Triple
T26439922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Supporting Actress for Silver Linings Playbook |
E665058
|
entity |
| Predicate | forGender |
P46670
|
FINISHED |
| Object | actress |
—
|
LITERAL 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: actress | Statement: [Academy Award for Best Supporting Actress for Silver Linings Playbook, forGender, actress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forGender Context triple: [Academy Award for Best Supporting Actress for Silver Linings Playbook, forGender, actress]
-
A.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
B.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
C.
plugGender
Indicates that one entity’s connector has a specified gender (e.g., male, female, neutral) in relation to another connector or interface.
-
D.
featuredGender
chosen
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
E.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
- 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_69ee883c851881909e2ab04efbb3c5fe |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f6121a29408190af33e7a709a8a65f |
completed | May 2, 2026, 3:02 p.m. |
| PD | Predicate disambiguation | batch_69f602d5c8808190a1fdbebd6f0981e8 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 26, 2026, 11:57 p.m.