Triple
T22454377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marques Houston as Kevin Barnes |
E555074
|
entity |
| Predicate | portrayalIncludes |
P49090
|
FINISHED |
| Object | comedicElements |
—
|
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: comedicElements | Statement: [Marques Houston as Kevin Barnes, portrayalIncludes, comedicElements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalIncludes Context triple: [Marques Houston as Kevin Barnes, portrayalIncludes, comedicElements]
-
A.
portrayalFeature
chosen
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
C.
portrayalIntroduced
Indicates that one entity is introduced or presented as a portrayal or depiction of another entity.
-
D.
portrayalIntendedAs
Indicates that one entity is meant to represent, depict, or stand in for another entity in an intentional portrayal.
-
E.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
- 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b4e2bd4819083e5bed44e9776c6 |
completed | April 29, 2026, 1:13 a.m. |
| PD | Predicate disambiguation | batch_69e898ad961c819098fd1e46129bddcc |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:48 p.m.