Triple
T605377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Wisconsin–Madison |
E11582
|
entity |
| Predicate | administrativeStaffApprox |
P803
|
FINISHED |
| Object | 10000+ |
—
|
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: 10000+ | Statement: [University of Wisconsin–Madison, administrativeStaffApprox, 10000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeStaffApprox Context triple: [University of Wisconsin–Madison, administrativeStaffApprox, 10000+]
-
A.
personnelComposition
Indicates the makeup or distribution of people or roles within a group, organization, or unit.
-
B.
employedApproximately
chosen
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
-
C.
numberOfOffices
Indicates the total count of offices associated with a given entity.
-
D.
administeredBy
Indicates that an action, service, or process is carried out, managed, or overseen by a specified agent or authority.
-
E.
numberOfBoardMembers
Indicates the total count of individuals who serve as members on a board.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49df34abc8190a578c8c2ab3d28e4 |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cf8fc1c81908a9c7df552aa1a59 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.