Triple
T424982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goodwin Knight |
E8186
|
entity |
| Predicate | officeLeftAsLieutenantGovernor |
P171
|
FINISHED |
| Object | 1953-10-05 |
—
|
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: 1953-10-05 | Statement: [Goodwin Knight, officeLeftAsLieutenantGovernor, 1953-10-05]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeLeftAsLieutenantGovernor Context triple: [Goodwin Knight, officeLeftAsLieutenantGovernor, 1953-10-05]
-
A.
hasLieutenantGovernor
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
B.
currentGovernor
Indicates that one entity is the person who presently holds the office of governor of the other entity.
-
C.
hadGovernor
Indicates that an administrative region or political entity was governed by a specific person who held the office of governor.
-
D.
electedOffice
Indicates that an entity holds or has held a particular office or position as a result of an election.
-
E.
leftOffice
chosen
Indicates that an entity ceased holding or performing the duties of a particular office or position.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eed56ab481909eec289075496260 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd6736c81909a6ca549f77b4345 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.