Triple
T2330579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moshe Y. Vardi |
E48391
|
entity |
| Predicate | notableStudent |
P4838
|
FINISHED |
| Object |
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
|
E260558
|
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 Kupferman | Statement: [Moshe Y. Vardi, notableStudent, Orna Kupferman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orna Kupferman Context triple: [Moshe Y. Vardi, notableStudent, Orna Kupferman]
-
A.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
B.
Ayelet Zurer
Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
-
C.
Einat Kalisch-Rotem
Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
-
D.
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.
-
E.
Miriam Bienstock
Miriam Bienstock was an American music industry executive and co-founder of Atlantic Records who played a key role in shaping the label’s early business operations and success.
- 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 Kupferman Triple: [Moshe Y. Vardi, notableStudent, Orna Kupferman]
Generated description
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orna Kupferman Target entity description: Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
A.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
B.
Ayelet Zurer
Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
-
C.
Einat Kalisch-Rotem
Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
-
D.
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.
-
E.
Miriam Bienstock
Miriam Bienstock was an American music industry executive and co-founder of Atlantic Records who played a key role in shaping the label’s early business operations and success.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc669956881908b8d9784d6a06acf |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea8786d508190aac531a88fc5076f |
completed | March 9, 2026, 11:01 a.m. |
| NEDg | Description generation | batch_69aea9ca68148190a8773a80c8296a4d |
completed | March 9, 2026, 11:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeaa32e3f48190ada5aab1b05e5aaa |
completed | March 9, 2026, 11:08 a.m. |
Created at: March 4, 2026, 7:50 p.m.