Triple
T4366870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schools of the University of Cambridge |
E98796
|
entity |
| Predicate | hasNumberOfSchools |
P55803
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Schools of the University of Cambridge, hasNumberOfSchools, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfSchools Context triple: [Schools of the University of Cambridge, hasNumberOfSchools, 6]
-
A.
isLargeSchool
Indicates that a school has a large size, typically in terms of student population, campus area, or overall capacity.
-
B.
numberOfUniversities
Indicates the quantity of universities associated with a given entity.
-
C.
hasMajorEducationalInstitutions
Indicates that the subject possesses or hosts significant higher-level educational organizations or facilities, such as universities or major colleges.
-
D.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
E.
hasSchoolsAccess
Indicates that one entity has permission or the ability to access schools or school-related resources associated with another entity.
- F. None of above. chosen
Provenance (4 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35201be7081908808e81634060f95 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff654308190b9717526120d80d3 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:17 p.m.