Triple
T28921969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Worst Jobs in History |
E733536
|
entity |
| Predicate | hasTonyRobinsonRole |
P180029
|
FINISHED |
| Object | host |
—
|
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: host | Statement: [The Worst Jobs in History, hasTonyRobinsonRole, host]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTonyRobinsonRole Context triple: [The Worst Jobs in History, hasTonyRobinsonRole, host]
-
A.
hasEdwardBurnsRole
Indicates that an entity holds or is assigned a role associated with Edward Burns, such as a character he plays or a position linked to his work.
-
B.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
-
C.
hasPortrayedRole
Indicates that an entity has performed or depicted a specific role or character, typically in a work such as a film, play, or television show.
-
D.
worksForCharacterPlayedBy
Indicates that one character is employed by, or works under, another character who is portrayed by a specific actor.
-
E.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
- F. None of above. chosen
Provenance (4 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_69f05b0a5cc0819094828367ae204b70 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
| PDg | Predicate description generation | batch_69f730890a008190a882f7828f1c9162 |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 28, 2026, 8:20 a.m.