Triple
T1333510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mak Yong |
E28694
|
entity |
| Predicate | typicalCast |
P27858
|
FINISHED |
| Object | all-female cast |
—
|
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: all-female cast | Statement: [Mak Yong, typicalCast, all-female cast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCast Context triple: [Mak Yong, typicalCast, all-female cast]
-
A.
typicalCasting
Indicates that one entity is the usual or standard casting choice for portraying another entity (such as a role, character, or type).
-
B.
cast
Indicates that an agent selects and assigns a person or thing to play a specific role or function in a production or context.
-
C.
castIn
Indicates that an actor or performer appears in a particular film, show, or production.
-
D.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
E.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1e98900819092c54c0fb58b958a |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef174708190a07bbc697fe19a2d |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c1bf31988190a659f48fe018f4bc |
completed | March 1, 2026, 10:46 p.m. |
Created at: March 1, 2026, 7:55 p.m.