Triple
T31709578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marlon Williams |
E809279
|
entity |
| Predicate | relationshipToActor |
P207499
|
FINISHED |
| Object | loosely based on Marlon Wayans’ comedic persona |
—
|
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: loosely based on Marlon Wayans’ comedic persona | Statement: [Marlon Williams, relationshipToActor, loosely based on Marlon Wayans’ comedic persona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToActor Context triple: [Marlon Williams, relationshipToActor, loosely based on Marlon Wayans’ comedic persona]
-
A.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
B.
relationshipToPlayer
Indicates the type of personal or social connection an entity has with the player.
-
C.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
D.
relationshipToUser
Indicates the type of connection or association an entity has with the current user.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- F. None of above. chosen
Provenance (4 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_69f348df4e048190a4a5a9932ada78d6 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7ee0388190a29faeb5cdb0950a |
completed | May 12, 2026, 7:16 p.m. |
Created at: April 30, 2026, 11:15 p.m.