Triple
T13740792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buckley railway station |
E330077
|
entity |
| Predicate | isSmallLocalStop |
P75020
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Buckley railway station, isSmallLocalStop, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSmallLocalStop Context triple: [Buckley railway station, isSmallLocalStop, true]
-
A.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
B.
isRuralStop
chosen
Indicates that a stop is located in a rural or sparsely populated area rather than in an urban or suburban setting.
-
C.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
D.
stopsAtFewerStationsThan
Indicates that one transit service or route makes stops at a smaller number of stations than another transit service or route.
-
E.
hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de0206a4b88190a60914e2b43e54f9 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe950b148190ba0df8a749269ec6 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:55 p.m.