Triple
T2222424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kew Gardens–Union Turnpike |
E48170
|
entity |
| Predicate | isExpressStopFor |
P25018
|
FINISHED |
| Object | E |
—
|
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: E | Statement: [Kew Gardens–Union Turnpike, isExpressStopFor, E]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isExpressStopFor Context triple: [Kew Gardens–Union Turnpike, isExpressStopFor, E]
-
A.
hasStopType
chosen
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
B.
hasStop
Indicates that something (such as a route, service, or journey) includes or is associated with a particular stop or stopping point.
-
C.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
D.
canExpress
Indicates that an entity has the ability or capacity to convey, articulate, or communicate something (such as an idea, emotion, or property).
-
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_69a88aa1ee708190862c8c378c41e9eb |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc03bfdd48190bfb96ec3e41c22dc |
completed | March 7, 2026, 6:05 a.m. |
| PD | Predicate disambiguation | batch_69abbdac31d8819092d17815e11921e9 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.