Triple
T16205360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Governor of Alabama |
E393312
|
entity |
| Predicate | officeHoldersAlsoHeldOffice |
P42708
|
FINISHED |
| Object | Governor of Alabama |
—
|
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: Governor of Alabama | Statement: [Lieutenant Governor of Alabama, officeHoldersAlsoHeldOffice, Governor of Alabama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHoldersAlsoHeldOffice Context triple: [Lieutenant Governor of Alabama, officeHoldersAlsoHeldOffice, Governor of Alabama]
-
A.
officeHoldersAlsoHeldPosition
chosen
Indicates that an individual who holds one office or position has also held another specified office or position (at some time, possibly earlier or later).
-
B.
hasMemberWhoHeldOffice
Indicates that a group or organization includes at least one member who has held a specified office or position.
-
C.
hasListOfOfficeHolders
Indicates that an entity is associated with a collection or record enumerating the individuals who have held a particular office or position.
-
D.
hasHistoricalOfficeHolder
Indicates that an office, position, or role has been held by a specific person at some point in the past.
-
E.
officeHoldersParticipateIn
Indicates that individuals holding an office take part in or are involved in a specified event, activity, or process.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2270f047c819084645da27759a3d2 |
completed | April 17, 2026, 12:26 p.m. |
| PD | Predicate disambiguation | batch_69e219e11f6081909106b1240a17fd37 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.