Triple
T13541748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dixie Leonard |
E323403
|
entity |
| Predicate | entertainsInContext |
P73699
|
FINISHED |
| Object | wartime |
—
|
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: wartime | Statement: [Dixie Leonard, entertainsInContext, wartime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: entertainsInContext Context triple: [Dixie Leonard, entertainsInContext, wartime]
-
A.
entertainmentType
Indicates the kind or category of entertainment associated with an entity or event.
-
B.
theatricalContext
Indicates the situational or environmental circumstances related to a theatrical performance or production in which an action or relationship occurs.
-
C.
entertainedTroopsDuring
chosen
Indicates that an entity provided entertainment or performances for military troops during a specified time period or event.
-
D.
hasHumorousTreatmentOf
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
E.
attractsAudience
Indicates that an entity draws the interest or attention of people who choose to watch, listen to, or engage with it.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafd8ba10819098faadcc6adf251e |
completed | April 12, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69dbae1046c48190b4ee98c6c9cb9d85 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.