Triple
T15349210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CITY |
E367005
|
entity |
| Predicate | safetyCriticalUse |
P30182
|
FINISHED |
| Object | air traffic management |
—
|
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 management | Statement: [CITY, safetyCriticalUse, air traffic management]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyCriticalUse Context triple: [CITY, safetyCriticalUse, air traffic management]
-
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.
safetyRequirement
Indicates that one entity specifies or imposes conditions, standards, or measures necessary to ensure the safety of another entity or activity.
-
C.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
D.
safetyContext
Indicates the circumstances, conditions, or environment that affect how safe an action, object, or situation is.
-
E.
measuresSafetyUsing
Indicates that an entity evaluates or assesses safety by employing a specified method, tool, or standard.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e27f8a88190a0f65756e3a1fdfc |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:17 a.m.