Triple

T23770816
Position Surface form Disambiguated ID Type / Status
Subject Polish Baltic coast E587520 entity
Predicate hasPort P35 FINISHED
Object Port of Kołobrzeg
The Port of Kołobrzeg is a seaport in the city of Kołobrzeg in northwestern Poland, serving as a regional hub for maritime trade, fishing, and tourism on the Baltic Sea.
E1619654 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: Port of Kołobrzeg | Statement: [Polish Baltic coast, hasPort, Port of Kołobrzeg]
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: Port of Kołobrzeg
Triple: [Polish Baltic coast, hasPort, Port of Kołobrzeg]
Generated description
The Port of Kołobrzeg is a seaport in the city of Kołobrzeg in northwestern Poland, serving as a regional hub for maritime trade, fishing, and tourism on the Baltic Sea.

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_69e2490d245881909028226a1393d624 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1c465d7948190a4381e39f792a7b4 completed April 29, 2026, 8:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0facef46e88190bb190f64770b7e1b completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fae10893c819092a3ecd95b6b9198 completed May 22, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf36d68881909ac3b5d6328efc8f completed May 22, 2026, 1:19 a.m.
Created at: April 17, 2026, 7:15 p.m.