Triple
T97982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vitaphone |
E1974
|
entity |
| Predicate | usedByExhibitorType |
P3517
|
FINISHED |
| Object | first-run urban theaters |
—
|
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: first-run urban theaters | Statement: [Vitaphone, usedByExhibitorType, first-run urban theaters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedByExhibitorType Context triple: [Vitaphone, usedByExhibitorType, first-run urban theaters]
-
A.
hasExhibition
Indicates that an entity organizes, hosts, or presents a particular exhibition.
-
B.
usedByInstitutionType
Indicates that something (such as a resource, tool, or service) is utilized or employed by a particular type or category of institution.
-
C.
organizationTypeOfPresenter
Indicates that the predicate specifies the type or category of organization to which the presenter belongs or that the presenter represents.
-
D.
sponsoringOrganizationType
Indicates the kind or category of organization that provides sponsorship or support in the described relationship or activity.
-
E.
usedByMedia
Indicates that something is employed or utilized by media organizations or media-related channels.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24feef1b08190bb9525f71cce053e |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebe7b1c8190a6bfbf31dc7c7f07 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a24f4b4658819087902414959161fb |
completed | Feb. 28, 2026, 2:13 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.