Triple
T11023678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orna Kupferman |
E260558
|
entity |
| Predicate | doctoralStudent |
P167
|
FINISHED |
| Object |
Sharon Shoham
Sharon Shoham is a computer scientist known for her research in formal methods and verification, and for being a doctoral student of Orna Kupferman.
|
E908131
|
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: Sharon Shoham | Statement: [Orna Kupferman, doctoralStudent, Sharon Shoham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharon Shoham Context triple: [Orna Kupferman, doctoralStudent, Sharon Shoham]
-
A.
Gila Almagor
Gila Almagor is a renowned Israeli actress, author, and film producer often referred to as the "first lady of Israeli cinema and theatre."
-
B.
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
C.
Sarit Kraus
Sarit Kraus is an Israeli computer scientist known for her influential work in artificial intelligence, multi-agent systems, and human-agent interaction.
-
D.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
E.
Daphna Kastner
Daphna Kastner is a Canadian actress, screenwriter, and director known for her work in independent films.
- 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: Sharon Shoham Triple: [Orna Kupferman, doctoralStudent, Sharon Shoham]
Generated description
Sharon Shoham is a computer scientist known for her research in formal methods and verification, and for being a doctoral student of Orna Kupferman.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sharon Shoham Target entity description: Sharon Shoham is a computer scientist known for her research in formal methods and verification, and for being a doctoral student of Orna Kupferman.
-
A.
Gila Almagor
Gila Almagor is a renowned Israeli actress, author, and film producer often referred to as the "first lady of Israeli cinema and theatre."
-
B.
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
C.
Sarit Kraus
Sarit Kraus is an Israeli computer scientist known for her influential work in artificial intelligence, multi-agent systems, and human-agent interaction.
-
D.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
E.
Daphna Kastner
Daphna Kastner is a Canadian actress, screenwriter, and director known for her work in independent films.
- 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797be9f148190a3a967bad5947496 |
completed | April 9, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e462bd90f48190aa3df8725026a9ba |
completed | April 19, 2026, 5:06 a.m. |
| NEDg | Description generation | batch_69e4666f98ac81908b3d3b8a6a8af8c9 |
completed | April 19, 2026, 5:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e46c3f28dc8190a521c00151b01fde |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 8, 2026, 9:25 p.m.