Triple
T31295074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorothea and Francesca |
E798049
|
entity |
| Predicate | hasGenderOfSubjects |
P19009
|
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: [Dorothea and Francesca, hasGenderOfSubjects, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderOfSubjects Context triple: [Dorothea and Francesca, hasGenderOfSubjects, female]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
-
C.
hasGenderOfRecipients
chosen
Indicates the gender category or composition of the recipients involved in a given relationship or action.
-
D.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
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_69f224dfde288190af313f3c221c857e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fb6fdc7eb081908ab8475efb38c430 |
completed | May 6, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69fb5a986e588190b7a10892bd2ff44c |
completed | May 6, 2026, 3:13 p.m. |
Created at: April 29, 2026, 9:14 p.m.