Triple
T21590281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hit & Miss |
E532759
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Karla Crome |
—
|
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: Karla Crome | Statement: [Hit & Miss, starring, Karla Crome]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karla Crome Context triple: [Hit & Miss, starring, Karla Crome]
-
A.
Karla Crome
chosen
Karla Crome is a British actress and writer known for her roles in television series such as Misfits, Under the Dome, and Carnival Row.
-
B.
Carlene Watkins
Carlene Watkins is an American television actress known for her roles in various sitcoms and TV series from the late 1970s onward.
-
C.
Carolyn Surtees
Carolyn Surtees is known as the wife of acclaimed American cinematographer Bruce Surtees.
-
D.
Lorna Patterson
Lorna Patterson is an American actress best known for her comedic role as the singing stewardess in the classic parody film "Airplane!"
-
E.
Sheila Girling
Sheila Girling was a British abstract painter known for her vibrant color-field works and for her long artistic partnership and marriage with sculptor Anthony Caro.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadd0ec88190929c76137bd1603e |
completed | April 27, 2026, 5:57 a.m. |
Created at: April 16, 2026, 6:32 p.m.