Triple
T12577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleni Kounalakis |
E253
|
entity |
| Predicate | startTimeAsLieutenantGovernorOfCalifornia |
P288
|
FINISHED |
| Object | 2019-01-07 |
—
|
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: 2019-01-07 | Statement: [Eleni Kounalakis, startTimeAsLieutenantGovernorOfCalifornia, 2019-01-07]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTimeAsLieutenantGovernorOfCalifornia Context triple: [Eleni Kounalakis, startTimeAsLieutenantGovernorOfCalifornia, 2019-01-07]
-
A.
inaugurationDay
Indicates the specific day on which a formal ceremony is held to induct someone into an official position or office.
-
B.
hasLieutenantGovernor
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
C.
firstInOfficeTo
Indicates that one entity was the earliest or first to hold a particular office or position in relation to another entity or context.
-
D.
startDate
chosen
Indicates the point in time when an event, state, or relationship begins.
-
E.
appointedBy
Indicates that one entity has been formally selected or assigned to a position, role, or office by another entity.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a243abb2ec8190937365e5ecec52ad |
completed | Feb. 28, 2026, 1:23 a.m. |
| PD | Predicate disambiguation | batch_69a23fe9470c8190918a6ca1df168646 |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.