Triple
T33326753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maudie Prickett |
E853283
|
entity |
| Predicate | portrayedTypicalRole |
P85537
|
FINISHED |
| Object | maid |
—
|
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: maid | Statement: [Maudie Prickett, portrayedTypicalRole, maid]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedTypicalRole Context triple: [Maudie Prickett, portrayedTypicalRole, maid]
-
A.
typicalRole
Indicates that one entity serves as the usual, characteristic, or commonly expected role or function of another entity.
-
B.
portraysRoleTrait
Indicates that one entity depicts or represents a particular role or character trait of another entity.
-
C.
typicalPerformerRoleType
chosen
Indicates the usual or characteristic role type that a performer commonly plays or is associated with in their performances.
-
D.
portrayedByCharacterType
Indicates that an entity is depicted or represented by a character of a specified type (e.g., hero, villain, sidekick) in a narrative or media work.
-
E.
contrastWithActorTypicalRoles
Indicates that an actor’s role in a given work is notably different from the types of roles they are typically known for playing.
- 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_69f349685f088190b8fda44083a018a9 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69feb5e66224819083b87c3707a5a5e0 |
completed | May 9, 2026, 4:19 a.m. |
| PD | Predicate disambiguation | batch_69feb3bd700c8190991ed200cd3c04db |
completed | May 9, 2026, 4:10 a.m. |
Created at: May 1, 2026, 1:33 a.m.