Triple

T34962671

Position	Surface form	Disambiguated ID	Type / Status
Subject	Могилёв-Подольский	`E1008301`	entity
Predicate	имеетЖелезнодорожноеСообщениеС	`P71547`	FINISHED
Object	Винница Винница — крупный город в центральной Украине на берегу Южного Буга, являющийся административным центром Винницкой области и важным промышленным и культурным узлом региона.	`E2118689`	NE FINISHED

How this triple was built (3 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: Винница | Statement: [Могилёв-Подольский, имеетЖелезнодорожноеСообщениеС, Винница]

NEDg Description generation gpt-5.1

Instruction

Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"

Input

Entity: Винница
Triple: [Могилёв-Подольский, имеетЖелезнодорожноеСообщениеС, Винница]

Generated description

Винница — крупный город в центральной Украине на берегу Южного Буга, являющийся административным центром Винницкой области и важным промышленным и культурным узлом региона.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: имеетЖелезнодорожноеСообщениеС
Context triple: [Могилёв-Подольский, имеетЖелезнодорожноеСообщениеС, Винница]

A. usesRailInfrastructureOf
Indicates that one entity operates on, accesses, or otherwise makes use of the rail infrastructure owned or managed by another entity.
B. hasPassengerRailConnection chosen
Indicates that there exists a passenger rail service linking one location or transport node to another.
C. connectsToRailwayNetwork
Indicates that one entity is physically or functionally linked to a railway network, allowing access or transfer between them.
D. hasRailSystem
Indicates that an entity possesses or is served by a rail-based transportation system.
E. railroadMet
Indicates that two or more railroads encountered or connected with each other at a specific place or time.
F. None of above.

Provenance (6 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_69f76dc69564819099e9e78aed6ff0a6`	completed	May 3, 2026, 3:46 p.m.
NER	Named-entity recognition	`batch_69f78710282c81909146dc0be91e983f`	completed	May 3, 2026, 5:34 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a37a8d1d7b08190899394500c164e68`	completed	June 21, 2026, 9:03 a.m.
NEDg	Description generation	`batch_6a37a9f0384c81908f981df56bb84816`	completed	June 21, 2026, 9:08 a.m.
NED2	Entity disambiguation (via description)	`batch_6a37aa70361481909038190d43bc3a72`	completed	June 21, 2026, 9:10 a.m.
PD	Predicate disambiguation	`batch_69f784162134819098413482ef52042f`	completed	May 3, 2026, 5:21 p.m.

Created at: May 3, 2026, 4 p.m.