Triple
T5447381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Finale |
E122281
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Lior Raz |
E420182
|
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: Lior Raz | Statement: [Operation Finale, starring, Lior Raz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lior Raz Context triple: [Operation Finale, starring, Lior Raz]
-
A.
Lior Raz
chosen
Lior Raz is an Israeli actor and screenwriter best known as the co-creator and star of the hit television series "Fauda."
-
B.
Avi Kivity
Avi Kivity is an Israeli software engineer best known as the original creator of the KVM virtualization infrastructure for the Linux kernel.
-
C.
Yoav Nir
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.
-
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.
Harel Weinstein
Harel Weinstein is an Israeli-American neuroscientist and biophysicist known for his work on membrane proteins and computational neuroscience.
- 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_69bd4640f52c81909e653ec361f66d76 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd91d0df348190b3de010c87cb6d5d |
completed | March 20, 2026, 6:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf4137ecf881908f3f036457b59281 |
completed | March 22, 2026, 1:09 a.m. |
Created at: March 20, 2026, 2:07 p.m.