Triple

T23583739
Position Surface form Disambiguated ID Type / Status
Subject Holocaust in Belarus E582277 entity
Predicate tookPlaceIn P40 FINISHED
Object Bobruisk Ghetto
The Bobruisk Ghetto was a World War II Jewish ghetto in Nazi-occupied Belarus where thousands of Jews were confined, exploited, and murdered as part of the Holocaust.
E1633531 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: Bobruisk Ghetto | Statement: [Holocaust in Belarus, tookPlaceIn, Bobruisk Ghetto]
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: Bobruisk Ghetto
Triple: [Holocaust in Belarus, tookPlaceIn, Bobruisk Ghetto]
Generated description
The Bobruisk Ghetto was a World War II Jewish ghetto in Nazi-occupied Belarus where thousands of Jews were confined, exploited, and murdered as part of the Holocaust.

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_69e248f8d8248190acd5aee77f0d1709 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b02e19808190a41bce305f6cd5da completed April 29, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe32ed3d08190a38a71c091e7a12a completed May 22, 2026, 5:01 a.m.
NEDg Description generation batch_6a0fe4dee88081909c792a3463ff3e45 completed May 22, 2026, 5:08 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe572cdd08190a613209dc88d5ad1 completed May 22, 2026, 5:11 a.m.
Created at: April 17, 2026, 6:40 p.m.