Triple
T16429410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noh theatre |
E399032
|
entity |
| Predicate | usesMaskType |
P40424
|
FINISHED |
| Object | onna-men (female mask) |
—
|
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: onna-men (female mask) | Statement: [Noh theatre, usesMaskType, onna-men (female mask)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMaskType Context triple: [Noh theatre, usesMaskType, onna-men (female mask)]
-
A.
usesMaskIn
Indicates that an entity wears or employs a mask while present in or interacting within a specified context or location.
-
B.
typicalMaskType
Indicates the kind or category of mask that is most commonly or characteristically used or associated with an entity.
-
C.
mayMask
Indicates that one entity is permitted or able to conceal, obscure, or hide another entity or its properties.
-
D.
includesMasqueradeType
Indicates that one entity contains or encompasses a specific type or category of masquerade within its scope or definition.
-
E.
traditionalMaskType
chosen
Indicates the specific kind or category of traditional mask associated with an entity.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328fd49708190abb5065fa430eff1 |
completed | April 18, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:09 a.m.