Triple

T336398
Position Surface form Disambiguated ID Type / Status
Subject Seán Lester E6736 entity
Predicate familyName P18 FINISHED
Object Lester
Lester is a surname of Irish origin borne by various notable individuals, including the diplomat Seán Lester.
 E58985 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: Lester | Statement: [Seán Lester, familyName, Lester]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lester
Context triple: [Seán Lester, familyName, Lester]
  • A. Lester
    Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
  • B. Leslie
    Leslie is a small town in Fife, Scotland, situated near Glenrothes and known historically for its textile and papermaking industries.
  • C. Earl
    An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
  • D. Leonard
    Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
  • E. Stringer Bell
    Stringer Bell is a central character in the television series "The Wire," a calculating drug kingpin who studies business and economics to run his criminal organization like a legitimate enterprise.
  • 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: Lester
Triple: [Seán Lester, familyName, Lester]
Generated description
Lester is a surname of Irish origin borne by various notable individuals, including the diplomat Seán Lester.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lester
Target entity description: Lester is a surname of Irish origin borne by various notable individuals, including the diplomat Seán Lester.
  • A. Lester
    Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
  • B. Leslie
    Leslie is a small town in Fife, Scotland, situated near Glenrothes and known historically for its textile and papermaking industries.
  • C. Earl
    An Earl is a noble rank in the British and some European peerage systems, historically positioned below a marquess and above a viscount.
  • D. Leonard
    Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
  • E. Stringer Bell
    Stringer Bell is a central character in the television series "The Wire," a calculating drug kingpin who studies business and economics to run his criminal organization like a legitimate enterprise.
  • 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_69a2e79434908190a9d5afe415153ad9 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eac950388190829875a07d821ddf completed Feb. 28, 2026, 1:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a462f0d0f081909615f95458d6d267 completed March 1, 2026, 4:01 p.m.
NEDg Description generation batch_69a4641055a08190a9bd874c5bb6379e completed March 1, 2026, 4:06 p.m.
NED2 Entity disambiguation (via description) batch_69a464720a20819096cb631fcb539620 completed March 1, 2026, 4:08 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.