Triple

T32035162
Position Surface form Disambiguated ID Type / Status
Subject Baran bo Odar E818075 entity
Predicate notableWork P4 FINISHED
Object Who Am I – No System Is Safe
Who Am I – No System Is Safe is a 2014 German techno-thriller film about a young hacker drawn into a dangerous cybercrime group and international intrigue.
 E1989330 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: Who Am I – No System Is Safe | Statement: [Baran bo Odar, notableWork, Who Am I – No System Is Safe]
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: Who Am I – No System Is Safe
Triple: [Baran bo Odar, notableWork, Who Am I – No System Is Safe]
Generated description
Who Am I – No System Is Safe is a 2014 German techno-thriller film about a young hacker drawn into a dangerous cybercrime group and international intrigue.

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_69f348fbc8148190b3c0f95d4772b153 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b49a18748190bfc7061da3eebef6 completed May 3, 2026, 2:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4f44cd4819096bffbbf8e02a448 completed June 14, 2026, 4:21 p.m.
NEDg Description generation batch_6a2ed5e9ddf48190b24ecf2d8ccd7a61 completed June 14, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed6af3b1081909c776164b167583a completed June 14, 2026, 4:28 p.m.
Created at: May 1, 2026, 12:18 a.m.