Triple
T65157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Airlines Center |
E1295
|
entity |
| Predicate | parkingCapacity |
P1708
|
FINISHED |
| Object | several thousand spaces |
—
|
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: several thousand spaces | Statement: [American Airlines Center, parkingCapacity, several thousand spaces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: parkingCapacity Context triple: [American Airlines Center, parkingCapacity, several thousand spaces]
-
A.
parkingType
Indicates the specific kind or category of parking arrangement associated with an entity (e.g., street, garage, lot, reserved).
-
B.
hasParking
chosen
Indicates that a place or facility provides designated parking space(s) available for use.
-
C.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
D.
typicalCapacity
Indicates the usual or standard amount, volume, or capability that something is designed or expected to hold, handle, or perform under normal conditions.
-
E.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2516eda54819090f5c14384d4eab1 |
completed | Feb. 28, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69a24ea5c140819080409a968c8d2ce8 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.