Triple

T18380022
Position Surface form Disambiguated ID Type / Status
Subject Ron Leibman E446417 entity
Predicate givenName P17 FINISHED
Object Ron
Ron Leibman was an American actor known for his work in film, television, and theater, including his Emmy-winning role in the series "Kaz" and his recurring role as Dr. Leonard Green on "Friends."
 E1321470 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: Ron | Statement: [Ron Leibman, givenName, Ron]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ron
Context triple: [Ron Leibman, givenName, Ron]
  • A. Ron
    Ron is a central character in the Harry Potter series, known as Harry Potter’s loyal best friend and a member of the Weasley family.
  • B. Ron
    Ron is a malfunctioning but endearing robot companion who forms an unlikely friendship with a socially awkward boy in the animated film "Ron's Gone Wrong."
  • C. Ron
    Ron is a person associated with Brewis, likely as a collaborator, colleague, or acquaintance.
  • D. Ron
    Ron is a West Chadic language spoken in parts of central Nigeria.
  • E. Ron
    Ron is a fictional assistant district attorney character, best known from the television series "Law & Order: Criminal Intent."
  • 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: Ron
Triple: [Ron Leibman, givenName, Ron]
Generated description
Ron Leibman was an American actor known for his work in film, television, and theater, including his Emmy-winning role in the series "Kaz" and his recurring role as Dr. Leonard Green on "Friends."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ron
Target entity description: Ron Leibman was an American actor known for his work in film, television, and theater, including his Emmy-winning role in the series "Kaz" and his recurring role as Dr. Leonard Green on "Friends."
  • A. Ron
    Ron is a fictional assistant district attorney character, best known from the television series "Law & Order: Criminal Intent."
  • B. Ron
    Ron is a character from the dark comedy-horror television series "Santa Clarita Diet," known for his involvement in the show's zombie-related chaos.
  • C. Ron
    Ron is a person associated with Brewis, likely as a collaborator, colleague, or acquaintance.
  • D. Ron
    Ron is the given name of Ron Dayne, a former American football running back and Heisman Trophy winner.
  • E. Ron
    Ron is the given name of American novelist Ron Currie Jr., known for his darkly comic and speculative fiction.
  • 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_69d8b9f370b88190b1e5081c2c238e7f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e5179aa328819097f5ed8193cfa401 completed April 19, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03d780a3bc8190a2ae02e8190dfb68 completed May 13, 2026, 1:44 a.m.
NEDg Description generation batch_6a03d8857ba081909a1bee74530523d0 completed May 13, 2026, 1:48 a.m.
NED2 Entity disambiguation (via description) batch_6a03da59c1b081908d9488233c611586 completed May 13, 2026, 1:56 a.m.
Created at: April 10, 2026, 10:45 a.m.