Triple
T33240823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsugaru lacquerware |
E850956
|
entity |
| Predicate | numberOfLacquerLayers |
P148172
|
FINISHED |
| Object | dozens of layers |
—
|
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: dozens of layers | Statement: [Tsugaru lacquerware, numberOfLacquerLayers, dozens of layers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLacquerLayers Context triple: [Tsugaru lacquerware, numberOfLacquerLayers, dozens of layers]
-
A.
typicalLayerCount
chosen
Indicates the usual or standard number of layers associated with an entity.
-
B.
hasNumberOfEnamelPanels
Indicates the relationship that specifies how many enamel panels are present on or associated with a given object or entity.
-
C.
hasNumberOfPaintedPanels
Indicates the relationship specifying how many panels in an object or structure are painted.
-
D.
coatingType
Indicates the type or kind of coating applied to or associated with an entity.
-
E.
layerOf
Indicates that one entity forms a distinct layer or stratum of another entity within a structured or composite whole.
- 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_69f34962386c81909ddc3bf9e18ddeb8 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a030713bd2881908bcc69e7d1e9fd09 |
completed | May 12, 2026, 10:55 a.m. |
| PD | Predicate disambiguation | batch_6a0304c29abc819085ffd17ab9a8c844 |
completed | May 12, 2026, 10:45 a.m. |
Created at: May 1, 2026, 1:31 a.m.