Triple

T2169592
Position Surface form Disambiguated ID Type / Status
Subject Edmund M. Clarke E46990 entity
Predicate coAuthor P398 FINISHED
Object Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
 E258561 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: Orna Grumberg | Statement: [Edmund M. Clarke, coAuthor, Orna Grumberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orna Grumberg
Context triple: [Edmund M. Clarke, coAuthor, Orna Grumberg]
  • A. Einat Kalisch-Rotem
    Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
  • B. Rachel Cohen-Kagan
    Rachel Cohen-Kagan was an Israeli politician, women's rights activist, and one of the signatories of Israel's Declaration of Independence.
  • C. Orly Sud
    Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
  • D. Ayelet Zurer
    Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
  • E. Marla Lerner Tanenbaum
    Marla Lerner Tanenbaum is an American philanthropist and baseball executive best known as a principal owner of the Washington Nationals and for her leadership in charitable and community initiatives.
  • 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: Orna Grumberg
Triple: [Edmund M. Clarke, coAuthor, Orna Grumberg]
Generated description
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orna Grumberg
Target entity description: Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
  • A. Einat Kalisch-Rotem
    Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
  • B. Rachel Cohen-Kagan
    Rachel Cohen-Kagan was an Israeli politician, women's rights activist, and one of the signatories of Israel's Declaration of Independence.
  • C. Orly Sud
    Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
  • D. Ayelet Zurer
    Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
  • E. Marla Lerner Tanenbaum
    Marla Lerner Tanenbaum is an American philanthropist and baseball executive best known as a principal owner of the Washington Nationals and for her leadership in charitable and community initiatives.
  • 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_69ae95f487708190b06a536dd20a069a completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae9694c390819095b3e935224902d3 completed March 9, 2026, 9:44 a.m.
NED2 Entity disambiguation (via description) batch_69ae9a5b2a04819091b102c51a5b2af4 completed March 9, 2026, 10 a.m.
Created at: March 4, 2026, 7:45 p.m.