Triple
T8523069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Truro Campus |
E201740
|
entity |
| Predicate | primaryDisciplineArea |
P43754
|
FINISHED |
| Object | medicine and health |
—
|
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: medicine and health | Statement: [Truro Campus, primaryDisciplineArea, medicine and health]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryDisciplineArea Context triple: [Truro Campus, primaryDisciplineArea, medicine and health]
-
A.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
B.
regionOfAcademicFocus
chosen
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
-
C.
secondaryDiscipline
Indicates that an entity has an additional, subordinate field of study or area of specialization associated with it, distinct from its primary discipline.
-
D.
dimensionOfStudy
Indicates the specific field, aspect, or perspective that characterizes or structures a particular study or research activity.
-
E.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe64215408190b45f462a32d3471d |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:16 p.m.