Triple
T26203653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2017 New Jersey gubernatorial election |
E655301
|
entity |
| Predicate | lieutenantGovernorBeforeElection |
P6364
|
FINISHED |
| Object | Kim Guadagno |
—
|
NE NERFINISHED |
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: Kim Guadagno | Statement: [2017 New Jersey gubernatorial election, lieutenantGovernorBeforeElection, Kim Guadagno]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lieutenantGovernorBeforeElection Context triple: [2017 New Jersey gubernatorial election, lieutenantGovernorBeforeElection, Kim Guadagno]
-
A.
vicePresidentBeforeElection
Indicates that an entity served as vice president prior to a specified election.
-
B.
incumbentBeforeElection
chosen
Indicates that the subject was already holding the relevant office or position prior to the specified election.
-
C.
lieutenantGovernorElected
Indicates that an individual attains the position of lieutenant governor through an electoral process.
-
D.
governorAfterElection
Indicates that one entity serves as the governor of a region or jurisdiction following a specified election event.
-
E.
precededInOfficeAsGovernorBy
Indicates that one individual assumed the role of governor after another specific individual, who held the office immediately before them.
- 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_69ee5b48236c81908fe385b6afc4f60b |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f60cdd664c8190862fe9543ab251c9 |
completed | May 2, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69f5f7fd90fc81909055b211368f9139 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 8:49 p.m.