Triple
T24941335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marquis of Santa Cruz |
E623461
|
entity |
| Predicate | traditionalGender |
P20413
|
FINISHED |
| Object | 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: male | Statement: [Marquis of Santa Cruz, traditionalGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalGender Context triple: [Marquis of Santa Cruz, traditionalGender, male]
-
A.
genderNorms
Indicates socially constructed expectations or rules about how individuals should behave, appear, or identify based on their perceived gender.
-
B.
hasTypicalGenderAssociation
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
C.
hasGenderConvention
Indicates that there is an established or customary way of assigning or expressing gender within a given context, system, or culture.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
hasGenderInSomeTraditions
chosen
Indicates that, in at least some cultural, religious, or historical traditions, the subject is regarded as having a specific 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6430a93a48190854ce71df680b2fa |
completed | May 2, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69f641da05b881909f6283c988639c53 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 18, 2026, 5:30 a.m.