Triple
T33184801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alan Brady |
E849435
|
entity |
| Predicate | hasWorkplaceInFiction |
P76778
|
FINISHED |
| Object | television studio |
—
|
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: television studio | Statement: [Alan Brady, hasWorkplaceInFiction, television studio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkplaceInFiction Context triple: [Alan Brady, hasWorkplaceInFiction, television studio]
-
A.
worksAtFictionalPlace
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
-
B.
worksWithInFiction
Indicates that two fictional characters are depicted as collaborating, interacting, or being associated with each other within a narrative work.
-
C.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
D.
worksForFictionalOrganization
Indicates that an entity is employed by or affiliated as a worker with a fictional organization.
-
E.
workLocationOfFictionalCharacter
chosen
Indicates the place or organization where a fictional character is depicted as working within their narrative context.
- 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_69f3495e0f108190a6a7006f79f9c2c3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fddac4e2f48190a9301d3422658b29 |
completed | May 8, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69fdda06969c8190b5d033964ea2a690 |
completed | May 8, 2026, 12:41 p.m. |
Created at: May 1, 2026, 1:29 a.m.