Triple
T6409395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Korean political system |
E127666
|
entity |
| Predicate | voterTurnoutClaimed |
P1234
|
FINISHED |
| Object | near 100 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: near 100 percent | Statement: [North Korean political system, voterTurnoutClaimed, near 100 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: voterTurnoutClaimed Context triple: [North Korean political system, voterTurnoutClaimed, near 100 percent]
-
A.
voterTurnoutPercentage
chosen
Indicates the proportion of eligible or registered voters who actually cast a ballot in a given election, expressed as a percentage.
-
B.
voterTurnoutChange
Indicates the amount or direction of change in voter turnout between two elections or time periods.
-
C.
voterTurnoutDescription
Indicates a textual explanation or characterization of the level, nature, or patterns of voter turnout in an election or voting event.
-
D.
numberOfVoters
Indicates the total count of individuals who participated in a particular vote or election.
-
E.
approximateNumberOfVotersBefore
Indicates that one value represents an estimated count of voters that existed prior to a specified point in time or event.
- 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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068cdf25881908d42a5d979637ad6 |
completed | March 22, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69c060f40ecc8190b1df17b96767675c |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:41 p.m.