Triple
T8089005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin B-26 Marauder |
E188806
|
entity |
| Predicate | operatorTrainingIssue |
P80429
|
FINISHED |
| Object | initially high accident rate in training |
—
|
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: initially high accident rate in training | Statement: [Martin B-26 Marauder, operatorTrainingIssue, initially high accident rate in training]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatorTrainingIssue Context triple: [Martin B-26 Marauder, operatorTrainingIssue, initially high accident rate in training]
-
A.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
B.
trainingSupport
Indicates that one entity provides assistance, resources, or facilitation to help another entity conduct or participate in training activities.
-
C.
laterOperationalIssue
Indicates that an operational issue occurred at some point after a referenced time, event, or condition.
-
D.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
E.
trainOperator
Indicates that one entity operates, manages, or runs train services for another entity or within a specific rail system.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb421c717c819089dd88c30a6401aa |
completed | March 31, 2026, 3:40 a.m. |
| PD | Predicate disambiguation | batch_69cb04a14cd88190a79ed26cbeec1c33 |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14be17208190bb51c3dfcb613f20 |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:29 p.m.