Triple

T1293786
Position Surface form Disambiguated ID Type / Status
Subject Mount Lee E27606 entity
Predicate namedAfter P63 FINISHED
Object Don Lee
Don Lee was a prominent early 20th-century American broadcasting pioneer and automobile dealer whose influence in Los Angeles led to Mount Lee being named in his honor.
 E152694 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: Don Lee | Statement: [Mount Lee, namedAfter, Don Lee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Don Lee
Context triple: [Mount Lee, namedAfter, Don Lee]
  • A. Hancock Lee
    Hancock Lee was a colonial Virginian planter and politician from the prominent Lee family of Virginia.
  • B. Tony Lee
    Tony Lee is an actor known for his role in the Australian drama film "Romper Stomper."
  • C. David Luan
    David Luan is an AI researcher and entrepreneur known for his work on large language models at OpenAI and as co-founder and CEO of Adept AI.
  • D. Hau Lee
    Hau Lee is a prominent operations and supply chain management scholar known for his influential research on global supply networks and his long-standing professorship at Stanford Graduate School of Business.
  • E. John Lee Mahin
    John Lee Mahin was an American screenwriter known for his work on numerous classic Hollywood films from the 1930s through the 1950s.
  • 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: Don Lee
Triple: [Mount Lee, namedAfter, Don Lee]
Generated description
Don Lee was a prominent early 20th-century American broadcasting pioneer and automobile dealer whose influence in Los Angeles led to Mount Lee being named in his honor.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Don Lee
Target entity description: Don Lee was a prominent early 20th-century American broadcasting pioneer and automobile dealer whose influence in Los Angeles led to Mount Lee being named in his honor.
  • A. Hancock Lee
    Hancock Lee was a colonial Virginian planter and politician from the prominent Lee family of Virginia.
  • B. Tony Lee
    Tony Lee is an actor known for his role in the Australian drama film "Romper Stomper."
  • C. David Luan
    David Luan is an AI researcher and entrepreneur known for his work on large language models at OpenAI and as co-founder and CEO of Adept AI.
  • D. Hau Lee
    Hau Lee is a prominent operations and supply chain management scholar known for his influential research on global supply networks and his long-standing professorship at Stanford Graduate School of Business.
  • E. John Lee Mahin
    John Lee Mahin was an American screenwriter known for his work on numerous classic Hollywood films from the 1930s through the 1950s.
  • 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_69a496d6682881909ba658f1c1e0e2b0 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c0f2eb608190a0ac47a73adae19b completed March 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbf243adc8190b8516554701b4290 completed March 8, 2026, 12:13 a.m.
NEDg Description generation batch_69acc2dc5c4c8190b6ba418aaacd1101 completed March 8, 2026, 12:29 a.m.
NED2 Entity disambiguation (via description) batch_69acc3ba816081908892101de3bfbf3e completed March 8, 2026, 12:32 a.m.
Created at: March 1, 2026, 7:51 p.m.