Triple
T872994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Live and Let Die |
E18854
|
entity |
| Predicate | characterOccupationOfProtagonist |
P11527
|
FINISHED |
| Object | British Secret Service agent |
—
|
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: British Secret Service agent | Statement: [Live and Let Die, characterOccupationOfProtagonist, British Secret Service agent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterOccupationOfProtagonist Context triple: [Live and Let Die, characterOccupationOfProtagonist, British Secret Service agent]
-
A.
creativeRole
Indicates that an entity holds a specific creative function or responsibility in relation to another entity, such as a work or project.
-
B.
roleInText
chosen
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
C.
mainProtagonist
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
D.
depictsPersonRole
Indicates that an image or representation shows a person in a specific role, function, or capacity.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac97d0f88190b67fcb7fc058e4b9 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8b9b5c81909ac71904f8b8b5cd |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.