Triple

T35196616
Position Surface form Disambiguated ID Type / Status
Subject Kriminal E1016278 entity
Predicate creatorRole P19360 FINISHED
Object Max Bunker was the writer of Kriminal
Max Bunker is an Italian comic book writer best known for creating influential noir and crime-themed series in the 1960s and 1970s.
 E2129891 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: Max Bunker was the writer of Kriminal | Statement: [Kriminal, creatorRole, Max Bunker was the writer of Kriminal]
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: Max Bunker was the writer of Kriminal
Triple: [Kriminal, creatorRole, Max Bunker was the writer of Kriminal]
Generated description
Max Bunker is an Italian comic book writer best known for creating influential noir and crime-themed series in the 1960s and 1970s.

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_69f76dde814c8190a71f60d514a424a4 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78e3148d8819098eb57e1671bf9c0 completed May 3, 2026, 6:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37fb2837f481909b87e33dd73acaf5 completed June 21, 2026, 2:54 p.m.
NEDg Description generation batch_6a37fbbd300c8190bae8823437872acf completed June 21, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_6a37fc86db2481909015f6fe63315f08 completed June 21, 2026, 3 p.m.
Created at: May 3, 2026, 4:02 p.m.