Triple
T36632805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peterborough North |
E904371
|
entity |
| Predicate | hadStationType |
P186105
|
FINISHED |
| Object | through station |
—
|
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: through station | Statement: [Peterborough North, hadStationType, through station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadStationType Context triple: [Peterborough North, hadStationType, through station]
-
A.
hasStationTypeAt
Indicates that a specific type of station is present or assigned at a particular location or point.
-
B.
hasStationHouseType
Indicates the specific type or classification of a station house associated with an entity.
-
C.
hasStationAt
Indicates that an entity maintains or operates a station located at a specified place.
-
D.
hadStationHotel
Indicates that a railway station possessed or was associated with a hotel facility serving its passengers or operations.
-
E.
hasStationBuilding
Indicates that a station is associated with or includes a station building as part of its facilities.
- 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_69f76e6c63e48190b1d0c3a79a6c7406 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c83f5960819089610ed39c839678 |
completed | May 3, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
| PDg | Predicate description generation | batch_69f7c776b4088190bef550c869da530d |
completed | May 3, 2026, 10:08 p.m. |
Created at: May 3, 2026, 4:11 p.m.