Triple

T36323120
Position Surface form Disambiguated ID Type / Status
Subject Rig 45 E894388 entity
Predicate castMember P1668 FINISHED
Object Mads Sjøgård Pettersen
Mads Sjøgård Pettersen is a Norwegian actor known for his roles in Nordic film and television productions.
E2271838 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: Mads Sjøgård Pettersen | Statement: [Rig 45, castMember, Mads Sjøgård Pettersen]
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: Mads Sjøgård Pettersen
Triple: [Rig 45, castMember, Mads Sjøgård Pettersen]
Generated description
Mads Sjøgård Pettersen is a Norwegian actor known for his roles in Nordic film and television 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_69f76e4d1a788190a6ab6ccca28547a7 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba467ccc8190b1f0c0d99ec6790f completed May 3, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41cc8b15d08190a8c56e9ec6343039 completed June 29, 2026, 1:38 a.m.
NEDg Description generation batch_6a41cdab97bc8190a6fef8d57f05a86e completed June 29, 2026, 1:43 a.m.
NED2 Entity disambiguation (via description) batch_6a41ce4cee4481909d34941327630fb7 completed June 29, 2026, 1:45 a.m.
Created at: May 3, 2026, 4:09 p.m.