Triple
T34309753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Marylebone railway station |
E880407
|
entity |
| Predicate | isLondonTerminus |
P24452
|
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: [London Marylebone railway station, isLondonTerminus, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLondonTerminus Context triple: [London Marylebone railway station, isLondonTerminus, true]
-
A.
hasTerminusInCentralLondon
Indicates that the route, service, or line ends at a terminal point located within Central London.
-
B.
originalLondonTerminusLocation
Indicates the location of an entity’s original terminus station in London.
-
C.
primaryLondonTerminal
chosen
Indicates that a given station serves as the main London terminal for a particular rail service or route.
-
D.
hasLondonUndergroundStation
Indicates that a place or area contains at least one London Underground (Tube) station within its boundaries.
-
E.
secondaryLondonTerminal
Indicates that a location serves as a secondary terminal in London associated with a primary London terminal for a given service or route.
- 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_69f349b8bb6c8190ad12a7957a574f04 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:57 a.m.