Triple
T11808584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyōgen theatre |
E280810
|
entity |
| Predicate | hasMaskType |
P65729
|
FINISHED |
| Object | okina mask in some pieces |
—
|
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: okina mask in some pieces | Statement: [Kyōgen theatre, hasMaskType, okina mask in some pieces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaskType Context triple: [Kyōgen theatre, hasMaskType, okina mask in some pieces]
-
A.
typicalMaskType
chosen
Indicates the kind or category of mask that is most commonly or characteristically used or associated with an entity.
-
B.
usesMaskIn
Indicates that an entity wears or employs a mask while present in or interacting within a specified context or location.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
-
E.
mayMask
Indicates that one entity is permitted or able to conceal, obscure, or hide another entity or its properties.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.