Triple
T17217546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Man with the Golden Gun (film) |
E417890
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Maud Adams |
—
|
NE NERFINISHED |
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: Maud Adams | Statement: [The Man with the Golden Gun (film), stars, Maud Adams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maud Adams Context triple: [The Man with the Golden Gun (film), stars, Maud Adams]
-
A.
Maud Adams
chosen
Maud Adams is a Swedish actress best known for her roles as Bond girls in the James Bond films "The Man with the Golden Gun" and "Octopussy."
-
B.
Luana Patten
Luana Patten was an American child actress best known for her early work in Walt Disney films during the 1940s and 1950s.
-
C.
Anne Ramsey
Anne Ramsey was an American character actress best known for her distinctive gravelly voice and memorable roles in films like "Throw Momma from the Train" and "The Goonies."
-
D.
Lilli Taylor
Lilli Taylor is an American actress known for her work in independent films and acclaimed television series, often portraying complex, unconventional characters.
-
E.
Sela Ward
Sela Ward is an American actress known for her Emmy-winning performances in television dramas such as "Sisters" and "Once and Again."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42ddb2b148190b3b50572cc285e3d |
completed | April 19, 2026, 1:20 a.m. |
Created at: April 10, 2026, 5:38 a.m.