Triple
T8422896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bicester North railway station |
E198903
|
entity |
| Predicate | hasLoungeOrCafe |
P82097
|
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: [Bicester North railway station, hasLoungeOrCafe, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoungeOrCafe Context triple: [Bicester North railway station, hasLoungeOrCafe, yes]
-
A.
hasLoungeType
Indicates that an entity is associated with, or classified by, a particular type or category of lounge.
-
B.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
C.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
D.
hasLoungeBrand
Indicates that an entity is associated with, or operates under, a particular lounge brand.
-
E.
hasDiningFeature
Indicates that something possesses a specific characteristic, amenity, or attribute related to dining.
- F. None of above. chosen
Provenance (4 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb859f787481908a11797a317c8849 |
completed | March 31, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69cb70d70ea081909c3dc1bd2ec14f85 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb77690720819099de1e22b84a9563 |
completed | March 31, 2026, 7:27 a.m. |
Created at: March 30, 2026, 6:06 p.m.