Triple
T29151158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madea Goes to Jail |
E738910
|
entity |
| Predicate | plotSummary |
P264
|
FINISHED |
| Object | Madea is arrested and sent to prison, where she encounters a group of troubled female inmates and influences their lives. |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: Madea is arrested and sent to prison, where she encounters a group of troubled female inmates and influences their lives. | Statement: [Madea Goes to Jail, plotSummary, Madea is arrested and sent to prison, where she encounters a group of troubled female inmates and influences their lives.]
Provenance (2 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_69f07cb46f148190874eb8576a447567 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f662a5297881909fe6bc9b5a013df3 |
completed | May 2, 2026, 8:46 p.m. |
Created at: April 28, 2026, 11:42 a.m.