Triple
T6091914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In Treatment |
E135784
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Ori Sivan |
E334922
|
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: Ori Sivan | Statement: [In Treatment, developer, Ori Sivan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ori Sivan Context triple: [In Treatment, developer, Ori Sivan]
-
A.
Roni Ashuri
Roni Ashuri is an entrepreneur and technology executive best known as a founder of the Israeli high-performance networking company Mellanox Technologies.
-
B.
Yoni Brenner
Yoni Brenner is a screenwriter and humorist known for his work on animated films, including contributing to the screenplay of "Rio 2."
-
C.
Omri Katz
Omri Katz is an American former child actor best known for playing Max Dennison in the 1993 Disney film "Hocus Pocus."
-
D.
Uri Sivan
chosen
Uri Sivan is an Israeli physicist and academic leader known for his research in nanotechnology and for serving as president of the Technion – Israel Institute of Technology.
-
E.
Ariel Porat
Ariel Porat is an Israeli legal scholar and academic leader who serves as president of Tel Aviv University.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ab7324819086d4708e6f9391c0 |
completed | March 22, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14153e95081909e0d77cb48733561 |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:12 p.m.