Triple
T28126344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drama Desk Award for Outstanding Actor in a Musical |
E710936
|
entity |
| Predicate | genderedCategory |
P55024
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Drama Desk Award for Outstanding Actor in a Musical, genderedCategory, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderedCategory Context triple: [Drama Desk Award for Outstanding Actor in a Musical, genderedCategory, yes]
-
A.
isGenderSpecificCategory
chosen
Indicates that the category applies specifically to one gender rather than being gender-neutral.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
bearerGender
Indicates the gender associated with the bearer in the relationship or context.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular 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_69ef9b73bd288190a21ae3d6aa14f386 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 9:20 p.m.