Triple
T33378021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanisław Zaleski |
E854688
|
entity |
| Predicate | hasNameComponentGender |
P104114
|
FINISHED |
| Object | masculine |
—
|
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: masculine | Statement: [Stanisław Zaleski, hasNameComponentGender, masculine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameComponentGender Context triple: [Stanisław Zaleski, hasNameComponentGender, masculine]
-
A.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasNameGenderUsage
Indicates that a particular name is used with a specific gender or set of genders in a given context.
-
D.
genderOfName
chosen
Indicates the gender typically associated with a given name.
-
E.
hasGenderInPortuguese
Indicates that a term or entity is associated with a specific grammatical gender in the Portuguese language.
- 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_69f3496ca10c8190908640d18fa00832 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fecb4d02f881909a9ee97ce98000d5 |
completed | May 9, 2026, 5:51 a.m. |
| PD | Predicate disambiguation | batch_69fec9846c1c8190b317f0711f0755db |
completed | May 9, 2026, 5:43 a.m. |
Created at: May 1, 2026, 1:35 a.m.