Triple

T4142089
Position Surface form Disambiguated ID Type / Status
Subject Lev Okun E89293 entity
Predicate familyName P18 FINISHED
Object Okun
Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
 E415091 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: Okun | Statement: [Lev Okun, familyName, Okun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Okun
Context triple: [Lev Okun, familyName, Okun]
  • A. Barro
    Barro is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
  • B. Engle
    Engle is a surname most notably associated with Joe Engle, an American astronaut and test pilot.
  • C. Reinhart
    Reinhart is a Germanic given name and surname, historically associated with meanings like "brave counsel" and appearing in various European cultural and literary traditions.
  • D. Deaton
    Deaton is a surname most notably associated with individuals such as Nobel Prize–winning economist Angus Deaton.
  • E. Laffer
    Laffer is a surname most prominently associated with Arthur Laffer, the American economist known for the Laffer curve concept in supply-side economics.
  • 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: Okun
Triple: [Lev Okun, familyName, Okun]
Generated description
Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Okun
Target entity description: Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
  • A. Barro
    Barro is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
  • B. Engle
    Engle is a surname most notably associated with Joe Engle, an American astronaut and test pilot.
  • C. Reinhart
    Reinhart is a Germanic given name and surname, historically associated with meanings like "brave counsel" and appearing in various European cultural and literary traditions.
  • D. Deaton
    Deaton is a surname most notably associated with individuals such as Nobel Prize–winning economist Angus Deaton.
  • E. Laffer
    Laffer is a surname most prominently associated with Arthur Laffer, the American economist known for the Laffer curve concept in supply-side economics.
  • 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_69aed95785788190ae75bcf0cd1cafdf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af024b8fe4819098e8f393474363c8 completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576cff6c881909134804ba6f9876d completed March 14, 2026, 2:55 p.m.
NEDg Description generation batch_69b577d391ac8190b6062b1f64e2e7e8 completed March 14, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_69b5787ed214819092fc425152069df9 completed March 14, 2026, 3:02 p.m.
Created at: March 9, 2026, 3:43 p.m.