Triple

T31297161
Position Surface form Disambiguated ID Type / Status
Subject Gmina Zabłudów E798110 entity
Predicate containsSettlement P847 FINISHED
Object Kamionka
Kamionka is a village in north-eastern Poland that forms part of the rural administrative district of Gmina Zabłudów.
E1955826 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: Kamionka | Statement: [Gmina Zabłudów, containsSettlement, Kamionka]
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: Kamionka
Triple: [Gmina Zabłudów, containsSettlement, Kamionka]
Generated description
Kamionka is a village in north-eastern Poland that forms part of the rural administrative district of Gmina Zabłudów.

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_69f224e0bd4c8190aab9b29a73f7aa3c completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69e31c0ac8190a39cff8445b2ede0 completed May 3, 2026, 1 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2a1e3587f481908193afc90f7a58ed completed June 11, 2026, 2:32 a.m.
NEDg Description generation batch_6a2a4b60d4d08190b023c1f5f7ca20e9 completed June 11, 2026, 5:45 a.m.
NED2 Entity disambiguation (via description) batch_6a2a4bcf52188190a36280326293f399 completed June 11, 2026, 5:46 a.m.
Created at: April 29, 2026, 9:14 p.m.