Triple
T6360935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edinburgh–Stirling services |
E143104
|
entity |
| Predicate | hasIntermediateStops |
P24280
|
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: [Edinburgh–Stirling services, hasIntermediateStops, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIntermediateStops Context triple: [Edinburgh–Stirling services, hasIntermediateStops, yes]
-
A.
numberOfIntermediateStops
Indicates the count of stops or pauses that occur between the starting point and the final destination in a journey or process.
-
B.
hasIntermediateStation
chosen
Indicates that a route, journey, or connection includes a station that lies between its starting point and its final destination.
-
C.
hasIntermediateCity
Indicates that there is a city located between two other places along a route or connection.
-
D.
numberOfIntermediateCoaches
Indicates the count of intermediate coaches (cars) that exist between two specified endpoints in a train configuration.
-
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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067fa0d0c819098d01545849142fc |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:32 p.m.