Triple
T38371901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Kaluuya as Fred Hampton |
E892606
|
entity |
| Predicate | portraysCause |
P201112
|
FINISHED |
| Object | Black liberation |
—
|
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 liberation | Statement: [Daniel Kaluuya as Fred Hampton, portraysCause, Black liberation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysCause Context triple: [Daniel Kaluuya as Fred Hampton, portraysCause, Black liberation]
-
A.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
-
B.
causeInStory
Indicates that one event, action, or state functions as the cause of another within the narrative structure of a story.
-
C.
focusesOnCause
Indicates that an action, explanation, or analysis is directed toward identifying, examining, or emphasizing the underlying cause of something.
-
D.
causeDescribedAs
Indicates that one entity is described or characterized as the cause of another entity or event.
-
E.
causesFeaturesIn
Indicates that one entity is responsible for producing, giving rise to, or bringing about specific characteristics or features in another entity.
- 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_69f76e47cb4c8190bdd92cd1db59c0c5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffc7b4c7f88190b6357a44e7f0940f |
completed | May 9, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69ffc755f09c8190995ca00d97336988 |
completed | May 9, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69ffc7b41e688190ad3b86d87c38888e |
completed | May 9, 2026, 11:48 p.m. |
Created at: May 3, 2026, 4:31 p.m.