, 4:04 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fb39948190a82a597c5bac5c57 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:49 a.m.