Triple

T27174382
Position Surface form Disambiguated ID Type / Status
Subject Gedenkstätte Günter Litfin E683002 entity
Predicate dedicatedTo P500 FINISHED
Object Günter Litfin
Günter Litfin was an East German tailor who became one of the first people killed while attempting to escape over the Berlin Wall, later commemorated as a symbol of victims of the inner-German border.
E1969098 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: Günter Litfin | Statement: [Gedenkstätte Günter Litfin, dedicatedTo, Günter Litfin]
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: Günter Litfin
Triple: [Gedenkstätte Günter Litfin, dedicatedTo, Günter Litfin]
Generated description
Günter Litfin was an East German tailor who became one of the first people killed while attempting to escape over the Berlin Wall, later commemorated as a symbol of victims of the inner-German border.

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_69eefad086808190ab89816c0c300476 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6254a0a9c8190a5491c785635ca35 completed May 2, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2b560e2e5481908a5112fcb3d5905b completed June 12, 2026, 12:42 a.m.
NEDg Description generation batch_6a2b5740b1e88190af5cf79a09800fc8 completed June 12, 2026, 12:48 a.m.
NED2 Entity disambiguation (via description) batch_6a2b5a4b8a9c8190a33f1916d94b808f completed June 12, 2026, 1 a.m.
Created at: April 27, 2026, 9:25 a.m.