Triple
T8369315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vietnam Railways network |
E197412
|
entity |
| Predicate | safetyChallenge |
P82346
|
FINISHED |
| Object | level crossing accidents |
—
|
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: level crossing accidents | Statement: [Vietnam Railways network, safetyChallenge, level crossing accidents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyChallenge Context triple: [Vietnam Railways network, safetyChallenge, level crossing accidents]
-
A.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
B.
safetyDemonstration
Indicates that an entity performs or provides a demonstration intended to explain or show safety procedures, precautions, or protocols to others.
-
C.
safetyGoal
Indicates that an entity is associated with a specific safety objective or target condition intended to prevent harm or reduce risk.
-
D.
safetyPoints
Indicates a relationship where an entity is assigned or associated with a measure of safety, typically quantified as points reflecting its safety level or performance.
-
E.
safetyCategory
Indicates the classification of something according to its level or type of safety.
- 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_69ca82f56730819080cec5d991c76f4c |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb808f7c0481909fef5834cb6e7a3e |
completed | March 31, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69cb70cd04b08190ab5f72afd22a7967 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76d823b08190a54fadb50660cda5 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 6:01 p.m.