Triple
T14910390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaBahn Arena |
E371244
|
entity |
| Predicate | hasParkingConnection |
P116641
|
FINISHED |
| Object | adjacent campus parking structures |
—
|
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: adjacent campus parking structures | Statement: [LaBahn Arena, hasParkingConnection, adjacent campus parking structures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingConnection Context triple: [LaBahn Arena, hasParkingConnection, adjacent campus parking structures]
-
A.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
B.
hasParkingFor
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
C.
hasParkStatus
Indicates that an entity holds a particular designation or status related to being a park (e.g., national park, city park, protected parkland).
-
D.
hasParkFeature
Indicates that a park includes, contains, or is associated with a specific feature or amenity.
-
E.
hasParkSection
Indicates that one entity includes, contains, or is associated with a specific section or area of a park.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61c6b9c8190a92934d49b98fe46 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a4d8dc8190a4c0841c20f2875f |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:26 a.m.