Triple
T4283563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Wisconsin System |
E97211
|
entity |
| Predicate | hasCampusCount |
P1684
|
FINISHED |
| Object | multiple campuses across Wisconsin |
—
|
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: multiple campuses across Wisconsin | Statement: [University of Wisconsin System, hasCampusCount, multiple campuses across Wisconsin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCampusCount Context triple: [University of Wisconsin System, hasCampusCount, multiple campuses across Wisconsin]
-
A.
numberOfCampuses
chosen
Indicates the total count of campuses associated with a given entity.
-
B.
hasCampusOn
Indicates that an institution or organization maintains a campus located on a specified geographic area or site.
-
C.
hasCampusCity
Indicates that an educational institution or campus is located in a particular city.
-
D.
hasAdditionalCampus
Indicates that an educational institution maintains one or more campuses in addition to its primary or main campus.
-
E.
hasCollegeCampus
Indicates that an institution or organization possesses or is associated with a specific college campus as a physical or organizational site.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3503a84548190989a96d1a30d6ef7 |
completed | March 12, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69b347fc4c0c8190a7fcd814e27308a5 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:07 p.m.