Triple

T440818
Position Surface form Disambiguated ID Type / Status
Subject 1936 United States presidential election E10108 entity
Predicate campaignSloganOfLoser P7699 FINISHED
Object Let’s make a change 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: Let’s make a change | Statement: [1936 United States presidential election, campaignSloganOfLoser, Let’s make a change]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: campaignSloganOfLoser
Context triple: [1936 United States presidential election, campaignSloganOfLoser, Let’s make a change]
  • A. campaignSloganOfWinner
    Indicates that a given slogan is the official campaign slogan used by the candidate who won a particular election.
  • B. electoralSlogan chosen
    Indicates that a phrase is used as a campaign message or motto to promote a candidate, party, or political cause in an election.
  • C. popularVoteLoser
    Indicates that the subject became the winner of an election despite receiving fewer popular votes than at least one opponent.
  • D. sloganUsedIn
    Indicates that a particular slogan is employed or featured within a specific context, such as a campaign, advertisement, or organization.
  • E. defeatedCandidate
    Indicates that one candidate has won an election or contest against another candidate, causing the other to lose.
  • 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef2af84881909635ebbbb3465b1b completed Feb. 28, 2026, 1:35 p.m.
PD Predicate disambiguation batch_69a2eddcf50c8190bfa0d1f8ee9f604a completed Feb. 28, 2026, 1:30 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.