Triple
T35281905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankie and Johnny |
E1018955
|
entity |
| Predicate | antagonistGender |
P119796
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Frankie and Johnny, antagonistGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistGender Context triple: [Frankie and Johnny, antagonistGender, male]
-
A.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
B.
hasFemaleAntagonistProtagonist
Indicates that the work features both a female antagonist and a female protagonist in central opposing roles.
-
C.
antagonistStatus
Indicates that an entity holds an opposing or adversarial role, often acting as the main source of conflict relative to another entity or objective.
-
D.
antagonistActorRole
Indicates that an actor plays the role of an antagonist in a given work or context.
-
E.
antagonistAttribute
chosen
Indicates that an entity possesses a characteristic or role specifically associated with being an antagonist in a narrative or conflict.
- 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_69f76de6d39c8190bb11342e4b91ff2b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.