Triple
T8012165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central South University |
E186519
|
entity |
| Predicate | project211University |
P80563
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Central South University, project211University, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: project211University Context triple: [Central South University, project211University, true]
-
A.
university2
Indicates a relationship where an entity is a university associated with, attended by, or otherwise linked to another entity.
-
B.
campus2
Indicates that one entity is a secondary or additional campus location associated with another primary campus or institution.
-
C.
university
Indicates that an educational institution of higher learning is associated with or attended by a given entity.
-
D.
universitySectionOf
Indicates that one entity is a specific section, division, or part within a larger university.
-
E.
universityName
Indicates the official name associated with a particular university.
- 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_69ca82abaffc8190ab8af79cdbc31ab3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3d722fbc8190b22745b581421f16 |
completed | March 31, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69cb048c9f488190b4fb8917a9c21bc5 |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14bcbbc0819094a98e7ffffb7a40 |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:19 p.m.