Triple
T7875189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theatre Royal Windsor |
E182832
|
entity |
| Predicate | hasSeatingCapacityApprox |
P2491
|
FINISHED |
| Object | 600 |
—
|
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: 600 | Statement: [Theatre Royal Windsor, hasSeatingCapacityApprox, 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatingCapacityApprox Context triple: [Theatre Royal Windsor, hasSeatingCapacityApprox, 600]
-
A.
seatingCapacity
chosen
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
seatCount
Indicates the number of seats associated with an entity, such as a venue, vehicle, or room.
-
C.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
D.
typicalSeatingCapacityUpperBound
Indicates the maximum number of seats that a venue or vehicle is typically designed or allowed to accommodate under normal conditions.
-
E.
typicalSeatingCapacityLowerBound
Indicates the minimum number of seats that an entity is typically designed or expected to provide.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a961188190b2f12f8fe5d66641 |
completed | March 31, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69cae928e1b88190b0620f4c4f03bc7d |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:57 p.m.