Triple
T3380319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2017 Chilean presidential election |
E71165
|
entity |
| Predicate | secondRoundRunnerUpVoteSharePercentage |
P47100
|
FINISHED |
| Object | 45.42 |
—
|
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: 45.42 | Statement: [2017 Chilean presidential election, secondRoundRunnerUpVoteSharePercentage, 45.42]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondRoundRunnerUpVoteSharePercentage Context triple: [2017 Chilean presidential election, secondRoundRunnerUpVoteSharePercentage, 45.42]
-
A.
secondRoundWinner
Indicates that the subject is the winner of the second round in a multi-round competition, contest, or process.
-
B.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
C.
gamesWonByRunnerUp
Indicates the number of games won by the runner-up in a competition or match.
-
D.
popularVoteRunnerUp
Indicates that one entity is the candidate who received the second-highest number of votes in a popular vote for the other entity’s election or contest.
-
E.
regionOfRunnerUp
Indicates the geographic region associated with the runner-up in a competition or event.
- F. None of above. chosen
Provenance (4 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_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb5e7c7f48190afb78c311b424c93 |
completed | March 8, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69ada434bae48190a77ea37f9274ad8f |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada527ff308190813a7ffdcdec4322 |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:14 p.m.