Triple
T18669447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary Foster |
E456431
|
entity |
| Predicate | producerOf |
P490
|
FINISHED |
| Object | Gloria Bell |
—
|
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: Gloria Bell | Statement: [Gary Foster, producerOf, Gloria Bell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gloria Bell Context triple: [Gary Foster, producerOf, Gloria Bell]
-
A.
Gloria Bell
chosen
Gloria Bell is a 2018 American romantic comedy-drama film, directed by Sebastián Lelio and starring Julianne Moore as a free-spirited divorcée navigating love and independence in Los Angeles.
-
B.
Gloria Greer
Gloria Greer was an American actress best known for her work in early 20th-century films and for her marriage to film director Alan Crosland.
-
C.
Gloria Rand
Gloria Rand is a Canadian actress best known as the first wife of actor William Shatner.
-
D.
Elaine Baylor
Elaine Baylor is known as the wife of legendary Basketball Hall of Famer Elgin Baylor.
-
E.
Gloria Swenson
Gloria Swenson is the tough, streetwise former mob moll who becomes an unlikely protector of a young boy in the crime thriller film "Gloria."
- 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_69d8d38f72b4819090a935175d9ca8af |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e556b0502881909ea05f2746163746 |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 10, 2026, 11:48 a.m.