Triple
T2400004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chancellor of the California State University |
E47743
|
entity |
| Predicate | numberOfCampusesOverseen |
P1684
|
FINISHED |
| Object | 23 |
—
|
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: 23 | Statement: [Chancellor of the California State University, numberOfCampusesOverseen, 23]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCampusesOverseen Context triple: [Chancellor of the California State University, numberOfCampusesOverseen, 23]
-
A.
numberOfCampuses
chosen
Indicates the total count of campuses associated with a given entity.
-
B.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
-
C.
hasAdditionalCampus
Indicates that an educational institution maintains one or more campuses in addition to its primary or main campus.
-
D.
numberOfFaculties
Indicates the total count of faculties associated with a given entity.
-
E.
campusSize
Indicates the physical extent or scale of a campus, typically measured in area or capacity.
- 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_69a88a1c450c81909f61abb8b6863885 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc8c95d8c819088e4bb4fb32452ae |
completed | March 7, 2026, 6:42 a.m. |
| PD | Predicate disambiguation | batch_69abc5a3825c81909ec6111dfc165453 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:57 p.m.