Triple

T19731994
Position Surface form Disambiguated ID Type / Status
Subject Laura Mackenzie Phillips E473876 entity
Predicate spouse P13 FINISHED
Object Jeffrey Sessler
Jeffrey Sessler is known as the former husband of American actress and singer Mackenzie Phillips.
 E1463080 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: Jeffrey Sessler | Statement: [Laura Mackenzie Phillips, spouse, Jeffrey Sessler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jeffrey Sessler
Context triple: [Laura Mackenzie Phillips, spouse, Jeffrey Sessler]
  • A. Michael D. Rosenthal
    Michael D. Rosenthal is a writer best known as the author whose work inspired the "Twilight Zone" episode "A Kind of Stopwatch."
  • B. David M. Rosenthal
    David M. Rosenthal is an American film director and screenwriter known for his work in thrillers and character-driven dramas.
  • C. Neil B. Shulman
    Neil B. Shulman is an American physician and author best known for writing the novel that inspired the film "Doc Hollywood."
  • D. Stephen H. Sachs
    Stephen H. Sachs is an American lawyer and politician who served as Maryland’s attorney general and was known for his work on legal reform and civil rights.
  • E. J. David Siegel
    J. David Siegel is a film editor known for his work on major animated features, including the superhero comedy "DC League of Super-Pets."
  • 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: Jeffrey Sessler
Triple: [Laura Mackenzie Phillips, spouse, Jeffrey Sessler]
Generated description
Jeffrey Sessler is known as the former husband of American actress and singer Mackenzie Phillips.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jeffrey Sessler
Target entity description: Jeffrey Sessler is known as the former husband of American actress and singer Mackenzie Phillips.
  • A. Michael D. Rosenthal
    Michael D. Rosenthal is a writer best known as the author whose work inspired the "Twilight Zone" episode "A Kind of Stopwatch."
  • B. David M. Rosenthal
    David M. Rosenthal is an American film director and screenwriter known for his work in thrillers and character-driven dramas.
  • C. Neil B. Shulman
    Neil B. Shulman is an American physician and author best known for writing the novel that inspired the film "Doc Hollywood."
  • D. Stephen H. Sachs
    Stephen H. Sachs is an American lawyer and politician who served as Maryland’s attorney general and was known for his work on legal reform and civil rights.
  • E. J. David Siegel
    J. David Siegel is a film editor known for his work on major animated features, including the superhero comedy "DC League of Super-Pets."
  • 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_69d8e517ebd48190979ee76723bcfadf completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e649fd18148190a6e85b2be0069dde completed April 20, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0947285210819089446cb626a46d4e completed May 17, 2026, 4:42 a.m.
NEDg Description generation batch_6a0947c3422c8190adecb8a91e3ebe31 completed May 17, 2026, 4:44 a.m.
NED2 Entity disambiguation (via description) batch_6a09489195b88190bdfb0c1b2d6d35e2 completed May 17, 2026, 4:48 a.m.
Created at: April 10, 2026, 1:47 p.m.