Triple
T161001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RideKC bus system |
E3284
|
entity |
| Predicate | safetyPartner |
P957
|
FINISHED |
| Object | local law enforcement agencies in the Kansas City region |
—
|
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: local law enforcement agencies in the Kansas City region | Statement: [RideKC bus system, safetyPartner, local law enforcement agencies in the Kansas City region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyPartner Context triple: [RideKC bus system, safetyPartner, local law enforcement agencies in the Kansas City region]
-
A.
hasPartner
Indicates that one entity is in a partner relationship (such as romantic, life, or business partnership) with another entity.
-
B.
protectedBy
chosen
Indicates that one entity provides protection, defense, or safeguarding for another entity.
-
C.
protects
Indicates taking action to keep someone or something safe from harm, danger, or negative effects.
-
D.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
E.
businessPartner
Indicates a formal collaborative relationship between two entities that work together in a business context, typically sharing responsibilities, risks, or benefits.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25856d934819095460b2ea566eb6b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.