Triple
T24901368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thames Trains |
E623585
|
entity |
| Predicate | accidentTrainType |
P56947
|
FINISHED |
| Object | Class 165 diesel multiple unit |
—
|
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: Class 165 diesel multiple unit | Statement: [Thames Trains, accidentTrainType, Class 165 diesel multiple unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accidentTrainType Context triple: [Thames Trains, accidentTrainType, Class 165 diesel multiple unit]
-
A.
accidentType
Indicates the specific category or kind of accident associated with an event or incident.
-
B.
trainTypeUsed
chosen
Indicates that a specific type or category of train is employed or operated in a given context or service.
-
C.
trainsCategory
Indicates that one entity is a category or type under which the other entity is trained or classified.
-
D.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
E.
numberOfCarsDerailed
Indicates the count of cars that have come off the tracks in a derailment incident.
- 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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:27 a.m.