Triple
T24280134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mak Yong |
E605512
|
entity |
| Predicate | hasMaskUsage |
P145582
|
FINISHED |
| Object | limited or no masks in most variants |
—
|
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: limited or no masks in most variants | Statement: [Mak Yong, hasMaskUsage, limited or no masks in most variants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaskUsage Context triple: [Mak Yong, hasMaskUsage, limited or no masks in most variants]
-
A.
usesMaskIn
Indicates that an entity wears or employs a mask while present in or interacting within a specified context or location.
-
B.
typicalMaskUsage
chosen
Indicates how a mask is most commonly or appropriately used in relation to an entity or situation.
-
C.
usesMasking
Indicates that one entity applies or employs a masking technique or mechanism in relation to another entity or process.
-
D.
hasIconicMask
Indicates that an entity is associated with or characterized by a distinctive, widely recognized mask.
-
E.
mainMask
Indicates that one entity serves as the primary or default mask applied to another entity or process.
- 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_69e2954707dc8190915551eb114cfff6 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28f51050481908a9bd3c586702057 |
completed | April 29, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69f1c457a2908190993824395b3c365d |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:07 a.m.