Triple
T25936609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Mama Joseph |
E653575
|
entity |
| Predicate | influenceOnPlot |
P98526
|
FINISHED |
| Object | her illness tests the family’s unity |
—
|
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: her illness tests the family’s unity | Statement: [Big Mama Joseph, influenceOnPlot, her illness tests the family’s unity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influenceOnPlot Context triple: [Big Mama Joseph, influenceOnPlot, her illness tests the family’s unity]
-
A.
influencesPlotOf
chosen
Indicates that one entity has an effect on or helps shape the storyline or narrative development of another entity.
-
B.
incorporatesInfluence
Indicates that one entity integrates or absorbs the influence, ideas, or characteristics of another into itself.
-
C.
influencedAspectOf
Indicates that one entity has affected, shaped, or altered a particular aspect or component of another entity.
-
D.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
E.
plotInvolvement
Indicates that an entity participates in, contributes to, or is affected by the events or storyline of a narrative work.
- 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_69e7ab3fd2f881908837305e4ba98011 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f676c440708190a4b9974e95d2291a |
completed | May 2, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69f675fd59608190b246383435e68fce |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 22, 2026, 8:39 a.m.