Triple
T38188760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London–Manchester |
E1005393
|
entity |
| Predicate | hasRailTerminusInLondon |
P146904
|
FINISHED |
| Object | London Euston railway station |
—
|
NE NERFINISHED |
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: London Euston railway station | Statement: [London–Manchester, hasRailTerminusInLondon, London Euston railway station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailTerminusInLondon Context triple: [London–Manchester, hasRailTerminusInLondon, London Euston railway station]
-
A.
hasLondonUndergroundStation
Indicates that a place or area contains at least one London Underground (Tube) station within its boundaries.
-
B.
hasTerminusInCentralLondon
chosen
Indicates that the route, service, or line ends at a terminal point located within Central London.
-
C.
hasLondonOvergroundPlatforms
Indicates that the subject has platforms specifically served by the London Overground rail network.
-
D.
hasAdjacentStationOnElizabethLine
Indicates that one station is directly next to another station along the Elizabeth Line, with no other stations in between.
-
E.
originalLondonTerminusLocation
Indicates the location of an entity’s original terminus station in London.
- 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_69f76dbc22c481908139b694ffde7a0c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fe21b0cba48190b56c39e9f1c0eafa |
completed | May 8, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69fe204576848190aecf204e2adba5dc |
completed | May 8, 2026, 5:41 p.m. |
Created at: May 3, 2026, 4:29 p.m.