Triple
T34267577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | muxe |
E879213
|
entity |
| Predicate | genderCategoryIncludes |
P178722
|
FINISHED |
| Object | people with male-assigned bodies |
—
|
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: people with male-assigned bodies | Statement: [muxe, genderCategoryIncludes, people with male-assigned bodies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderCategoryIncludes Context triple: [muxe, genderCategoryIncludes, people with male-assigned bodies]
-
A.
genderOfCategory
Indicates that a given category or class is associated with a particular gender.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
D.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
E.
bearerGender
Indicates the gender associated with the bearer in the relationship or context.
- 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_69f349b4f5fc819094b441d18e95e5f1 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f713bfdc148190a249a7874320bab8 |
completed | May 3, 2026, 9:22 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
| PDg | Predicate description generation | batch_69f7135fa2988190a20a94cfe616d754 |
completed | May 3, 2026, 9:20 a.m. |
Created at: May 1, 2026, 1:56 a.m.