Triple
T11023679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orna Kupferman |
E260558
|
entity |
| Predicate | doctoralStudent |
P167
|
FINISHED |
| Object |
Nir Piterman
Nir Piterman is a computer scientist known for his work in formal verification, automata theory, and temporal logic.
|
E901523
|
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: Nir Piterman | Statement: [Orna Kupferman, doctoralStudent, Nir Piterman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nir Piterman Context triple: [Orna Kupferman, doctoralStudent, Nir Piterman]
-
A.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
-
B.
Nir Bergman
Nir Bergman is an Israeli film and television director and screenwriter known for his influential work in character-driven dramas.
-
C.
Avi Kivity
Avi Kivity is an Israeli software engineer best known as the original creator of the KVM virtualization infrastructure for the Linux kernel.
-
D.
Tom Erez
Tom Erez is a researcher in machine learning and control, known for his work on deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
E.
Amnon Niv
Amnon Niv is an Israeli architect best known for designing prominent high-rise buildings, including some of the country's most recognizable skyscrapers.
- 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: Nir Piterman Triple: [Orna Kupferman, doctoralStudent, Nir Piterman]
Generated description
Nir Piterman is a computer scientist known for his work in formal verification, automata theory, and temporal logic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nir Piterman Target entity description: Nir Piterman is a computer scientist known for his work in formal verification, automata theory, and temporal logic.
-
A.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
-
B.
Nir Bergman
Nir Bergman is an Israeli film and television director and screenwriter known for his influential work in character-driven dramas.
-
C.
Avi Kivity
Avi Kivity is an Israeli software engineer best known as the original creator of the KVM virtualization infrastructure for the Linux kernel.
-
D.
Tom Erez
Tom Erez is a researcher in machine learning and control, known for his work on deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
E.
Amnon Niv
Amnon Niv is an Israeli architect best known for designing prominent high-rise buildings, including some of the country's most recognizable skyscrapers.
- 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_69e3a997d7bc8190982467039e0f5504 |
completed | April 18, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_69e3b1dfea348190a61eb19266801ad0 |
completed | April 18, 2026, 4:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b2c65fa081908038cc2f71e49073 |
completed | April 18, 2026, 4:35 p.m. |
Created at: April 8, 2026, 9:25 p.m.