Triple
T25040767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yanyuwa language |
E627100
|
entity |
| Predicate | hasGenderedSpeech |
P160090
|
FINISHED |
| Object | male speech variety |
—
|
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 speech variety | Statement: [Yanyuwa language, hasGenderedSpeech, male speech variety]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderedSpeech Context triple: [Yanyuwa language, hasGenderedSpeech, male speech variety]
-
A.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
B.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
C.
hasGenderDistinctionsInPronouns
Indicates that the language or system uses different pronoun forms to reflect the gender of the referent.
-
D.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
E.
hasGenderConvention
Indicates that there is an established or customary way of assigning or expressing gender within a given context, system, or culture.
- F. None of above. chosen
Provenance (4 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_69e2ff2a2c088190be513727ee8bfe78 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f6018f91248190985323d1a678e539 |
completed | May 2, 2026, 1:52 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f5ffc6268c8190b63f6360ebadab73 |
completed | May 2, 2026, 1:44 p.m. |
Created at: April 18, 2026, 6:08 a.m.