Triple

T1046569
Position Surface form Disambiguated ID Type / Status
Subject Meyer Lansky E22593 entity
Predicate givenName P17 FINISHED
Object Meyer
Meyer is a given name most famously associated with Meyer Lansky, a major organized crime figure in the United States during the 20th century.
 E119689 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: Meyer | Statement: [Meyer Lansky, givenName, Meyer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Meyer
Context triple: [Meyer Lansky, givenName, Meyer]
  • A. Müller
    Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
  • B. Blaustein
    Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
  • C. Scheider
    Scheider is the surname of American actor Roy Scheider, best known for his role as Chief Brody in the film "Jaws."
  • D. Morgenstern
    Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
  • E. Martz
    Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
  • 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: Meyer
Triple: [Meyer Lansky, givenName, Meyer]
Generated description
Meyer is a given name most famously associated with Meyer Lansky, a major organized crime figure in the United States during the 20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Meyer
Target entity description: Meyer is a given name most famously associated with Meyer Lansky, a major organized crime figure in the United States during the 20th century.
  • A. Müller
    Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
  • B. Blaustein
    Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
  • C. Scheider
    Scheider is the surname of American actor Roy Scheider, best known for his role as Chief Brody in the film "Jaws."
  • D. Morgenstern
    Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
  • E. Martz
    Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
  • 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_69a493d91478819094cc01fb65564bc1 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b84bb0048190badf6d2f7f684d99 completed March 1, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bcb65d08190b2ea04b6de3bb39b completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3ce6228881908f429cb0a016a17a completed March 7, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_69ac3d3ed140819087ede15c555e2f4d completed March 7, 2026, 2:59 p.m.
Created at: March 1, 2026, 7:42 p.m.