Triple
T795149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMU |
E17001
|
entity |
| Predicate | academicStrength |
P10834
|
FINISHED |
| Object | creative industries |
—
|
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: creative industries | Statement: [MMU, academicStrength, creative industries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academicStrength Context triple: [MMU, academicStrength, creative industries]
-
A.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
-
C.
academicType
Indicates the specific academic category or classification associated with an entity (such as a work, program, or role).
-
D.
academicEmphasis
chosen
Indicates a focus or concentration of study or specialization within an academic program or curriculum.
-
E.
academicReputation
Indicates the perceived quality and standing of an entity within the academic community, based on factors like scholarly impact, prestige, and recognition.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7b04cd8819092e2fd7ba4df8672 |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a510f61881909175d6d8719246cd |
completed | March 1, 2026, 8:44 p.m. |
Created at: March 1, 2026, 7:38 p.m.