Triple
T10797760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RFC 7539 |
E254754
|
entity |
| Predicate | hasAuthor |
P4244
|
FINISHED |
| Object | Yoav 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: Yoav Nir | Statement: [RFC 7539, hasAuthor, Yoav Nir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yoav Nir Context triple: [RFC 7539, hasAuthor, Yoav 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.
Yoav Kutner
Yoav Kutner is a technology entrepreneur best known as a co-founder of the Magento e-commerce platform and later founder of Varien Inc.
-
C.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
-
D.
Dov Moran
Dov Moran is an Israeli entrepreneur and inventor best known as the creator of the USB flash drive and a pioneer in the flash memory industry.
-
E.
Nir Bergman
Nir Bergman is an Israeli film and television director and screenwriter known for his influential work in character-driven dramas.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73333dc4081909faa40c10bce2735 |
completed | April 9, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de566352608190ab15e3a4b690c9a5 |
completed | April 14, 2026, 2:59 p.m. |
Created at: April 8, 2026, 9:17 p.m.