Triple
T19189323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Castleton Moor railway station |
E469787
|
entity |
| Predicate | usageCategory |
P134798
|
FINISHED |
| Object | low-usage 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: low-usage station | Statement: [Castleton Moor railway station, usageCategory, low-usage station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usageCategory Context triple: [Castleton Moor railway station, usageCategory, low-usage station]
-
A.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
B.
usageAmong
Indicates how frequently or in what manner something is used within a particular group, context, or population.
-
C.
commonUseCategory
Indicates that multiple entities share the same general category of use or functional purpose.
-
D.
usagePattern
Indicates how something is typically used or the recurring manner in which it is employed or consumed.
-
E.
categoryIUsedFor
Indicates that one entity is used as a category or classification label for another entity.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a01d08819081608a6ab8c6e705 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe9ef7081908a74a57d1fc731ea |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:07 p.m.