Triple
T14944728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Perseus and Andromeda |
E372626
|
entity |
| Predicate | hasGenderOfMainFigures |
P21355
|
FINISHED |
| Object | male hero and female victim |
—
|
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: male hero and female victim | Statement: [Perseus and Andromeda, hasGenderOfMainFigures, male hero and female victim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderOfMainFigures Context triple: [Perseus and Andromeda, hasGenderOfMainFigures, male hero and female victim]
-
A.
genderDepicted
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
B.
hasLeadCharacterGender
chosen
Indicates that the primary or lead character in a work has a specified gender.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
E.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68d20048190a403af85fe43dede |
completed | April 15, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:39 a.m.