Triple
T34910984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Connecticut gubernatorial election |
E1006863
|
entity |
| Predicate | lieutenantGovernorWinnerTookOfficeOn |
P71683
|
FINISHED |
| Object | 2015-01-07 |
—
|
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: 2015-01-07 | Statement: [2014 Connecticut gubernatorial election, lieutenantGovernorWinnerTookOfficeOn, 2015-01-07]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lieutenantGovernorWinnerTookOfficeOn Context triple: [2014 Connecticut gubernatorial election, lieutenantGovernorWinnerTookOfficeOn, 2015-01-07]
-
A.
officeAssumedAsLieutenantGovernor
Indicates that an individual has formally taken on and begun serving in the role of lieutenant governor.
-
B.
lieutenantGovernorDuringTerm
Indicates that a person served as lieutenant governor of a jurisdiction during the specified officeholder’s term in a particular office.
-
C.
lieutenantGovernorElected
Indicates that an individual attains the position of lieutenant governor through an electoral process.
-
D.
startTimeAsLieutenantGovernor
chosen
Indicates the date and time at which an individual began serving in the role of lieutenant governor.
-
E.
assumedOfficeAsGovernor
Indicates that an individual formally took on and began serving in the role of governor of a specific jurisdiction.
- 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_69f76dc1b4a081909b4c6e4d8ec0aa2d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4 p.m.