Triple

T3237171
Position Surface form Disambiguated ID Type / Status
Subject Public Speaking E67881 entity
Predicate editedBy P1954 FINISHED
Object David Tedeschi
David Tedeschi is a film editor and director best known for his long-running collaboration with Martin Scorsese on documentaries and concert films.
 E98621 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: David Tedeschi | Statement: [Public Speaking, editedBy, David Tedeschi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: David Tedeschi
Context triple: [Public Speaking, editedBy, David Tedeschi]
  • A. David Tedeschi
    David Tedeschi is an American film editor and director best known for his extensive collaborations with Martin Scorsese on documentaries and concert films.
  • B. John Bloom
    John Bloom is a film editor best known for his Academy Award-winning work on movies such as "Gandhi" and his editing contributions to notable films including "Charlie Wilson's War."
  • C. Jim James
    Jim James is an American singer, songwriter, and guitarist best known as the frontman of the rock band My Morning Jacket.
  • D. John Stanier
    John Stanier is a cinematographer best known for his work on major action films such as "Rambo III."
  • E. Matt Messina
    Matt Messina is an American film and television composer best known for his award-winning score for the movie "Juno."
  • 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: David Tedeschi
Triple: [Public Speaking, editedBy, David Tedeschi]
Generated description
David Tedeschi is a film editor and director best known for his long-running collaboration with Martin Scorsese on documentaries and concert films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: David Tedeschi
Target entity description: David Tedeschi is a film editor and director best known for his long-running collaboration with Martin Scorsese on documentaries and concert films.
  • A. David Tedeschi chosen
    David Tedeschi is an American film editor and director best known for his extensive collaborations with Martin Scorsese on documentaries and concert films.
  • B. John Bloom
    John Bloom is a film editor best known for his Academy Award-winning work on movies such as "Gandhi" and his editing contributions to notable films including "Charlie Wilson's War."
  • C. Jim James
    Jim James is an American singer, songwriter, and guitarist best known as the frontman of the rock band My Morning Jacket.
  • D. John Stanier
    John Stanier is a cinematographer best known for his work on major action films such as "Rambo III."
  • E. Matt Messina
    Matt Messina is an American film and television composer best known for his award-winning score for the movie "Juno."
  • F. None of above.

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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef29bf48190a9aa3a39f0138428 completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2774a97c481908be820bc5ddf786d completed March 12, 2026, 8:20 a.m.
NEDg Description generation batch_69b2780e41e0819080ddb26668f32838 completed March 12, 2026, 8:23 a.m.
NED2 Entity disambiguation (via description) batch_69b27bd238a48190b9d13ee8a8bc955d completed March 12, 2026, 8:39 a.m.
Created at: March 8, 2026, 3:08 p.m.