Triple
T20251004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colgan Air |
E498553
|
entity |
| Predicate | safetyReputationImpact |
P107603
|
FINISHED |
| Object | affected by Flight 3407 crash |
—
|
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: affected by Flight 3407 crash | Statement: [Colgan Air, safetyReputationImpact, affected by Flight 3407 crash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyReputationImpact Context triple: [Colgan Air, safetyReputationImpact, affected by Flight 3407 crash]
-
A.
hasSafetyReputation
chosen
Indicates that one entity is associated with an assessment or record of safety-related reliability or trustworthiness.
-
B.
securityReputation
Indicates the assessed trustworthiness or risk level associated with an entity’s security posture or behavior.
-
C.
institutionalReputationContext
Indicates the situational or environmental factors that shape or influence an institution’s reputation.
-
D.
associatedWithReputation
Indicates a relationship where an entity is linked to, influenced by, or characterized in terms of another entity’s reputation or perceived standing.
-
E.
defensiveReputation
Indicates that an entity is regarded or recognized as being strong, reliable, or skilled in defense.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a79a208190a5a7c0f6515bc393 |
completed | April 20, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:41 p.m.