Triple

T4236938
Position Surface form Disambiguated ID Type / Status
Subject Lance Berkman E94716 entity
Predicate familyName P18 FINISHED
Object Berkman
Berkman is a surname most prominently associated with former Major League Baseball All-Star Lance Berkman.
 E423847 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: Berkman | Statement: [Lance Berkman, familyName, Berkman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Berkman
Context triple: [Lance Berkman, familyName, Berkman]
  • A. Kogod
    Kogod is the business school of American University in Washington, D.C., offering undergraduate and graduate programs in business and management.
  • B. Berk
    Berk is a Turkish surname shared by various individuals, including the notable poet İlhan Berk.
  • C. Kleinburg
    Kleinburg is a historic, affluent village within the city of Vaughan, Ontario, known for its charming main street and the McMichael Canadian Art Collection.
  • D. Kita Campus
    Kita Campus is the main northern campus of Hokkaido University in Sapporo, Japan, housing key faculties and research facilities including the Graduate School of Science.
  • E. Harkness
    Harkness is a surname of Scottish origin borne by various notable individuals in fields such as business, philanthropy, and the arts.
  • 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: Berkman
Triple: [Lance Berkman, familyName, Berkman]
Generated description
Berkman is a surname most prominently associated with former Major League Baseball All-Star Lance Berkman.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Berkman
Target entity description: Berkman is a surname most prominently associated with former Major League Baseball All-Star Lance Berkman.
  • A. Kogod
    Kogod is the business school of American University in Washington, D.C., offering undergraduate and graduate programs in business and management.
  • B. Berk
    Berk is a Turkish surname shared by various individuals, including the notable poet İlhan Berk.
  • C. Kleinburg
    Kleinburg is a historic, affluent village within the city of Vaughan, Ontario, known for its charming main street and the McMichael Canadian Art Collection.
  • D. Kita Campus
    Kita Campus is the main northern campus of Hokkaido University in Sapporo, Japan, housing key faculties and research facilities including the Graduate School of Science.
  • E. Harkness
    Harkness is a surname of Scottish origin borne by various notable individuals in fields such as business, philanthropy, and the arts.
  • 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_69b34537cc6481909cd0a96acbb33ef7 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e7422a88190955f5f4347fa80d2 completed March 12, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a86996f48190987d3ac234a9b7f4 completed March 14, 2026, 6:26 p.m.
NEDg Description generation batch_69b5a9f58de48190b6f2f56804bc6d30 completed March 14, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_69b5aabd2080819091d65362cf02120b completed March 14, 2026, 6:36 p.m.
Created at: March 12, 2026, 11:05 p.m.