Triple
T4881599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Moneypenny |
E109338
|
entity |
| Predicate | portrayedInFranchiseAs |
P55200
|
FINISHED |
| Object | civilian staff member of MI6 |
—
|
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: civilian staff member of MI6 | Statement: [Miss Moneypenny, portrayedInFranchiseAs, civilian staff member of MI6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedInFranchiseAs Context triple: [Miss Moneypenny, portrayedInFranchiseAs, civilian staff member of MI6]
-
A.
portrayedInFranchise
Indicates that an entity is depicted as a character or element within a specific media franchise.
-
B.
portraysFictionalEntity
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
C.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
D.
wasPortrayedAs
chosen
Indicates that one entity has been depicted or represented in the form or role of another entity, typically within some medium or context.
-
E.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
- 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_69bd440e9d64819083e82cf33b4d9570 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6dde6fcc8190a5aa7587f85632bd |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2be5e881909f6ec9c3bcde49f3 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:27 p.m.