Triple
T25704515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Booby |
E644549
|
entity |
| Predicate | occupiesRoleInWork |
P142038
|
FINISHED |
| Object | comic antagonist |
—
|
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: comic antagonist | Statement: [Lady Booby, occupiesRoleInWork, comic antagonist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupiesRoleInWork Context triple: [Lady Booby, occupiesRoleInWork, comic antagonist]
-
A.
hasOrganizationalRole
Indicates that an entity holds a specific role, position, or function within an organization.
-
B.
inUniverseWorkplaceRole
Indicates that an entity holds a specific workplace role or job position within a fictional or narrative universe.
-
C.
hasWorksIn
Indicates that one entity is employed by or performs their professional activities within the organization, location, or context represented by another entity.
-
D.
employedRole
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
E.
hasOccupationInWork
chosen
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
- 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_69e77e83c8ec8190bf52fcdac4838984 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f60c3b09488190ade1b69ff7f0df0e |
completed | May 2, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f60b8461ac81908c5bd3d73eed59f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 21, 2026, 9:01 p.m.