Triple
T7780741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belsize Park tube station |
E221507
|
entity |
| Predicate | hasRailwayStationCategory |
P27767
|
FINISHED |
| Object | London Underground station in Camden |
—
|
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: London Underground station in Camden | Statement: [Belsize Park tube station, hasRailwayStationCategory, London Underground station in Camden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailwayStationCategory Context triple: [Belsize Park tube station, hasRailwayStationCategory, London Underground station in Camden]
-
A.
railwayStationCategory
chosen
Indicates the classification or type category assigned to a railway station within a rail network or system.
-
B.
hasRailwayStation
Indicates that a place or location is served by, or contains, a railway station.
-
C.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
-
D.
railwayStationInstanceOf
Indicates that a given railway station is an instance of a specified class or type of railway station.
-
E.
hasRailwayStationOn
Indicates that a railway station is located on or serves a particular railway line, route, or network segment.
- 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_69ca83ebbef881909ac47f789145fef7 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cae7e779ec8190b77296d9c2ac3210 |
completed | March 30, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69caa488532c819093ac40bba0b3c7ef |
completed | March 30, 2026, 4:27 p.m. |
Created at: March 30, 2026, 4:20 p.m.