Triple
T1625643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Power and Mechanical Engineering, Wuhan University |
E35134
|
entity |
| Predicate | typeOfCampusUnit |
P110
|
FINISHED |
| Object | school |
—
|
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: school | Statement: [School of Power and Mechanical Engineering, Wuhan University, typeOfCampusUnit, school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCampusUnit Context triple: [School of Power and Mechanical Engineering, Wuhan University, typeOfCampusUnit, school]
-
A.
campusType
chosen
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
B.
typeOfInstitution
Indicates the specific kind or category of institution that an entity belongs to or is classified as.
-
C.
campusName
Indicates the official name assigned to a particular campus.
-
D.
governsInstitutionType
Indicates that an entity has authoritative control or regulatory oversight over a particular type or category of institution.
-
E.
hasAcademicUnit
Indicates that an entity is associated with, or belongs to, a specific academic unit such as a department, school, or faculty within an educational institution.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9431af5ac8190893133f1ae490142 |
completed | March 5, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69a907c91c888190b6ed295c1a2e0977 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.