Triple
T504072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Central Convention Complex |
E10462
|
entity |
| Predicate | railwayStationOpened |
P14462
|
FINISHED |
| Object | 1880 |
—
|
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: 1880 | Statement: [Manchester Central Convention Complex, railwayStationOpened, 1880]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayStationOpened Context triple: [Manchester Central Convention Complex, railwayStationOpened, 1880]
-
A.
hasRailwayStation
Indicates that a place or location is served by, or contains, a railway station.
-
B.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
-
C.
hasHeritageRailwayStation
Indicates that an entity possesses or is associated with a railway station that is preserved or operated as a heritage or historical railway facility.
-
D.
openedAsRailStop
Indicates that an entity began operation or was first established specifically as a railway stop.
-
E.
stationName
Indicates the name assigned to a particular station in the relationship.
- F. None of above. chosen
Provenance (4 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f149bd1c81908ff58ac504ace2bf |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfce7a08190a408bc019de60d5d |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebbd70481908b462296671de67b |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.