Triple

T17024355
Position Surface form Disambiguated ID Type / Status
Subject Project A E413024 entity
Predicate stars P1956 FINISHED
Object Mars (actor)
Mars is a Japanese actor known for his supporting and character roles in film and television.
 E1245666 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: Mars (actor) | Statement: [Project A, stars, Mars (actor)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mars (actor)
Context triple: [Project A, stars, Mars (actor)]
  • A. David Mars
    David Mars is an American venture capitalist and technology investor known for his work in growth-stage companies and his marriage to actress and singer Patina Miller.
  • B. Lance Kerwin
    Lance Kerwin was an American actor best known for his prominent roles in 1970s television dramas and horror projects, particularly as a teen protagonist.
  • C. Murray Hamilton
    Murray Hamilton was an American character actor best known for playing the stubborn Mayor Larry Vaughn in the classic thriller film "Jaws."
  • D. Jeff Morrow
    Jeff Morrow was an American actor best known for his roles in mid-20th-century films and television, particularly in biblical epics and science fiction movies.
  • E. Chris Hargensen
    Chris Hargensen is a central antagonist in Stephen King’s novel "Carrie," known as a cruel high school bully whose actions help trigger the story’s catastrophic climax.
  • 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: Mars (actor)
Triple: [Project A, stars, Mars (actor)]
Generated description
Mars is a Japanese actor known for his supporting and character roles in film and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mars (actor)
Target entity description: Mars is a Japanese actor known for his supporting and character roles in film and television.
  • A. David Mars
    David Mars is an American venture capitalist and technology investor known for his work in growth-stage companies and his marriage to actress and singer Patina Miller.
  • B. Lance Kerwin
    Lance Kerwin was an American actor best known for his prominent roles in 1970s television dramas and horror projects, particularly as a teen protagonist.
  • C. Murray Hamilton
    Murray Hamilton was an American character actor best known for playing the stubborn Mayor Larry Vaughn in the classic thriller film "Jaws."
  • D. Jeff Morrow
    Jeff Morrow was an American actor best known for his roles in mid-20th-century films and television, particularly in biblical epics and science fiction movies.
  • E. Chris Hargensen
    Chris Hargensen is a central antagonist in Stephen King’s novel "Carrie," known as a cruel high school bully whose actions help trigger the story’s catastrophic climax.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d371148190a60d32a72abec09a completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a011b514de481909c78c17a3014b468 completed May 10, 2026, 11:57 p.m.
NEDg Description generation batch_6a011c021e1c819098e04b1cbaf33ecd completed May 11, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a011c8afb608190b51c7a4c9ccaa0a5 completed May 11, 2026, 12:02 a.m.
Created at: April 10, 2026, 5:33 a.m.