Triple
T24287237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunbury railway station |
E605702
|
entity |
| Predicate | hasBusInterchangeNearby |
P2423
|
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: [Sunbury railway station, hasBusInterchangeNearby, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusInterchangeNearby Context triple: [Sunbury railway station, hasBusInterchangeNearby, yes]
-
A.
hasBusInterchange
chosen
Indicates that one transport-related entity includes, contains, or is associated with a bus interchange facility.
-
B.
hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
-
C.
hasNearbyInterchange
Indicates that one location has a transportation interchange (such as a junction or transfer point) situated close to it.
-
D.
hasBusStation
Indicates that a place or area contains or is served by a bus station.
-
E.
hasAdjacentBusStation
Indicates that one location has a bus station situated directly next to or very near it.
- 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_69e295480d0c8190846fc3c2e2da1d4c |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28f56cdc08190a1e06f67dffd4769 |
completed | April 29, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69f1c457a2908190993824395b3c365d |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:08 a.m.