Triple
T37843990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Orlofsky |
E943552
|
entity |
| Predicate | genderPresentationOnStage |
P175023
|
FINISHED |
| Object | trouser role |
—
|
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: trouser role | Statement: [Prince Orlofsky, genderPresentationOnStage, trouser role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderPresentationOnStage Context triple: [Prince Orlofsky, genderPresentationOnStage, trouser role]
-
A.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
B.
genderAsHuman
Indicates that the specified entity has a particular human gender (e.g., male, female) assigned or identified.
-
C.
genderPositioning
chosen
Indicates how roles, behaviors, or identities are organized, expressed, or perceived in relation to gender within a given context.
-
D.
genderCustom
Indicates that an entity has a user-specified or non-standard gender designation beyond predefined gender categories.
-
E.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
- 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_69f76eeb0f7081908d6d3adbc469889c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:19 p.m.