Triple
T34223059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connolly railway station |
E877975
|
entity |
| Predicate | hasLuasStop |
P17789
|
FINISHED |
| Object | Connolly 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: Connolly Luas stop | Statement: [Connolly railway station, hasLuasStop, Connolly Luas stop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLuasStop Context triple: [Connolly railway station, hasLuasStop, Connolly Luas stop]
-
A.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
B.
hasStop
chosen
Indicates that something (such as a route, service, or journey) includes or is associated with a particular stop or stopping point.
-
C.
hasStopFeature
Indicates that one entity possesses or is equipped with a feature that enables stopping or halting an associated process, action, or movement.
-
D.
isSmallStop
Indicates that something functions as a minor or less significant stop within a route, sequence, or process.
-
E.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
- 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_69f710846ef4819092c9a75057a0d767 |
completed | May 3, 2026, 9:08 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:55 a.m.