Triple

T1790530
Position Surface form Disambiguated ID Type / Status
Subject The Martian E39484 entity
Predicate castMember P1668 FINISHED
Object Aksel Hennie
Aksel Hennie is a Norwegian actor and filmmaker known internationally for roles in films such as "Headhunters" and the sci-fi drama "The Martian."
 E207677 NE FINISHED

How this triple was built (4 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: Aksel Hennie | Statement: [The Martian, castMember, Aksel Hennie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aksel Hennie
Context triple: [The Martian, castMember, Aksel Hennie]
  • A. Rune Gerhardsen
    Rune Gerhardsen is a Norwegian Labour Party politician and former Oslo city council leader, known as the son of long-serving prime minister Einar Gerhardsen.
  • B. Niels Torp
    Niels Torp is a Norwegian architect known for designing prominent public and commercial buildings in Norway and abroad.
  • C. Nils Lie
    Nils Lie was a Norwegian judge and legal scholar known for his contributions to Norway’s judicial system in the early 20th century.
  • D. Andreas Roald
    Andreas Roald is a film producer known for his work on the period drama "Effie Gray."
  • E. Henrik Christensen
    Henrik Christensen is a prominent robotics researcher and academic known for his influential contributions to computer vision, autonomous systems, and robotics education.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Aksel Hennie
Triple: [The Martian, castMember, Aksel Hennie]
Generated description
Aksel Hennie is a Norwegian actor and filmmaker known internationally for roles in films such as "Headhunters" and the sci-fi drama "The Martian."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aksel Hennie
Target entity description: Aksel Hennie is a Norwegian actor and filmmaker known internationally for roles in films such as "Headhunters" and the sci-fi drama "The Martian."
  • A. Rune Gerhardsen
    Rune Gerhardsen is a Norwegian Labour Party politician and former Oslo city council leader, known as the son of long-serving prime minister Einar Gerhardsen.
  • B. Niels Torp
    Niels Torp is a Norwegian architect known for designing prominent public and commercial buildings in Norway and abroad.
  • C. Nils Lie
    Nils Lie was a Norwegian judge and legal scholar known for his contributions to Norway’s judicial system in the early 20th century.
  • D. Andreas Roald
    Andreas Roald is a film producer known for his work on the period drama "Effie Gray."
  • E. Henrik Christensen
    Henrik Christensen is a prominent robotics researcher and academic known for his influential contributions to computer vision, autonomous systems, and robotics education.
  • F. None of above. chosen

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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa6512804c8190a5743c10bd37f83f completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1b8e26c8190af6e45265e2b182f completed March 8, 2026, 7:44 p.m.
NEDg Description generation batch_69add29b34048190bee7908ac6c650e4 completed March 8, 2026, 7:48 p.m.
NED2 Entity disambiguation (via description) batch_69add35731588190a13c969490ca2c09 completed March 8, 2026, 7:51 p.m.
Created at: March 4, 2026, 7:32 p.m.