Triple
T33514715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alonzo P. Hawk |
E858334
|
entity |
| Predicate | appearsOppositeCharacter |
P194395
|
FINISHED |
| Object | Professor Ned Brainard |
—
|
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: Professor Ned Brainard | Statement: [Alonzo P. Hawk, appearsOppositeCharacter, Professor Ned Brainard]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearsOppositeCharacter Context triple: [Alonzo P. Hawk, appearsOppositeCharacter, Professor Ned Brainard]
-
A.
appearsAgainst
Indicates that one entity is visually or publicly presented in opposition to, or in contrast with, another entity.
-
B.
antagonistAlterEgoOf
Indicates that one entity serves as the primary opposing force or enemy of another entity’s alternate identity or secret persona.
-
C.
antagonistActorRole
Indicates that an actor plays the role of an antagonist in a given work or context.
-
D.
appearsWithCharacter
Indicates that two characters are shown or present together within the same scene, shot, or context.
-
E.
oppositeRoleTo
chosen
Indicates that two entities hold roles that are opposed, complementary, or functionally contrary to each other within a given context.
- 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_69f3497721848190978fbee5e0a526f8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd8e5f7c4c8190ab8e2f2a7bb1bd79 |
completed | May 8, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69fd8d8a16f08190b9e880901bfa44fe |
completed | May 8, 2026, 7:15 a.m. |
Created at: May 1, 2026, 1:39 a.m.