Triple
T8328114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aviatrix Trophy |
E195005
|
entity |
| Predicate | hasRecipientGenderFocus |
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: [Aviatrix Trophy, hasRecipientGenderFocus, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecipientGenderFocus Context triple: [Aviatrix Trophy, hasRecipientGenderFocus, female]
-
A.
hasGenderOfRecipients
chosen
Indicates the gender category or composition of the recipients involved in a given relationship or action.
-
B.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
C.
hasFemaleRecipients
Indicates that the subject entity has one or more recipients who are female.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
- 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_69ca82e87f2c8190bdb71ee29dfc642d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f8243288190b1ae74d69395fc91 |
completed | March 31, 2026, 8:02 a.m. |
| PD | Predicate disambiguation | batch_69cb70c3231c81909e3d463192c9de22 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:56 p.m.