Triple
T3171498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soliloquy (from "Carousel") |
E66356
|
entity |
| Predicate | associatedCharacterOccupation |
P21567
|
FINISHED |
| Object | carnival barker |
—
|
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: carnival barker | Statement: [Soliloquy (from "Carousel"), associatedCharacterOccupation, carnival barker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCharacterOccupation Context triple: [Soliloquy (from "Carousel"), associatedCharacterOccupation, carnival barker]
-
A.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
B.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
C.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
D.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
E.
representedOccupation
Indicates that one entity has served as an official or formal representative of another entity’s occupation or professional role.
- 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_69ad8585d7988190af37365331093ccd |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada66c043081908eb23a4fe3420a78 |
completed | March 8, 2026, 4:40 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0076b4819094628f1ad10b8f68 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.