Triple
T2355489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Street, Boston |
E47542
|
entity |
| Predicate | hasCurbsideParking |
P3380
|
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: [Charles Street, Boston, hasCurbsideParking, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCurbsideParking Context triple: [Charles Street, Boston, hasCurbsideParking, yes]
-
A.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
B.
hasDropOffArea
Indicates that an entity provides a designated area where items, passengers, or goods can be temporarily left or unloaded.
-
C.
hasParkingNearby
Indicates that a location has one or more parking facilities or spaces available within a close surrounding area.
-
D.
hasParkAndRideGarage
Indicates that a location includes a parking facility where people can park their vehicles and transfer to public transit services.
-
E.
parkingType
chosen
Indicates the specific kind or category of parking arrangement associated with an entity (e.g., street, garage, lot, reserved).
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abcb802da08190980100444010f91e |
completed | March 7, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69abc5981ce48190a3f7852d28276e11 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:54 p.m.