Triple
T10982308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memorial Stadium |
E259537
|
entity |
| Predicate | hasSeatingCapacityCategory |
P96445
|
FINISHED |
| Object | over 60,000 |
—
|
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: over 60,000 | Statement: [Memorial Stadium, hasSeatingCapacityCategory, over 60,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatingCapacityCategory Context triple: [Memorial Stadium, hasSeatingCapacityCategory, over 60,000]
-
A.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
-
C.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
D.
seatCategory
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an entity.
-
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. 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d772eb518c8190a885a417815f2ff6 |
completed | April 9, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69d72e9055908190b438f039574aaaaf |
completed | April 9, 2026, 4:44 a.m. |
| PDg | Predicate description generation | batch_69d732242fdc8190be77d1f730a42935 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:24 p.m.