Triple
T21907365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Columbina |
E540973
|
entity |
| Predicate | typicalMaskUsage |
P145582
|
FINISHED |
| Object | often unmasked |
—
|
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 unmasked | Statement: [Columbina, typicalMaskUsage, often unmasked]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMaskUsage Context triple: [Columbina, typicalMaskUsage, often unmasked]
-
A.
typicalMaskType
Indicates the kind or category of mask that is most commonly or characteristically used or associated with an entity.
-
B.
usesMasking
Indicates that one entity applies or employs a masking technique or mechanism in relation to another entity or process.
-
C.
usesMaskIn
Indicates that an entity wears or employs a mask while present in or interacting within a specified context or location.
-
D.
mainMask
Indicates that one entity serves as the primary or default mask applied to another entity or process.
-
E.
traditionalMaskType
Indicates the specific kind or category of traditional mask associated with an entity.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f121d74d388190be58b937c486fa69 |
completed | April 28, 2026, 9:08 p.m. |
| PD | Predicate disambiguation | batch_69e6be9ebf4c8190892df1a8e1313f88 |
completed | April 21, 2026, 12:02 a.m. |
| PDg | Predicate description generation | batch_69e6c187bc548190b4ca13150f6bae38 |
completed | April 21, 2026, 12:15 a.m. |
Created at: April 16, 2026, 7:38 p.m.