Triple
T13236363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Come September |
E315155
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Brenda De Banzie |
E190480
|
NE 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: Brenda De Banzie | Statement: [Come September, starring, Brenda De Banzie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brenda De Banzie Context triple: [Come September, starring, Brenda De Banzie]
-
A.
Brenda de Banzie
chosen
Brenda de Banzie was a British stage and film actress known for her strong character roles in mid-20th-century cinema and theatre.
-
B.
Brenda James
Brenda James is an actress best known for her role in the horror-comedy film "Slither."
-
C.
Brenda Grate
Brenda Grate is an American voice actress best known for voicing Faline in Disney’s animated film "Bambi."
-
D.
Brenda Joyce
Brenda Joyce was an American film actress best known for her roles in 1930s and 1940s Hollywood productions, including several Tarzan films.
-
E.
Bammie Green
Bammie Green was the husband of American actress Anne Francis, known primarily in connection with her personal life rather than for a public career of his own.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d56da008190af55da3a9e7ffd4d |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a35ccc88190881a7066b7af8fea |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:22 p.m.