Triple

T32761207
Position Surface form Disambiguated ID Type / Status
Subject Pioneer (2013 film) E837759 entity
Predicate starring P1507 FINISHED
Object Eirik Stubø
Eirik Stubø is a Norwegian theatre director and actor known primarily for his work in Scandinavian stage productions.
E2088736 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: Eirik Stubø | Statement: [Pioneer (2013 film), starring, Eirik Stubø]
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: Eirik Stubø
Triple: [Pioneer (2013 film), starring, Eirik Stubø]
Generated description
Eirik Stubø is a Norwegian theatre director and actor known primarily for his work in Scandinavian stage productions.

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_69f34939857c8190aa9970c51feec1eb completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cce41fd08190ab90130d3beabb47 completed May 3, 2026, 4:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e5ff27288190a142e460255e3e64 completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36e75dafb081908f1aafe1fdc60cb6 completed June 20, 2026, 7:17 p.m.
NED2 Entity disambiguation (via description) batch_6a36e7b750708190bb913e8368704235 completed June 20, 2026, 7:19 p.m.
Created at: May 1, 2026, 1:13 a.m.