Triple
T2498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Imperial University |
E46
|
entity |
| Predicate | typeOfInstitution |
P303
|
FINISHED |
| Object | research-intensive university |
—
|
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: research-intensive university | Statement: [Osaka Imperial University, typeOfInstitution, research-intensive university]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfInstitution Context triple: [Osaka Imperial University, typeOfInstitution, research-intensive university]
-
A.
campusType
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
B.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
C.
hostsInstitution
Indicates that one entity serves as the hosting location or organizing body for an institution.
-
D.
usedByInstitutionType
Indicates that something (such as a resource, tool, or service) is utilized or employed by a particular type or category of institution.
-
E.
cityOfInstitution
Indicates the city in which an institution is located or based.
- 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a23344daf8819083118bbac5f46568 |
completed | Feb. 28, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69a232e52e7c81909c072703e28e8c61 |
completed | Feb. 28, 2026, 12:12 a.m. |
| PDg | Predicate description generation | batch_69a233443224819097b91b150fdfbd1a |
completed | Feb. 28, 2026, 12:13 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.