Triple

T10121314
Position Surface form Disambiguated ID Type / Status
Subject Sue Snell E223295 entity
Predicate opposes P437 FINISHED
Object Chris Hargensen E137397 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: Chris Hargensen | Statement: [Sue Snell, opposes, Chris Hargensen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chris Hargensen
Context triple: [Sue Snell, opposes, Chris Hargensen]
  • A. Chris Hargensen chosen
    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.
  • 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. Glen Bateman
    Glen Bateman is a retired sociology professor and key survivor character in Stephen King's post-apocalyptic novel "The Stand," known for his analytical perspective on the collapse and rebuilding of society.
  • 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. Fred Ward
    Fred Ward was an American character actor known for his rugged, everyman roles in films such as "Tremors," "The Right Stuff," and "Short Cuts."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca8422047c81909d66b717b8b18cf3 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd266b18c8190b35fe637c912e756 completed April 2, 2026, 2:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69d8dbe24a64819098bc94a8a62cc46b completed April 10, 2026, 11:15 a.m.
Created at: March 30, 2026, 9:04 p.m.