Triple
T23316822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penn Cinema Riverfront |
E590730
|
entity |
| Predicate | accessibleForFreeParking |
P32762
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Penn Cinema Riverfront, accessibleForFreeParking, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accessibleForFreeParking Context triple: [Penn Cinema Riverfront, accessibleForFreeParking, yes]
-
A.
isAccessibleForFreeParking
chosen
Indicates that a location or facility can be used for parking without any cost.
-
B.
hasParkingFor
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
C.
accessibleWithoutParkTicket
Indicates that a location, attraction, or service can be reached or used without requiring a park admission ticket.
-
D.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
E.
hasParkingNearby
Indicates that a location has one or more parking facilities or spaces available within a close surrounding area.
- 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_69e25d1d32188190948eb76909d1dcc3 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197816024819086c547ba89f84286 |
completed | April 29, 2026, 5:30 a.m. |
| PD | Predicate disambiguation | batch_69effcf8ca2c8190887d4f4656617d21 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:06 p.m.