Triple
T21868493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaw Brothers Studio |
E539944
|
entity |
| Predicate | backlotSize |
P7951
|
FINISHED |
| Object | one of the largest in Asia at the time |
—
|
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: one of the largest in Asia at the time | Statement: [Shaw Brothers Studio, backlotSize, one of the largest in Asia at the time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: backlotSize Context triple: [Shaw Brothers Studio, backlotSize, one of the largest in Asia at the time]
-
A.
hasBacklot
chosen
Indicates that one entity possesses or includes a backlot area associated with it.
-
B.
hasLargestStudioAreaSquareMetres
Indicates that the subject entity possesses the studio with the greatest area, measured in square metres, compared to relevant alternatives.
-
C.
filmStudioLot
Indicates a relationship where a film studio owns, operates, or is associated with a specific studio lot or production facility.
-
D.
filmingStudio
Indicates that a studio is responsible for producing or filming a particular audiovisual work.
-
E.
arenaWidth
Indicates the width dimension of an arena in a given context or representation.
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f33305d081908cd070134420607a |
completed | April 28, 2026, 5:49 p.m. |
| PD | Predicate disambiguation | batch_69e6be9394f88190945ddd1dc004d29d |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:57 p.m.