Triple
T29039267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actress for "Carmen Jones" |
E737948
|
entity |
| Predicate | leadRoleCharacterName |
P36851
|
FINISHED |
| Object | Carmen Jones |
—
|
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: Carmen Jones | Statement: [Academy Award for Best Actress for "Carmen Jones", leadRoleCharacterName, Carmen Jones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadRoleCharacterName Context triple: [Academy Award for Best Actress for "Carmen Jones", leadRoleCharacterName, Carmen Jones]
-
A.
leadCharacterCaste
Indicates that the lead character in a work belongs to a specified caste.
-
B.
leadCharacterField
Indicates that one entity serves as the primary or main character associated with another entity, such as a work or production.
-
C.
leadActorRolePattern
Indicates a recurring or characteristic type of role that an actor typically plays as a leading performer in productions.
-
D.
leadCharacterNickname
Indicates that one entity is the nickname commonly used for the lead (main) character of another entity.
-
E.
characterName
chosen
Indicates that an entity has a specific name used to identify its character.
- F. None of above.
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_69f077efb3848190b41574e1670f6ae2 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f6603f2f88819080e922efd5f23f04 |
completed | May 2, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69f659d297cc8190b2b962ba30a1edb3 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 10 a.m.