Triple
T30812556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moscow central tariff zone |
E784684
|
entity |
| Predicate | appliesToPassengerCategory |
P138413
|
FINISHED |
| Object | adult passengers |
—
|
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: adult passengers | Statement: [Moscow central tariff zone, appliesToPassengerCategory, adult passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToPassengerCategory Context triple: [Moscow central tariff zone, appliesToPassengerCategory, adult passengers]
-
A.
appliesToPassengerType
chosen
Indicates that a rule, condition, or attribute is relevant or restricted to a specific type or category of passenger.
-
B.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
C.
isPassengerWith
Indicates that one entity is traveling together with another entity as a passenger in the same vehicle or conveyance.
-
D.
isInPassengerService
Indicates that an entity (such as a vehicle, vessel, or aircraft) is currently being used to carry passengers as part of regular service.
-
E.
fareAppliesTo
Indicates that a specific fare is applicable to a particular trip, service, passenger category, or travel condition.
- 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_69f224b4eda48190bd212ce4f3901e56 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe031bc6208190860099aef72d8dcb |
completed | May 8, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69fe014c8b388190b5d4e0cb95ee2be5 |
completed | May 8, 2026, 3:29 p.m. |
Created at: April 29, 2026, 8:43 p.m.