Triple

T23742115
Position Surface form Disambiguated ID Type / Status
Subject Snabba Cash E586703 entity
Predicate authorOfSourceWork P2353 FINISHED
Object Jens Lapidus
Jens Lapidus is a Swedish criminal defense lawyer turned bestselling crime novelist, best known for his gritty Stockholm Noir trilogy that began with "Snabba Cash" ("Easy Money").
E1634384 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: Jens Lapidus | Statement: [Snabba Cash, authorOfSourceWork, Jens Lapidus]
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: Jens Lapidus
Triple: [Snabba Cash, authorOfSourceWork, Jens Lapidus]
Generated description
Jens Lapidus is a Swedish criminal defense lawyer turned bestselling crime novelist, best known for his gritty Stockholm Noir trilogy that began with "Snabba Cash" ("Easy Money").

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_69e24908efb08190bf755c3a9b91f222 completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1bad734b08190a4b3365df97c73e1 completed April 29, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe33338488190b625688937f4a7a9 completed May 22, 2026, 5:01 a.m.
NEDg Description generation batch_6a0fe44e2f9c8190a16f81052341c70a completed May 22, 2026, 5:06 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe4e5d698819092a5d1b75f213ca0 completed May 22, 2026, 5:08 a.m.
Created at: April 17, 2026, 7:11 p.m.