Triple
T23864902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richmond Staples Mill Road station |
E592550
|
entity |
| Predicate | hasCarRentalNearby |
P6090
|
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: [Richmond Staples Mill Road station, hasCarRentalNearby, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCarRentalNearby Context triple: [Richmond Staples Mill Road station, hasCarRentalNearby, yes]
-
A.
hasRentalCarCenter
chosen
Indicates that a location or facility includes or is associated with a rental car center where vehicles can be rented.
-
B.
hasNearbyHotel
Indicates that one entity is located close to or within a short distance of a hotel.
-
C.
hasParkingNearby
Indicates that a location has one or more parking facilities or spaces available within a close surrounding area.
-
D.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
E.
hasAirportHotelNearby
Indicates that an airport has at least one hotel located in its immediate vicinity or within a short travel distance.
- 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_69e25d22eb488190914b193aff952e83 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1cae318cc8190afd0915517964494 |
completed | April 29, 2026, 9:09 a.m. |
| PD | Predicate disambiguation | batch_69f1614a65a88190bde1efb368a151e4 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:13 p.m.