Triple
T2271425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RFC 8439 |
E50666
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Y. Nir |
E254754
|
NE FINISHED |
How this triple was built (2 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: Y. Nir | Statement: [RFC 8439, author, Y. Nir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Y. Nir Context triple: [RFC 8439, author, Y. Nir]
-
A.
Yoav Nir
chosen
Yoav Nir is a computer scientist and cryptography expert known for his work on internet security standards, including co-authoring RFC 7539 on the ChaCha20 and Poly1305 encryption algorithms.
-
B.
Doron Peled
Doron Peled is a computer scientist known for his contributions to formal methods and model checking, particularly in collaboration with Edmund M. Clarke.
-
C.
Ephraim Katzir
Ephraim Katzir was an Israeli biophysicist and politician who served as the fourth President of Israel and was renowned for his pioneering work in the field of protein chemistry.
-
D.
Amnon Yariv
Amnon Yariv is an Israeli-American physicist and electrical engineer renowned for his pioneering contributions to lasers, optoelectronics, and photonics theory.
-
E.
Ziv Aviram
Ziv Aviram is an Israeli entrepreneur and co-founder of Mobileye, known for pioneering advanced driver-assistance and autonomous driving technologies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a88b05910c8190a9a2b1ff230c85f9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1c0de488190876b644cdaa41637 |
completed | March 7, 2026, 6:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae960554108190a202855ad0d3caaa |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:48 p.m.