Triple
T23849080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2018 United States Senate election in Florida |
E592106
|
entity |
| Predicate | recountType |
P140435
|
FINISHED |
| Object | machine recount |
—
|
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: machine recount | Statement: [2018 United States Senate election in Florida, recountType, machine recount]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recountType Context triple: [2018 United States Senate election in Florida, recountType, machine recount]
-
A.
recount
Indicates that an entity narrates or describes past events or experiences, often in a detailed or sequential manner, to another entity.
-
B.
recountOccurred
chosen
Indicates that an official recount of votes or tallies has taken place for a given election or decision.
-
C.
recordsType
Indicates that one entity documents, stores, or keeps an official account of a particular type or category of information, event, or item.
-
D.
retellsStoryOf
Indicates that one entity recounts or narrates the story originally told or experienced by another entity.
-
E.
recountLocation
Indicates that an entity narrates or describes events or experiences associated with a particular location.
- 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_69e25d221d908190b9b502ad31e66a3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c9862cb081908e2433678190dee8 |
completed | April 29, 2026, 9:04 a.m. |
| PD | Predicate disambiguation | batch_69f1614612b481908c45d99e588882f9 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:10 p.m.