Triple
T8067318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saar status referendum of 1955 |
E188275
|
entity |
| Predicate | votesAgainstPercentage |
P66702
|
FINISHED |
| Object | approximately 67.7 percent |
—
|
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: approximately 67.7 percent | Statement: [Saar status referendum of 1955, votesAgainstPercentage, approximately 67.7 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: votesAgainstPercentage Context triple: [Saar status referendum of 1955, votesAgainstPercentage, approximately 67.7 percent]
-
A.
noVotesPercentage
chosen
Indicates the proportion of total votes that were cast as "no" in a given decision or election.
-
B.
voterTurnoutPercentage
Indicates the proportion of eligible or registered voters who actually cast a ballot in a given election, expressed as a percentage.
-
C.
oppositionPercentage
Indicates the proportion of entities or participants that are in opposition to a given proposal, action, or subject relative to the whole.
-
D.
voteCount
Indicates the number of votes that have been cast for or associated with a particular item or option.
-
E.
referendumYesPercentage
Indicates the percentage of votes cast in favor of the "yes" option in a referendum.
- 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_69ca82b42674819086840efea12478e5 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ff75d208190b7c53d2fe55878ac |
completed | March 31, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69cb049cd51c8190bb3b0f503e42fa8d |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:26 p.m.