Triple
T30988417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linda Christian as Valerie Mathis |
E789595
|
entity |
| Predicate | firstTelevisedBondGirlPortrayal |
P204257
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Linda Christian as Valerie Mathis, firstTelevisedBondGirlPortrayal, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstTelevisedBondGirlPortrayal Context triple: [Linda Christian as Valerie Mathis, firstTelevisedBondGirlPortrayal, true]
-
A.
firstAppearanceAsMissMoneypenny
Indicates the event or context in which a person is depicted or credited for the first time in the role of Miss Moneypenny.
-
B.
screenDebutOfBondUniverseElement
Indicates the first on-screen appearance of a particular element within the Bond universe.
-
C.
firstJamesBondFilmAppearanceYear
Indicates the year in which a given actor or character first appeared in a James Bond film.
-
D.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
E.
hasBondGirl
Indicates that a person, typically a James Bond character, is associated with a romantic or significant female partner known as a "Bond girl."
- 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_69f224c550b081909ddfceb0c3d03bdd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0358c946f08190a8fb41c643074475 |
completed | May 12, 2026, 4:43 p.m. |
| PD | Predicate disambiguation | batch_6a03586e5bb08190a71e075fde7cd679 |
completed | May 12, 2026, 4:42 p.m. |
| PDg | Predicate description generation | batch_6a0358c89acc8190b22421ff8ce0f575 |
completed | May 12, 2026, 4:43 p.m. |
Created at: April 29, 2026, 8:56 p.m.