Triple
T33055591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mitch Landrieu |
E845836
|
entity |
| Predicate | lieutenantGovernorOf |
P209
|
FINISHED |
| Object | Louisiana |
—
|
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: Louisiana | Statement: [Mitch Landrieu, lieutenantGovernorOf, Louisiana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lieutenantGovernorOf Context triple: [Mitch Landrieu, lieutenantGovernorOf, Louisiana]
-
A.
hasLieutenantGovernor
chosen
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
B.
provinceGovernor
Indicates that one entity serves as the governor or chief administrative authority of a particular province in relation to the other entity.
-
C.
lieutenantGovernorElected
Indicates that an individual attains the position of lieutenant governor through an electoral process.
-
D.
governorateOf
Indicates that one entity is the governorate (administrative region) to which another entity belongs or is located within.
-
E.
firstLieutenantGovernor
Indicates that one entity serves as the first lieutenant governor (the inaugural holder of the lieutenant governor office) of another entity, typically a state or territory.
- 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_69f3495333b8819095e9af56855b9061 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d27120988190aacec621cf2bf0e8 |
completed | May 3, 2026, 4:43 a.m. |
Created at: May 1, 2026, 1:25 a.m.