Triple
T17010200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamela Brown |
E412678
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Pamela Brown |
—
|
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: Pamela Brown | Statement: [Pamela Brown, name, Pamela Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pamela Brown Context triple: [Pamela Brown, name, Pamela Brown]
-
A.
Pamela Brown
chosen
Pamela Brown was a British stage and film actress known for her intense character roles in mid-20th-century cinema and theatre.
-
B.
Pamela Hart
Pamela Hart is an actress best known for her role in the 1998 psychological thriller film "Pi."
-
C.
Pamela Reeves
Pamela Reeves was a respected American attorney and federal judge who served on the U.S. District Court for the Eastern District of Tennessee and was known for her trailblazing role as the court’s first female chief judge.
-
D.
Pamela Britton
Pamela Britton was an American actress best known for her character roles in mid-20th-century film and television, including appearances in classic noir and popular TV sitcoms.
-
E.
Pamela Goynes-Brown
Pamela Goynes-Brown is an American politician who serves as the mayor of North Las Vegas, Nevada.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d47a8444819081f1262eb7dbda40 |
completed | April 18, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:33 a.m.