Triple
T22103900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Education (2019 film) |
E546236
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Louise Ford |
—
|
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: Louise Ford | Statement: [Bad Education (2019 film), editor, Louise Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louise Ford Context triple: [Bad Education (2019 film), editor, Louise Ford]
-
A.
Louise Ford
chosen
Louise Ford is a film editor known for her work on acclaimed movies such as the psychological horror film "The Lighthouse."
-
B.
Louise Ford
Louise Ford is a British actress and comedian known for her work in television sketch shows and sitcoms, as well as for her relationship with actor Rowan Atkinson.
-
C.
Louise Franklin
Louise Franklin was the wife of British actor George Coulouris, known primarily in relation to his personal life.
-
D.
Louise Lombard
Louise Lombard is a British actress best known for her roles in the TV series "CSI: Crime Scene Investigation" and "The House of Eliott."
-
E.
Louise Burns
Louise Burns is a fictional character in the television series "M*A*S*H," known primarily as the often-mentioned but never-seen wife of Major Frank Burns.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1291815f88190a6eaf73e444dc1c2 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.