Triple
T20655309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephanie Allain |
E507607
|
entity |
| Predicate | hasEthnicFocusInWork |
P98449
|
FINISHED |
| Object | Black filmmakers |
—
|
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: Black filmmakers | Statement: [Stephanie Allain, hasEthnicFocusInWork, Black filmmakers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEthnicFocusInWork Context triple: [Stephanie Allain, hasEthnicFocusInWork, Black filmmakers]
-
A.
hasNotableEthnicIdentityInIndustry
Indicates that an entity is recognized for having a significant or distinctive ethnic identity within a particular industry or professional field.
-
B.
hasEthnicScope
Indicates that something is relevant or applicable specifically to a particular ethnic group or ethnic context.
-
C.
primaryEthnicFocus
chosen
Indicates that something is chiefly oriented toward, concerned with, or designed for a particular ethnic group as its main focus.
-
D.
hasEthnicInfluence
Indicates that one entity has a cultural, traditional, or ethnic impact on, or contributes to shaping the ethnic character of, another entity.
-
E.
hasEthnicTarget
Indicates that an action, statement, or event is directed toward or targets a specific ethnic group.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2ec90e881909250884483429acf |
completed | April 20, 2026, 11:12 p.m. |
| PD | Predicate disambiguation | batch_69e5c0315f5081908098707c6455e56e |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 11:43 a.m.