Triple
T13024202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TQL Stadium |
E326257
|
entity |
| Predicate | hasSeatingCapacityForSoccer |
P2491
|
FINISHED |
| Object | 26000 |
—
|
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: 26000 | Statement: [TQL Stadium, hasSeatingCapacityForSoccer, 26000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatingCapacityForSoccer Context triple: [TQL Stadium, hasSeatingCapacityForSoccer, 26000]
-
A.
seatingCapacity
chosen
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
B.
capacityForWorldCup
Indicates the maximum number of spectators a venue can accommodate specifically for World Cup events.
-
C.
venueCapacityApproximate
Indicates an approximate or estimated capacity of a venue in terms of how many people it can accommodate.
-
D.
stadiumCapacityApprox
Indicates an approximate number of people that a stadium can accommodate.
-
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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efac71881908a21d70c3c6ce099 |
completed | April 10, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:52 p.m.