Triple
T16510192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portuguese presidential election, 1980 |
E401039
|
entity |
| Predicate | winningPopularVote |
P9220
|
FINISHED |
| Object | 3,262,520 |
—
|
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: 3,262,520 | Statement: [Portuguese presidential election, 1980, winningPopularVote, 3,262,520]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winningPopularVote Context triple: [Portuguese presidential election, 1980, winningPopularVote, 3,262,520]
-
A.
popularVoteWinner
Indicates that the subject is the candidate who received the highest number of individual votes cast by the electorate in an election.
-
B.
popularVoteOutcome
Indicates the result of a popular vote, specifying which option or candidate received the majority or winning share of votes.
-
C.
smithPopularVote
Indicates that Smith received a specified number or share of votes in a popular vote election or ballot.
-
D.
popularVotes
chosen
Indicates the number of votes an entity (such as a candidate or option) receives directly from individual voters in an election or decision process.
-
E.
LabourPopularVote
Indicates the proportion of total votes cast in an election that were received by the Labour Party.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e54f7508190804bbae4c9bc8fe3 |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.