Triple
T29760925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actress for Sigourney Weaver |
E753764
|
entity |
| Predicate | actress |
P167856
|
FINISHED |
| Object | Sigourney Weaver |
—
|
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: Sigourney Weaver | Statement: [Academy Award for Best Actress for Sigourney Weaver, actress, Sigourney Weaver]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: actress Context triple: [Academy Award for Best Actress for Sigourney Weaver, actress, Sigourney Weaver]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
supportingActorAwardRecipient
Indicates that an entity has received an award specifically for a supporting acting role in a performance or production.
-
C.
madeActressA
Indicates that one entity caused or was responsible for another entity becoming an actress.
-
D.
leadingActressNominee
Indicates that a person has been nominated for an award in the leading actress category for a particular work or performance.
-
E.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
- F. None of above. chosen
Provenance (4 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_69f0ef827ff88190ade56e0b0846b713 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f673d041448190a414a19cf28b09d2 |
completed | May 2, 2026, 9:59 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 28, 2026, 8:33 p.m.