Triple
T16611565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crashing |
E403581
|
entity |
| Predicate | star |
P23405
|
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: [Crashing, star, Louise Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louise Ford Context triple: [Crashing, star, Louise Ford]
-
A.
Louise Ford
Louise Ford is a film editor known for her work on acclaimed movies such as the psychological horror film "The Lighthouse."
-
B.
Louise Ford
chosen
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 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.
-
E.
Dorothy Ford
Dorothy Ford is the grandmother of Georgia Ford, known primarily through her familial connection within the Ford family.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36096356c819092815d64db041793 |
completed | April 18, 2026, 10:44 a.m. |
Created at: April 10, 2026, 5:17 a.m.