Triple
T17641053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Min (Vietnamese given name) |
E429226
|
entity |
| Predicate | canBeUsedForGender |
P29732
|
FINISHED |
| Object | male |
—
|
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 | Statement: [Min (Vietnamese given name), canBeUsedForGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeUsedForGender Context triple: [Min (Vietnamese given name), canBeUsedForGender, male]
-
A.
usedByGender
chosen
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
B.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
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.
-
E.
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.
- 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_69d889e2c2608190b762e76d9b2262f1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46de50bf481909e938613b38f0202 |
completed | April 19, 2026, 5:53 a.m. |
| PD | Predicate disambiguation | batch_69e3cddc87188190ac2f049b86038676 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 6:02 a.m.