Triple
T29082951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deadliest Woman in the Galaxy |
E734025
|
entity |
| Predicate | genderOfDescribedCharacter |
P95608
|
FINISHED |
| Object | female |
—
|
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 | Statement: [Deadliest Woman in the Galaxy, genderOfDescribedCharacter, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfDescribedCharacter Context triple: [Deadliest Woman in the Galaxy, genderOfDescribedCharacter, female]
-
A.
genderOfPersona
Indicates the gender identity associated with a given persona.
-
B.
genderDepicted
chosen
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
C.
fictionalGender
Indicates that one entity has a gender identity or classification that exists only within a fictional or imaginary context.
-
D.
genderOfName
Indicates the gender typically associated with a given name.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or 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_69f05b0c0f28819086eae6e84f2ae472 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6645ba71c81908044ade6ab577018 |
completed | May 2, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f663362c008190a22afed262f1e426 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 28, 2026, 10:57 a.m.