Triple
T16909871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louie De Palma |
E410165
|
entity |
| Predicate | comedicRoleInSeries |
P71317
|
FINISHED |
| Object | primary source of conflict and dark humor |
—
|
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: primary source of conflict and dark humor | Statement: [Louie De Palma, comedicRoleInSeries, primary source of conflict and dark humor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comedicRoleInSeries Context triple: [Louie De Palma, comedicRoleInSeries, primary source of conflict and dark humor]
-
A.
narrativeRoleInSeries
Indicates the specific narrative function or role an entity plays within a particular series or serialized work.
-
B.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
C.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
D.
isHumorousCharacter
chosen
Indicates that the character is portrayed in a humorous way or primarily serves a comedic role in the context.
-
E.
cameoCharacter
Indicates that one entity appears briefly or in a minor, special-guest role within the context or work associated with another entity.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3ca3bdc3081908a9b4f6e63405348 |
completed | April 18, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69e32b9489408190bcb2ede567ff5bf9 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:30 a.m.