Triple
T8437597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akinobu |
E199266
|
entity |
| Predicate | isUnisexName |
P28714
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Akinobu, isUnisexName, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isUnisexName Context triple: [Akinobu, isUnisexName, false]
-
A.
isUnisex
chosen
Indicates that something is suitable, designed, or intended for use by individuals of any gender.
-
B.
isUnisexInSomeRegions
Indicates that the item or concept is considered suitable or applicable to all genders, but only in certain geographic or cultural regions.
-
C.
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.
-
D.
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.
-
E.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
- 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:08 p.m.