Triple
T34965743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | After Dark |
E1008389
|
entity |
| Predicate | mainFrameCharacters |
P167070
|
FINISHED |
| Object | an itinerant portrait painter and his wife |
—
|
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: an itinerant portrait painter and his wife | Statement: [After Dark, mainFrameCharacters, an itinerant portrait painter and his wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainFrameCharacters Context triple: [After Dark, mainFrameCharacters, an itinerant portrait painter and his wife]
-
A.
mainCharactersAre
Indicates that the specified entities serve as the primary or central characters in a narrative or work.
-
B.
mainMortalCharacter
Indicates that the referenced entity serves as the primary mortal (non-immortal) character in the context of a story or narrative.
-
C.
mainCharacterField
chosen
Indicates that one entity is designated as the primary or central character associated with another entity, such as a work or narrative.
-
D.
mainGuestCharacter
Indicates that one entity serves as the primary guest character in relation to another entity, such as a work or episode.
-
E.
maimedCharacter
Indicates that one character has caused severe physical injury or mutilation to 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_69f76dc69564819099e9e78aed6ff0a6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.