Triple
T1909796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashton Moss tram stop |
E38081
|
entity |
| Predicate | isInMetrolinkZoneSystem |
P844
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ashton Moss tram stop, isInMetrolinkZoneSystem, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInMetrolinkZoneSystem Context triple: [Ashton Moss tram stop, isInMetrolinkZoneSystem, yes]
-
A.
hasFareZone
chosen
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
-
B.
hasMetroLine
Indicates that a location or area is served by, or connected to, a specific metro (subway) line.
-
C.
hasRailwayZone
Indicates that a location or railway entity falls under the jurisdiction or coverage area of a specific railway zone.
-
D.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
-
E.
hasFormerFareZone
Indicates that an entity was previously assigned to a particular fare zone, but is no longer in that fare zone.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafeba3d88190afcce67483d8625b |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.