Triple
T661533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stade de France |
E11765
|
entity |
| Predicate | seatingCapacityForFootball |
P2491
|
FINISHED |
| Object | about 80,000 |
—
|
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: about 80,000 | Statement: [Stade de France, seatingCapacityForFootball, about 80,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatingCapacityForFootball Context triple: [Stade de France, seatingCapacityForFootball, about 80,000]
-
A.
seatingCapacity
chosen
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
stadiumCapacityApprox
Indicates an approximate number of people that a stadium can accommodate.
-
C.
homeStadiumCapacity
Indicates the seating capacity of the stadium that serves as a team's or organization's home venue.
-
D.
homeArenaCapacity
Indicates the maximum number of spectators that can be accommodated in an entity’s home arena.
-
E.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d1406ec8190abf546549264c85d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.