Triple
T37128722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isla Fisher as Tooth Fairy |
E919457
|
entity |
| Predicate | antagonistInWork |
P116420
|
FINISHED |
| Object | Pitch Black |
—
|
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: Pitch Black | Statement: [Isla Fisher as Tooth Fairy, antagonistInWork, Pitch Black]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: antagonistInWork Context triple: [Isla Fisher as Tooth Fairy, antagonistInWork, Pitch Black]
-
A.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
B.
antagonistActorRole
chosen
Indicates that an actor plays the role of an antagonist in a given work or context.
-
C.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
-
D.
antagonistEmbodiment
Indicates that one entity serves as the personification or concrete manifestation of an antagonistic force, role, or concept in relation to another entity.
-
E.
leadAntagonistCharacter
Indicates that one character serves as the primary opposing or villainous force in relation to another entity in the narrative.
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb78cbef988190b8f79d946b46e6b2 |
completed | May 6, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9ac5a08190b24ef308963fc52b |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:15 p.m.