Triple
T37106988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Supporting Actor for "Moonstruck" |
E918867
|
entity |
| Predicate | categoryGenderRestriction |
P15554
|
FINISHED |
| Object | male supporting actor |
—
|
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: male supporting actor | Statement: [Academy Award for Best Supporting Actor for "Moonstruck", categoryGenderRestriction, male supporting actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: categoryGenderRestriction Context triple: [Academy Award for Best Supporting Actor for "Moonstruck", categoryGenderRestriction, male supporting actor]
-
A.
genderCategoryIncludes
Indicates that a given gender category encompasses or contains the specified gender identity or subgroup.
-
B.
genderOfCategory
Indicates that a given category or class is associated with a particular gender.
-
C.
hasGenderRequirement
chosen
Indicates that a particular role, activity, or context specifies a required or restricted gender for participation or eligibility.
-
D.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
E.
demographicRestriction
Indicates that participation, access, or applicability is limited or conditioned based on specific demographic characteristics (such as age, gender, ethnicity, or similar attributes).
- 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_69f76e9b99c8819096164b21ff5bd996 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.