Triple
T35625824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amy Sedaris as Princess Carolyn |
E1029447
|
entity |
| Predicate | characterRelationships |
P47184
|
FINISHED |
| Object | BoJackHorseman |
—
|
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: BoJackHorseman | Statement: [Amy Sedaris as Princess Carolyn, characterRelationships, BoJackHorseman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterRelationships Context triple: [Amy Sedaris as Princess Carolyn, characterRelationships, BoJackHorseman]
-
A.
characterActorRelationship
chosen
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
B.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
C.
portraysCharacterRelationship
Indicates that one entity depicts or represents the relationship between characters in another entity.
-
D.
titleCharacterRelation
Indicates the relationship between a work’s title and a specific character it references or centers on.
-
E.
relatedCharacter
Indicates that one character has a specified relationship or association with another character.
- 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_69f76e07bb0c8190968ea2d836fc42c9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffa5f31c8881908c26e2aa52df6ece |
completed | May 9, 2026, 9:24 p.m. |
| PD | Predicate disambiguation | batch_69ffa42aee408190ad1a5f285688b338 |
completed | May 9, 2026, 9:16 p.m. |
Created at: May 3, 2026, 4:05 p.m.