Triple

T9789332
Position Surface form Disambiguated ID Type / Status
Subject Young Guns E237567 entity
Predicate editor P1954 FINISHED
Object Jack Hofstra
Jack Hofstra is a film editor known for his work on the Western action movie "Young Guns."
 E821521 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: Jack Hofstra | Statement: [Young Guns, editor, Jack Hofstra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jack Hofstra
Context triple: [Young Guns, editor, Jack Hofstra]
  • A. Thomas Blatt
    Thomas Blatt was a Polish Jewish Holocaust survivor, writer, and speaker best known for his escape from the Sobibor extermination camp and his later efforts to document its history.
  • B. David Frankfurter
    David Frankfurter was a Jewish medical student best known for assassinating Swiss Nazi leader Wilhelm Gustloff in 1936, an event later revisited in Günter Grass’s novella "Crabwalk."
  • C. Howard Gobioff
    Howard Gobioff was a computer scientist and early Google engineer best known as a co-author of the influential Google File System paper that helped shape modern distributed storage systems.
  • D. William Margulies
    William Margulies was an American cinematographer known for his work on mid-20th-century Hollywood films and television productions.
  • E. Daniel H. Lowenstein
    Daniel H. Lowenstein is a prominent American legal scholar known for his pioneering work in election law and political reform.
  • 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: Jack Hofstra
Triple: [Young Guns, editor, Jack Hofstra]
Generated description
Jack Hofstra is a film editor known for his work on the Western action movie "Young Guns."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jack Hofstra
Target entity description: Jack Hofstra is a film editor known for his work on the Western action movie "Young Guns."
  • A. Thomas Blatt
    Thomas Blatt was a Polish Jewish Holocaust survivor, writer, and speaker best known for his escape from the Sobibor extermination camp and his later efforts to document its history.
  • B. David Frankfurter
    David Frankfurter was a Jewish medical student best known for assassinating Swiss Nazi leader Wilhelm Gustloff in 1936, an event later revisited in Günter Grass’s novella "Crabwalk."
  • C. Howard Gobioff
    Howard Gobioff was a computer scientist and early Google engineer best known as a co-author of the influential Google File System paper that helped shape modern distributed storage systems.
  • D. William Margulies
    William Margulies was an American cinematographer known for his work on mid-20th-century Hollywood films and television productions.
  • E. Daniel H. Lowenstein
    Daniel H. Lowenstein is a prominent American legal scholar known for his pioneering work in election law and political reform.
  • 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_69ca84da927881909bda80caecad6010 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda214875481909f39e1d4dbac1fdb completed April 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c42c9fe081908145911cad6723c2 completed April 5, 2026, 2:08 a.m.
NEDg Description generation batch_69d1c4eb7a0481908bbd72f6d28d4746 completed April 5, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_69d1c5c0e6e88190bbf6eb379e6d1aa3 completed April 5, 2026, 2:15 a.m.
Created at: March 30, 2026, 8:27 p.m.