Triple
T7659638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bubba Bexley |
E173470
|
entity |
| Predicate | portrayedInGenreContext |
P21332
|
FINISHED |
| Object | American television comedy |
—
|
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: American television comedy | Statement: [Bubba Bexley, portrayedInGenreContext, American television comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedInGenreContext Context triple: [Bubba Bexley, portrayedInGenreContext, American television comedy]
-
A.
coveredInGenre
Indicates that a work or item is associated with, categorized under, or treated within a particular genre.
-
B.
genreOfAppearance
chosen
Indicates the genre or type of creative work in which an entity appears.
-
C.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
D.
depictsGenre
Indicates that one entity visually represents or portrays the genre category associated with another entity.
-
E.
portrayedInWork
Indicates that an entity is depicted or represented as a character, figure, or subject within a specific creative work.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 3:59 p.m.