Triple
T13610404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Smile Back |
E325171
|
entity |
| Predicate | leadActorOccupation |
P110410
|
FINISHED |
| Object | comedian |
—
|
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: comedian | Statement: [I Smile Back, leadActorOccupation, comedian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadActorOccupation Context triple: [I Smile Back, leadActorOccupation, comedian]
-
A.
majorActor
Indicates that the subject is a primary or leading participant in the action, event, or production involving the object.
-
B.
employedActor
Indicates that one entity has hired or currently employs another entity to perform work or services.
-
C.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
-
D.
leadActorLaterOffice
Indicates that an individual who was the lead actor in a work later held a formal office or official position.
-
E.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbbb8c77dc8190b7bd803b5e168d23 |
completed | April 12, 2026, 3:34 p.m. |
Created at: April 9, 2026, 9:50 p.m.