Triple
T6064500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giuliano |
E135120
|
entity |
| Predicate | usageGender |
P15656
|
FINISHED |
| Object | primarily male |
—
|
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: primarily male | Statement: [Giuliano, usageGender, primarily male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usageGender Context triple: [Giuliano, usageGender, primarily male]
-
A.
genderUsage
chosen
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
B.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
C.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
- 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_69c00878d06881909ee78e88913bf890 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05723c91c819090b4d4672e72f9f3 |
completed | March 22, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69c049f031408190b08b2766237c5dd0 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:10 p.m.