Triple
T33297520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fox and His Friends |
E852487
|
entity |
| Predicate | hasQueerProtagonist |
P88125
|
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: [Fox and His Friends, hasQueerProtagonist, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasQueerProtagonist Context triple: [Fox and His Friends, hasQueerProtagonist, true]
-
A.
hasLGBTCharacter
chosen
Indicates that the subject includes, features, or is associated with one or more characters who identify as lesbian, gay, bisexual, or transgender.
-
B.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
C.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
D.
protagonistGenderIdentityTheme
Indicates that the work explores themes related to the protagonist’s gender identity and its development, expression, or impact within the narrative.
-
E.
hasLGBTTheme
Indicates that the subject includes, features, or centrally involves lesbian, gay, bisexual, or transgender themes or issues.
- 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_69f34966ed4c81908dc9dda82d8c7fe3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:33 a.m.