Triple
T4881577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Moneypenny |
E109338
|
entity |
| Predicate | notableInteractionStyleWithJamesBond |
P59584
|
FINISHED |
| Object | playful banter |
—
|
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: playful banter | Statement: [Miss Moneypenny, notableInteractionStyleWithJamesBond, playful banter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableInteractionStyleWithJamesBond Context triple: [Miss Moneypenny, notableInteractionStyleWithJamesBond, playful banter]
-
A.
numberOfJamesBondFilmsStarring
Indicates the count of James Bond films in which a specified actor (or entity) appeared in the starring role.
-
B.
successorAsJamesBond
Indicates that one entity took over the role of portraying James Bond from another entity.
-
C.
firstJamesBondFilmAppearanceYear
Indicates the year in which a given actor or character first appeared in a James Bond film.
-
D.
chiefBritishNegotiator
Indicates that the subject serves as the primary British representative responsible for conducting negotiations with the object.
-
E.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
- 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_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. |
| PDg | Predicate description generation | batch_69bd6d5976a081909090c0c263f6e9b7 |
completed | March 20, 2026, 3:52 p.m. |
Created at: March 20, 2026, 1:27 p.m.