Triple
T8660175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Louisiana System |
E205526
|
entity |
| Predicate | hasMemberInstitutionCount |
P276
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [University of Louisiana System, hasMemberInstitutionCount, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMemberInstitutionCount Context triple: [University of Louisiana System, hasMemberInstitutionCount, 9]
-
A.
hasNumberOfMemberInstitutions
chosen
Indicates the quantitative count of member institutions associated with a given entity.
-
B.
hasMemberOrganizationsIn
Indicates that an entity includes or comprises member organizations that are located in or associated with a specified place or region.
-
C.
memberAssociationCount
Indicates the number of associations or group memberships linked to a given member.
-
D.
hasInstitutions
Indicates that one entity possesses, contains, or is associated with one or more institutions.
-
E.
numberOfMemberOrganizations
Indicates the total count of organizations that are members of a given group, association, or umbrella entity.
- 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48701c748190a5f7bd9e2da0e5e9 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.