Triple
T29970764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wu Daozi |
E761306
|
entity |
| Predicate | hasRoleInLegend |
P134527
|
FINISHED |
| Object | painter who entered his own mural |
—
|
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: painter who entered his own mural | Statement: [Wu Daozi, hasRoleInLegend, painter who entered his own mural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRoleInLegend Context triple: [Wu Daozi, hasRoleInLegend, painter who entered his own mural]
-
A.
inLegendRole
chosen
Indicates that an entity appears in a legendary or mythic context fulfilling a specific narrative or symbolic role.
-
B.
hasLegendAssociatedWith
Indicates that something is connected to or accompanied by a traditional story, myth, or legend.
-
C.
hasRoleCharacteristic
Indicates that an entity possesses a specific characteristic, quality, or attribute associated with a particular role.
-
D.
hasRole
Indicates that an entity occupies, performs, or is assigned a specific role or function in relation to another entity or context.
-
E.
hasLegalRole
Indicates that an entity holds a specific legal capacity, status, or function in relation to another entity or 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_69f22467626081908d5afea489590e96 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: April 29, 2026, 6:32 p.m.