Triple
T15718564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Creative Campus |
E381024
|
entity |
| Predicate | hasFacilityForSubject |
P119904
|
FINISHED |
| Object | fine art |
—
|
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: fine art | Statement: [Creative Campus, hasFacilityForSubject, fine art]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacilityForSubject Context triple: [Creative Campus, hasFacilityForSubject, fine art]
-
A.
hasFacilityStatusFor
Indicates that an entity has a particular operational or condition status assigned to a specific facility.
-
B.
hasFacilityLevel
Indicates the degree or tier of capability, service, or infrastructure that a particular facility possesses.
-
C.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
D.
hasSubfacility
Indicates that one facility is a subordinate or component facility within another, larger facility.
-
E.
usesFacilityFor
Indicates that an entity makes use of a particular facility to carry out a specific purpose, activity, or function.
- 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f932a248190b65ecfb2bc56e715 |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0094af5b481908ad51d5d7ba0c726 |
completed | April 15, 2026, 9:55 p.m. |
Created at: April 10, 2026, 4:45 a.m.