Triple
T25836409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genthin rail disaster of 1939 |
E650812
|
entity |
| Predicate | involvedTrain |
P169900
|
FINISHED |
| Object | D 10 express train |
—
|
NE NERFINISHED |
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: D 10 express train | Statement: [Genthin rail disaster of 1939, involvedTrain, D 10 express train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedTrain Context triple: [Genthin rail disaster of 1939, involvedTrain, D 10 express train]
-
A.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
B.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
-
C.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
D.
trainsInDiscipline
Indicates that one entity undergoes training or instruction within a particular discipline, field, or area of expertise associated with another entity.
-
E.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training 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_69e7ab37438081908f1ccf6284839520 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f688d015908190ad5df37030ecf332 |
completed | May 2, 2026, 11:29 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f688034580819086a0f9100645f8ba |
completed | May 2, 2026, 11:25 p.m. |
Created at: April 22, 2026, 7:43 a.m.