Triple

T1790536
Position Surface form Disambiguated ID Type / Status
Subject The Martian E39484 entity
Predicate character P662 FINISHED
Object Teddy Sanders
Teddy Sanders is the cautious yet politically minded NASA Administrator in Andy Weir’s science fiction novel (and its film adaptation) "The Martian."
 E214683 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: Teddy Sanders | Statement: [The Martian, character, Teddy Sanders]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teddy Sanders
Context triple: [The Martian, character, Teddy Sanders]
  • A. Jim Tunney
    Jim Tunney is a former NFL official renowned as one of the league’s most respected referees, often called the “Dean of NFL Referees.”
  • B. Tom Mason
    Tom Mason is the former history professor turned resistance leader who serves as the central protagonist in the post-apocalyptic alien invasion series "Falling Skies."
  • C. David M. Barkley
    David M. Barkley was the son of U.S. Vice President Alben W. Barkley and a member of the prominent Barkley political family.
  • D. Larry Blanford
    Larry Blanford is a professional cinematographer known for his work on feature films such as the romantic comedy "Think Like a Man."
  • E. Ted Cheesman
    Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
  • 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: Teddy Sanders
Triple: [The Martian, character, Teddy Sanders]
Generated description
Teddy Sanders is the cautious yet politically minded NASA Administrator in Andy Weir’s science fiction novel (and its film adaptation) "The Martian."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teddy Sanders
Target entity description: Teddy Sanders is the cautious yet politically minded NASA Administrator in Andy Weir’s science fiction novel (and its film adaptation) "The Martian."
  • A. Jim Tunney
    Jim Tunney is a former NFL official renowned as one of the league’s most respected referees, often called the “Dean of NFL Referees.”
  • B. Tom Mason
    Tom Mason is the former history professor turned resistance leader who serves as the central protagonist in the post-apocalyptic alien invasion series "Falling Skies."
  • C. David M. Barkley
    David M. Barkley was the son of U.S. Vice President Alben W. Barkley and a member of the prominent Barkley political family.
  • D. Larry Blanford
    Larry Blanford is a professional cinematographer known for his work on feature films such as the romantic comedy "Think Like a Man."
  • E. Ted Cheesman
    Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
  • 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_69adf3bef0648190a7fd6ae38b41038e completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf471909881909de20d9d1fa0b372 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf50b17a081909b93ad3e08c71772 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:32 p.m.