Triple
T32544415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugh Jackman as P. T. Barnum |
E831800
|
entity |
| Predicate | hasOnScreenSpouse |
P30304
|
FINISHED |
| Object | Charity Barnum |
—
|
NE NERFINISHED |
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: Charity Barnum | Statement: [Hugh Jackman as P. T. Barnum, hasOnScreenSpouse, Charity Barnum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnScreenSpouse Context triple: [Hugh Jackman as P. T. Barnum, hasOnScreenSpouse, Charity Barnum]
-
A.
hasSpouseInStory
chosen
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
B.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
C.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
D.
hasNamesakeSpouse
Indicates that one entity has a spouse who shares the same name as another specified entity.
-
E.
hasSpouseInTVSeries
Indicates that one person is the spouse of another person within the context of a specific TV series.
- 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_69f34925fd08819084cfe4ec566cb704 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7626667f48190ad90867eb67ec582 |
completed | May 3, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f76175d6608190b60b268e20f49ed9 |
completed | May 3, 2026, 2:53 p.m. |
Created at: May 1, 2026, 1:02 a.m.