Triple
T5835514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girl Crazy (1943 film) |
E129458
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Guy Kibbee |
E115943
|
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: Guy Kibbee | Statement: [Girl Crazy (1943 film), castMember, Guy Kibbee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guy Kibbee Context triple: [Girl Crazy (1943 film), castMember, Guy Kibbee]
-
A.
Guy Kibbee
chosen
Guy Kibbee was an American character actor best known for his affable, often comical supporting roles in 1930s and 1940s Hollywood films.
-
B.
Milton Kibbee
Milton Kibbee was an American character actor known for his numerous supporting roles in Hollywood films during the 1930s and 1940s.
-
C.
Kevin Durkan
Kevin Durkan is a notable individual recognized for achievements significant enough to be associated with the surname Durkan.
-
D.
George Gatins
George Gatins is an American screenwriter and film producer best known for writing the 2014 action film adaptation of the racing video game series Need for Speed.
-
E.
Phil Davis
Phil Davis is an English actor and director known for his character roles in film and television, including prominent work in British crime dramas and literary adaptations.
- 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_69c0084af79c81908af128ccc29983d0 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c034a358708190bfce78e7bd75db36 |
completed | March 22, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a196ebb88190aea57899be954aae |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:54 p.m.