Triple
T6134632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lust for Life |
E136801
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Pamela Brown |
E412678
|
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: Pamela Brown | Statement: [Lust for Life, castMember, Pamela Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pamela Brown Context triple: [Lust for Life, castMember, 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 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.
-
C.
Pamela Goynes-Brown
Pamela Goynes-Brown is an American politician who serves as the mayor of North Las Vegas, Nevada.
-
D.
Pamela Martin
Pamela Martin is an American film editor known for her work on acclaimed movies such as "The Fighter" and "Little Miss Sunshine."
-
E.
Pamela Duncan
Pamela Duncan was an American film and television actress active in the 1950s and 1960s, known for her roles in low-budget Westerns and genre pictures.
- 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c7f34d081909e589b201b22be21 |
completed | March 22, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd93fee4c88190a00a71c146067eef |
completed | April 1, 2026, 9:54 p.m. |
Created at: March 22, 2026, 4:15 p.m.