Triple
T7821519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Truro railway station |
E181138
|
entity |
| Predicate | hasCafesOrShops |
P24664
|
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: [Truro railway station, hasCafesOrShops, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCafesOrShops Context triple: [Truro railway station, hasCafesOrShops, yes]
-
A.
hasCafes
chosen
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
B.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
C.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
-
D.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
E.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
- 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_69ca828153f48190bdb27ac46f8e0745 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cafa083fe88190a77efb7cfee4bd6f |
completed | March 30, 2026, 10:32 p.m. |
| PD | Predicate disambiguation | batch_69cae91ae008819098e56bbe51143b31 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:41 p.m.