Triple

T20808032
Position Surface form Disambiguated ID Type / Status
Subject Nick Grinde E512215 entity
Predicate notableWork P4 FINISHED
Object Before I Hang
Before I Hang is a 1940 American horror film starring Boris Karloff as a scientist whose life-extension experiments lead to murderous consequences.
 E1451653 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: Before I Hang | Statement: [Nick Grinde, notableWork, Before I Hang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Before I Hang
Context triple: [Nick Grinde, notableWork, Before I Hang]
  • A. Hangin’ In
    Hangin’ In is a Canadian television sitcom from the early 1980s that follows the staff and clients of a youth drop-in center.
  • B. Hangin’ Around
    "Hangin’ Around" is a song by country artist Eric Church from his album "Desperate Man."
  • C. Hanging Up
    Hanging Up is a 2000 American comedy-drama film about three sisters coping with their aging father and their own complicated relationships, directed by and starring Diane Keaton.
  • D. Hanging Around
    "Hanging Around" is a song by the American rock band Wiggle.
  • E. Dangling Man
    Dangling Man is Saul Bellow’s debut novel, a philosophical first-person narrative about an unemployed young man in Chicago awaiting his World War II draft and grappling with alienation and moral uncertainty.
  • 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: Before I Hang
Triple: [Nick Grinde, notableWork, Before I Hang]
Generated description
Before I Hang is a 1940 American horror film starring Boris Karloff as a scientist whose life-extension experiments lead to murderous consequences.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Before I Hang
Target entity description: Before I Hang is a 1940 American horror film starring Boris Karloff as a scientist whose life-extension experiments lead to murderous consequences.
  • A. Hangin’ In
    Hangin’ In is a Canadian television sitcom from the early 1980s that follows the staff and clients of a youth drop-in center.
  • B. Hangin’ Around
    "Hangin’ Around" is a song by country artist Eric Church from his album "Desperate Man."
  • C. Hanging Up
    Hanging Up is a 2000 American comedy-drama film about three sisters coping with their aging father and their own complicated relationships, directed by and starring Diane Keaton.
  • D. Hanging Around
    "Hanging Around" is a song by the American rock band Wiggle.
  • E. Dangling Man
    Dangling Man is Saul Bellow’s debut novel, a philosophical first-person narrative about an unemployed young man in Chicago awaiting his World War II draft and grappling with alienation and moral uncertainty.
  • 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2d0a2a081908fb0e3d890e87aaf completed April 21, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08f8c979d481909dfa2aa97b1cefab completed May 16, 2026, 11:07 p.m.
NEDg Description generation batch_6a08f9deb9248190af178b5c28728e00 completed May 16, 2026, 11:12 p.m.
NED2 Entity disambiguation (via description) batch_6a08fa80202c8190929f8f1b47d6fae4 completed May 16, 2026, 11:15 p.m.
Created at: April 16, 2026, 12:40 p.m.