Triple
T9015428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duplicity |
E215581
|
entity |
| Predicate | featuresFormerOccupationOfProtagonists |
P35945
|
FINISHED |
| Object | government agents |
—
|
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: government agents | Statement: [Duplicity, featuresFormerOccupationOfProtagonists, government agents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFormerOccupationOfProtagonists Context triple: [Duplicity, featuresFormerOccupationOfProtagonists, government agents]
-
A.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
B.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
C.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
D.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
E.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional 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_69ca83a38aa88190bf1bb80c4548b5e2 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69fc0e4c819080b60456375f94cd |
completed | April 1, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69cc5edf84408190aa5f57cb8bfd00e1 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:06 p.m.