Triple
T1466971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French plebiscite of 1802 |
E27045
|
entity |
| Predicate | officialYesPercentage |
P3019
|
FINISHED |
| Object | about 99.76% |
—
|
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: about 99.76% | Statement: [French plebiscite of 1802, officialYesPercentage, about 99.76%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officialYesPercentage Context triple: [French plebiscite of 1802, officialYesPercentage, about 99.76%]
-
A.
majorityStatusInCountry
Indicates that an entity holds majority status—such as being the largest or dominant group—within a specified country.
-
B.
voterTurnoutPercentage
Indicates the proportion of eligible or registered voters who actually cast a ballot in a given election, expressed as a percentage.
-
C.
officialProportion
chosen
Indicates the proportion or percentage of something as formally defined or reported by an official source or authority.
-
D.
referendumResultRemainPercentage
Indicates the percentage of votes in a referendum that were cast in favor of remaining (as opposed to leaving or changing the status quo).
-
E.
surveyedFor
Indicates that one entity has been examined, questioned, or assessed in order to gather information specifically about another entity or topic.
- 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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5bcfa0881909d6137c69825bc7a |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:01 p.m.