Triple
T31102764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Song Liling |
E792716
|
entity |
| Predicate | operaSpecialty |
P200607
|
FINISHED |
| Object | Peking 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: Peking opera | Statement: [Song Liling, operaSpecialty, Peking opera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaSpecialty Context triple: [Song Liling, operaSpecialty, Peking opera]
-
A.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
B.
operaGenre
Indicates that an opera belongs to or is categorized under a particular musical or dramatic genre.
-
C.
operaEnÁmbito
Indicates that an entity operates, functions, or carries out its activities within a specified domain, field, or scope.
-
D.
associatedOpera
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).
-
E.
operaHouseType
Indicates the specific kind or classification of an opera house associated with an entity.
- F. None of above. chosen
Provenance (4 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ff9b1ad27081908f8a492396950795 |
completed | May 9, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69ff9a6354c48190ae21070c1424cb7a |
completed | May 9, 2026, 8:34 p.m. |
| PDg | Predicate description generation | batch_69ff9b19f254819099bb9c034d2b399b |
completed | May 9, 2026, 8:37 p.m. |
Created at: April 29, 2026, 9:03 p.m.