Triple
T1090899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisiana Superdome |
E24159
|
entity |
| Predicate | seatingCapacityMaximum |
P2491
|
FINISHED |
| Object | over 76,000 |
—
|
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 76,000 | Statement: [Louisiana Superdome, seatingCapacityMaximum, over 76,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatingCapacityMaximum Context triple: [Louisiana Superdome, seatingCapacityMaximum, over 76,000]
-
A.
seatingCapacity
chosen
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
audienceCapacityType
Indicates the classification or type of capacity used to describe how many audience members a venue or event space can accommodate.
-
C.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
D.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
E.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
- 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_69a49404428c819092dcc9632f5f7b8b |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b980c8448190b08c3a9a7e7f4e85 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b741b0cc8190be001a16a81f6d9e |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.