Triple
T7005277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dee Road station |
E162436
|
entity |
| Predicate | hasParkingLotOnBothSidesOfTracks |
P73541
|
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: [Dee Road station, hasParkingLotOnBothSidesOfTracks, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingLotOnBothSidesOfTracks Context triple: [Dee Road station, hasParkingLotOnBothSidesOfTracks, yes]
-
A.
railTracks
Indicates that one entity consists of, includes, or is associated with rail tracks used for guiding trains or rail vehicles.
-
B.
hasTrackLanes
Indicates that an entity (such as a road or track) includes one or more designated lanes for vehicle or train movement.
-
C.
hasWheelchairLanes
Indicates that a location, route, or facility includes designated lanes or pathways specifically designed for wheelchair use.
-
D.
hasParallelRunway
Indicates that one runway is parallel in orientation and alignment to another runway.
-
E.
hasParkingApron
Indicates that a location or facility includes a designated parking apron area for vehicles or aircraft.
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc34b5a88190a793e07dd4d0018b |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c67c94819084fdcf0398606027 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c575f081908b43d95d1d99b1a4 |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:33 p.m.