Triple
T24635955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wei-Wu-Ying Opera House |
E609810
|
entity |
| Predicate | seatingCapacityOfPlayhouse |
P2491
|
FINISHED |
| Object | about 1210 |
—
|
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: about 1210 | Statement: [Wei-Wu-Ying Opera House, seatingCapacityOfPlayhouse, about 1210]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatingCapacityOfPlayhouse Context triple: [Wei-Wu-Ying Opera House, seatingCapacityOfPlayhouse, about 1210]
-
A.
seatingCapacity
chosen
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
roomCapacity
Indicates the maximum number of people or occupants that a room is designed or allowed to hold.
-
C.
capacityPerChair
Indicates the amount of capacity (such as weight, volume, or occupancy) that is assigned to or supported by each individual chair.
-
D.
venueCapacityApproximate
Indicates an approximate or estimated capacity of a venue in terms of how many people it can accommodate.
-
E.
typicalSeatingCapacityUpperBound
Indicates the maximum number of seats that a venue or vehicle is typically designed or allowed to accommodate under normal conditions.
- 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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:32 a.m.