Triple
T17693916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexander Pritzel |
E441108
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Tom Erez |
—
|
NE NERFINISHED |
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: Tom Erez | Statement: [Alexander Pritzel, coAuthorWith, Tom Erez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Erez Context triple: [Alexander Pritzel, coAuthorWith, Tom Erez]
-
A.
Tom Erez
chosen
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).
-
B.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
-
C.
Nir Piterman
Nir Piterman is a computer scientist known for his work in formal verification, automata theory, and temporal logic.
-
D.
Avi Kivity
Avi Kivity is an Israeli software engineer best known as the original creator of the KVM virtualization infrastructure for the Linux kernel.
-
E.
Lior Raz
Lior Raz is an Israeli actor and screenwriter best known as the co-creator and star of the hit television series "Fauda."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4715485d88190b9b6f347ff85d7c7 |
completed | April 19, 2026, 6:08 a.m. |
Created at: April 10, 2026, 10:04 a.m.