Triple
T29050993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miek |
E735260
|
entity |
| Predicate | MCUVersionGenderPresentation |
P46670
|
FINISHED |
| Object | female-presenting (later films) |
—
|
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-presenting (later films) | Statement: [Miek, MCUVersionGenderPresentation, female-presenting (later films)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MCUVersionGenderPresentation Context triple: [Miek, MCUVersionGenderPresentation, female-presenting (later films)]
-
A.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
B.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
featuredGender
chosen
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
E.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
- 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_69f077e64b88819094d37bdbca8191b3 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f66065c17081908a0bb6b8a7f16558 |
completed | May 2, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69f659d297cc8190b2b962ba30a1edb3 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 10:08 a.m.