Triple
T34222011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluebell |
E877945
|
entity |
| Predicate | hasNearbyLuasStop |
P29999
|
FINISHED |
| Object | Bluebell Luas stop |
—
|
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: Bluebell Luas stop | Statement: [Bluebell, hasNearbyLuasStop, Bluebell Luas stop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyLuasStop Context triple: [Bluebell, hasNearbyLuasStop, Bluebell Luas stop]
-
A.
hasNearbyTramStop
chosen
Indicates that a location has a tram stop situated within a short walking distance or close proximity.
-
B.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
C.
hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
-
D.
nearbyLRTStation
Indicates that there is an LRT (light rail transit) station located close to the referenced place or entity.
-
E.
nearbyTerminus
Indicates that one terminus (end point or final stop) is located close to another terminus in space.
- 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_69f349b16d0481908754e3069f05e0c1 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ffb69812808190a751853b30183e65 |
completed | May 9, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69ffb63bdda88190a9dd8426dc0bad43 |
completed | May 9, 2026, 10:33 p.m. |
Created at: May 1, 2026, 1:55 a.m.