Triple
T4505992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regular Show |
E101332
|
entity |
| Predicate | protagonistsOccupation |
P21567
|
FINISHED |
| Object | groundskeepers |
—
|
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: groundskeepers | Statement: [Regular Show, protagonistsOccupation, groundskeepers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistsOccupation Context triple: [Regular Show, protagonistsOccupation, groundskeepers]
-
A.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
B.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
C.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
D.
protagonistDescription
Indicates that a text provides a descriptive summary or characterization of the story’s main protagonist.
-
E.
protagonistBasedOn
Indicates that a fictional work’s main character is modeled on, inspired by, or derived from a particular real or fictional person or entity.
- 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_69bd43d175248190894dc58b5b395c26 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56ff78748190bb667e70c69dc817 |
completed | March 20, 2026, 2:17 p.m. |
| PD | Predicate disambiguation | batch_69bd5218afb4819087c99e0a1f22e137 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:01 p.m.