Triple
T65337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U4 Network |
E1300
|
entity |
| Predicate | hasDisciplineCoverage |
P2902
|
FINISHED |
| Object | multiple academic disciplines |
—
|
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: multiple academic disciplines | Statement: [U4 Network, hasDisciplineCoverage, multiple academic disciplines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDisciplineCoverage Context triple: [U4 Network, hasDisciplineCoverage, multiple academic disciplines]
-
A.
supportsDiscipline
Indicates that one entity provides assistance, resources, or endorsement that helps sustain or advance a particular discipline.
-
B.
offersDiscipline
chosen
Indicates that one entity provides or makes available a particular field of study, training, or area of specialization to another entity.
-
C.
associatedWithDiscipline
Indicates that an entity has a relevant connection or involvement with a particular academic, professional, or thematic discipline.
-
D.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
hasClericalDiscipline
Indicates that an entity is subject to, or governed by, a particular set of clerical or religious disciplinary rules or practices.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2516eda54819090f5c14384d4eab1 |
completed | Feb. 28, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69a24ea5c140819080409a968c8d2ce8 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.