Triple

T2169601
Position Surface form Disambiguated ID Type / Status
Subject Edmund M. Clarke E46990 entity
Predicate notableStudent P4838 FINISHED
Object Joël Ouaknine
Joël Ouaknine is a computer scientist known for his work in formal verification, automata theory, and the analysis of infinite-state and probabilistic systems.
E280308 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: Joël Ouaknine | Statement: [Edmund M. Clarke, notableStudent, Joël Ouaknine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joël Ouaknine
Context triple: [Edmund M. Clarke, notableStudent, Joël Ouaknine]
  • A. Antoine Nahas
    Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
  • B. Gil Avérous
    Gil Avérous is a French politician who serves as the mayor of the city of Châteauroux.
  • C. Fabien Roussel
    Fabien Roussel is a French politician who has served as the national secretary and presidential candidate of the French Communist Party.
  • D. Jean-Claude Kalache
    Jean-Claude Kalache is a cinematographer and lighting artist best known for his work on Pixar animated films such as Monsters, Inc.
  • E. Laurent Durand
    Laurent Durand was an 18th-century French publisher and bookseller best known for helping to produce the influential Enlightenment-era Encyclopédie.
  • 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: Joël Ouaknine
Triple: [Edmund M. Clarke, notableStudent, Joël Ouaknine]
Generated description
Joël Ouaknine is a computer scientist known for his work in formal verification, automata theory, and the analysis of infinite-state and probabilistic systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Joël Ouaknine
Target entity description: Joël Ouaknine is a computer scientist known for his work in formal verification, automata theory, and the analysis of infinite-state and probabilistic systems.
  • A. Antoine Nahas
    Antoine Nahas was a Lebanese architect best known for designing the National Museum of Beirut, a landmark institution of Lebanon’s cultural heritage.
  • B. Gil Avérous
    Gil Avérous is a French politician who serves as the mayor of the city of Châteauroux.
  • C. Fabien Roussel
    Fabien Roussel is a French politician who has served as the national secretary and presidential candidate of the French Communist Party.
  • D. Jean-Claude Kalache
    Jean-Claude Kalache is a cinematographer and lighting artist best known for his work on Pixar animated films such as Monsters, Inc.
  • E. Laurent Durand
    Laurent Durand was an 18th-century French publisher and bookseller best known for helping to produce the influential Enlightenment-era Encyclopédie.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbeaeb58881908ad34f7b253bac2a completed March 7, 2026, 5:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6533d4b88190ac722cd1ebfa3d8f completed March 10, 2026, 12:26 a.m.
NEDg Description generation batch_69af66c7e25c819095c993553b428385 completed March 10, 2026, 12:33 a.m.
NED2 Entity disambiguation (via description) batch_69af671900e081908e9b9e24aae651e2 completed March 10, 2026, 12:34 a.m.
Created at: March 4, 2026, 7:45 p.m.