Triple
T37106695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yidl Mitn Fidl |
E918860
|
entity |
| Predicate | characterGenderDisguise |
P146968
|
FINISHED |
| Object | female character posing as male |
—
|
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: female character posing as male | Statement: [Yidl Mitn Fidl, characterGenderDisguise, female character posing as male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterGenderDisguise Context triple: [Yidl Mitn Fidl, characterGenderDisguise, female character posing as male]
-
A.
genderAsHuman
Indicates that the specified entity has a particular human gender (e.g., male, female) assigned or identified.
-
B.
hasCrossDressingProtagonist
chosen
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
C.
playsGender
Indicates that one entity performs or assumes a particular gender role or identity in a given context.
-
D.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
E.
genderCustom
Indicates that an entity has a user-specified or non-standard gender designation beyond predefined gender categories.
- 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_69ffc704e1e88190884928a6a5c55a87 |
completed | May 9, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69ffc6b483d881908ad872e25fa6abc5 |
completed | May 9, 2026, 11:43 p.m. |
Created at: May 3, 2026, 4:14 p.m.