Triple
T27114091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oh My General |
E686792
|
entity |
| Predicate | portraysCrossDressing |
P100368
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Oh My General, portraysCrossDressing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysCrossDressing Context triple: [Oh My General, portraysCrossDressing, true]
-
A.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
B.
reasonForCrossDressing
Indicates the motivation or purpose behind an entity engaging in cross-dressing.
-
C.
portraysPersonAs
chosen
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
D.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
E.
genderDepicted
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
- 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_69ef148accd48190b6ed6e13a15f2a4f |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: April 27, 2026, 8:55 a.m.