Triple
T34167174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanagasa Festival |
E876442
|
entity |
| Predicate | hatType |
P154091
|
FINISHED |
| Object | straw hat decorated with artificial flowers |
—
|
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: straw hat decorated with artificial flowers | Statement: [Hanagasa Festival, hatType, straw hat decorated with artificial flowers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hatType Context triple: [Hanagasa Festival, hatType, straw hat decorated with artificial flowers]
-
A.
hatName
chosen
Indicates that an entity has or is associated with a hat identified by a specific name.
-
B.
chapeau
Indicates that one entity serves as a hat or head covering worn by another entity.
-
C.
hatTeil
Indicates that something is a part or component of something else.
-
D.
hatOrt
Indicates that something is located at or associated with a specific place or location.
-
E.
hatAmtsbezeichnungIn
Indicates that an entity holds or is associated with a specific official title or designation within a given context.
- 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_69f349ad97ac8190bf1f17417c970e64 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f70fdfb02481908656f80d4f801ddf |
completed | May 3, 2026, 9:05 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:54 a.m.