Triple
T9694179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ngenechen |
E234605
|
entity |
| Predicate | genderConcept |
P2577
|
FINISHED |
| Object | often described with combined male and female aspects |
—
|
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: often described with combined male and female aspects | Statement: [Ngenechen, genderConcept, often described with combined male and female aspects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderConcept Context triple: [Ngenechen, genderConcept, often described with combined male and female aspects]
-
A.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
B.
genderCategories
chosen
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
D.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d348868819083aec7a5da8c455b |
completed | April 1, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b840f081909f66bf0b66d17d9b |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:17 p.m.