Triple
T21414695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 385 |
E528268
|
entity |
| Predicate | hasCabDesignIssue |
P41290
|
FINISHED |
| Object | initial units had windscreen distortion problems |
—
|
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: initial units had windscreen distortion problems | Statement: [British Rail Class 385, hasCabDesignIssue, initial units had windscreen distortion problems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCabDesignIssue Context triple: [British Rail Class 385, hasCabDesignIssue, initial units had windscreen distortion problems]
-
A.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
B.
hasDesign
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with another entity.
-
C.
hasInternalIssue
Indicates that an entity is experiencing a problem, fault, or malfunction originating within itself or its internal components or processes.
-
D.
designIssue
chosen
Indicates that there is a problem, flaw, or concern related to the design of an entity or system.
-
E.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b2032fe48190907b282e2fffa2bd |
completed | April 22, 2026, 11:33 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:45 p.m.