Triple

T8891278
Position Surface form Disambiguated ID Type / Status
Subject Crocker E211680 entity
Predicate hasNotableBearer P458 FINISHED
Object Lawrence Crocker
Lawrence Crocker is a philosopher known for his work in legal philosophy, ethics, and the theory of punishment.
 E766589 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: Lawrence Crocker | Statement: [Crocker, hasNotableBearer, Lawrence Crocker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lawrence Crocker
Context triple: [Crocker, hasNotableBearer, Lawrence Crocker]
  • A. Leland Palmer
    Leland Palmer is an American actress, singer, and dancer best known for her work in musical theatre and film during the 1960s and 1970s.
  • B. David Huddleston
    David Huddleston was an American character actor best known for his memorable supporting roles in film and television, including the title role in the cult classic comedy "The Big Lebowski."
  • C. J. Douglas Brown
    J. Douglas Brown was an American economist and academic who played a key role in shaping U.S. Social Security policy during the New Deal era.
  • D. Red Miller
    Red Miller was an American football coach best known for leading the Denver Broncos to their first Super Bowl appearance in the 1977 season.
  • E. Charles Noland
    Charles Noland is an American actor known for his character roles in film, television, and theater.
  • 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: Lawrence Crocker
Triple: [Crocker, hasNotableBearer, Lawrence Crocker]
Generated description
Lawrence Crocker is a philosopher known for his work in legal philosophy, ethics, and the theory of punishment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lawrence Crocker
Target entity description: Lawrence Crocker is a philosopher known for his work in legal philosophy, ethics, and the theory of punishment.
  • A. Leland Palmer
    Leland Palmer is an American actress, singer, and dancer best known for her work in musical theatre and film during the 1960s and 1970s.
  • B. David Huddleston
    David Huddleston was an American character actor best known for his memorable supporting roles in film and television, including the title role in the cult classic comedy "The Big Lebowski."
  • C. J. Douglas Brown
    J. Douglas Brown was an American economist and academic who played a key role in shaping U.S. Social Security policy during the New Deal era.
  • D. Red Miller
    Red Miller was an American football coach best known for leading the Denver Broncos to their first Super Bowl appearance in the 1977 season.
  • E. Charles Noland
    Charles Noland is an American actor known for his character roles in film, television, and theater.
  • 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_69ca83907954819096d52a245b635841 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc61ba33c48190a657fc4147a326c0 completed April 1, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba1e46a48190b7a559f9d9bd348d completed April 3, 2026, 1:01 p.m.
NEDg Description generation batch_69cfbade9330819096d4b0eeacdad6da completed April 3, 2026, 1:04 p.m.
NED2 Entity disambiguation (via description) batch_69cfbec2b8888190a0390168fdcef05f completed April 3, 2026, 1:21 p.m.
Created at: March 30, 2026, 6:54 p.m.