Triple
T21115277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verna Bloom |
E520279
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Verna Bloom |
—
|
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: Verna Bloom | Statement: [Verna Bloom, name, Verna Bloom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verna Bloom Context triple: [Verna Bloom, name, Verna Bloom]
-
A.
Verna Bloom
chosen
Verna Bloom was an American actress best known for her roles in films such as "Animal House," "High Plains Drifter," and "The Last Temptation of Christ."
-
B.
Verna Meads
Verna Meads is a private individual best known for being married to Pinetree.
-
C.
Velma Kelly
Velma Kelly is a brash, vaudeville-performing murderess and one of the central characters in the musical "Chicago," known for her sharp wit, show-stopping numbers, and rivalry with Roxie Hart.
-
D.
Verna Willis
Verna Willis was an American film editor and script supervisor active during Hollywood’s early studio era.
-
E.
Barbara Pepper
Barbara Pepper was an American film and television actress best known for her comedic roles in the mid-20th century, including a regular part on the sitcom "Green Acres."
- 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72105bd648190beecc636284397bd |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:54 p.m.