Triple
T26260924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiang region |
E656843
|
entity |
| Predicate | hasRegionalOpera |
P30380
|
FINISHED |
| Object | Xiang opera |
—
|
NE NERFINISHED |
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: Xiang opera | Statement: [Xiang region, hasRegionalOpera, Xiang opera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegionalOpera Context triple: [Xiang region, hasRegionalOpera, Xiang opera]
-
A.
hasOperaHouseAssociation
Indicates a relationship where an entity is connected or related to an opera house, such as through location, ownership, operation, affiliation, or significant involvement.
-
B.
hasOperaHouse
Indicates that one entity possesses, contains, or is the location of an opera house.
-
C.
premiereOperaPlace
Indicates the place where an opera was first premiered or publicly performed.
-
D.
premiereOperaCountry
Indicates the country in which an opera was first premiered or publicly performed.
-
E.
associatedOpera
chosen
Indicates that there is a relationship linking an entity to an opera with which it is connected or related (e.g., as subject, inspiration, 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_69ee5b4e21bc819082be98bc9ab09796 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69fd783fed9c81909e792702636c4f1f |
completed | May 8, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_69fd7788e63c81909de22fdafcfe41c0 |
completed | May 8, 2026, 5:41 a.m. |
Created at: April 26, 2026, 9:10 p.m.