Triple
T211977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto International Film Festival |
E4739
|
entity |
| Predicate | organizerType |
P3580
|
FINISHED |
| Object | non-profit organization |
—
|
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: non-profit organization | Statement: [Toronto International Film Festival, organizerType, non-profit organization]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: organizerType Context triple: [Toronto International Film Festival, organizerType, non-profit organization]
-
A.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
B.
organizationTypeOfPresenter
Indicates that the predicate specifies the type or category of organization to which the presenter belongs or that the presenter represents.
-
C.
organizationType
chosen
Indicates the specific category or classification of an organization in terms of its nature, structure, or primary function.
-
D.
typeOfEvent
Indicates that one entity is classified as a specific kind or category of event.
-
E.
organizes
Indicates that one entity arranges, coordinates, or structures activities, items, or people into an ordered or planned form for a particular purpose.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b4f71b88190866c8262922ae204 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.