Triple
T37417993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2018 United States Senate election in Texas |
E929768
|
entity |
| Predicate | popularVoteForBetoORourke |
P73183
|
FINISHED |
| Object | 4080407 |
—
|
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: 4080407 | Statement: [2018 United States Senate election in Texas, popularVoteForBetoORourke, 4080407]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: popularVoteForBetoORourke Context triple: [2018 United States Senate election in Texas, popularVoteForBetoORourke, 4080407]
-
A.
popularVoteOpponent
Indicates that one entity is the opponent of another in a popular vote or general election contest.
-
B.
popularVoteWinner
Indicates that the subject is the candidate who received the highest number of individual votes cast by the electorate in an election.
-
C.
popularVoteParty1
Indicates that the first party in an election received a specified share or count of the popular vote.
-
D.
popularVoteOutcome
Indicates the result of a popular vote, specifying which option or candidate received the majority or winning share of votes.
-
E.
popularVoteCountOpponent
chosen
Indicates the number of votes received by the opposing candidate or party in a popular vote contest.
- 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_69f76ebde49481908566cd96b37ccc84 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdb04ed81c8190b8feea90c1c785a6 |
completed | May 8, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69fda9d6c5148190a63205b6d9b0a1b4 |
completed | May 8, 2026, 9:16 a.m. |
Created at: May 3, 2026, 4:16 p.m.