Triple
T26573673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief Justice of the Wisconsin Supreme Court |
E666891
|
entity |
| Predicate | officeHoldersNumberAtATime |
P47665
|
FINISHED |
| Object | one |
—
|
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: one | Statement: [Chief Justice of the Wisconsin Supreme Court, officeHoldersNumberAtATime, one]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeHoldersNumberAtATime Context triple: [Chief Justice of the Wisconsin Supreme Court, officeHoldersNumberAtATime, one]
-
A.
officeHoldersNumber
Indicates the number of individuals who hold a particular office or position.
-
B.
officeHoldersNumberLimit
chosen
Indicates a constraint specifying the maximum number of individuals who may simultaneously hold a particular office or position.
-
C.
firstOfficeHoldersCount
Indicates the number of individuals who initially held a particular office or position.
-
D.
officeHoldersNumbered
Indicates that a specific office or position has its holders identified and distinguished by assigned numbers (e.g., first holder, second holder, etc.).
-
E.
officeHolderCountIncludes
Indicates that a specified count or total explicitly includes the number of individuals holding 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_69ee9cfa21c081909e4e36e087debfc6 |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: April 27, 2026, 1:59 a.m.