Triple

T32317181
Position Surface form Disambiguated ID Type / Status
Subject Roudnice nad Labem E825671 entity
Predicate hasRiverPort P18124 FINISHED
Object Elbe river port
The Elbe river port in Roudnice nad Labem is a regional inland port on the Elbe River that supports cargo transport and local economic activity in the Czech Republic.
 E2001416 NE 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: Elbe river port | Statement: [Roudnice nad Labem, hasRiverPort, Elbe river port]
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: Elbe river port
Triple: [Roudnice nad Labem, hasRiverPort, Elbe river port]
Generated description
The Elbe river port in Roudnice nad Labem is a regional inland port on the Elbe River that supports cargo transport and local economic activity in the Czech Republic.

Provenance (5 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_69f3491213b88190a57094d8697a7455 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bdbc092c81908d93b4c4a4899cde completed May 3, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a30571ba5cc819099ec2f08cbca1310 completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a305835ae0c8190b88773e510826a0d completed June 15, 2026, 7:53 p.m.
NED2 Entity disambiguation (via description) batch_6a3058b2245c8190811b6cdad0f18aa6 completed June 15, 2026, 7:55 p.m.
Created at: May 1, 2026, 12:46 a.m.