Triple
T17441598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Okehampton railway station |
E424666
|
entity |
| Predicate | hasTouristTraffic |
P80470
|
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: [Okehampton railway station, hasTouristTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTouristTraffic Context triple: [Okehampton railway station, hasTouristTraffic, yes]
-
A.
touristTraffic
chosen
Indicates the level, flow, or intensity of tourists visiting or moving through a particular place or area.
-
B.
hasTouristVisits
Indicates that one entity experiences or records visits from tourists to another entity.
-
C.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
-
D.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
E.
hasTouristPopularity
Indicates that a place or attraction is recognized as being popular or frequently visited by tourists.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44ff75538819083f77756d39a1aaa |
completed | April 19, 2026, 3:45 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.