Triple
T15733593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DAUNTLESS |
E381407
|
entity |
| Predicate | safetyRelevance |
P30182
|
FINISHED |
| Object | air traffic control coordination |
—
|
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: air traffic control coordination | Statement: [DAUNTLESS, safetyRelevance, air traffic control coordination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyRelevance Context triple: [DAUNTLESS, safetyRelevance, air traffic control coordination]
-
A.
safetyRelevant
chosen
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
B.
safetyImplication
Indicates that one entity has a consequence, effect, or relevance for the safety or risk level associated with another entity or situation.
-
C.
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.
-
D.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
-
E.
safetyCategory
Indicates the classification of something according to its level or type of safety.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.