Triple
T18045383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haim Oron |
E431757
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Haim Oron |
—
|
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: Haim Oron | Statement: [Haim Oron, name, Haim Oron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haim Oron Context triple: [Haim Oron, name, Haim Oron]
-
A.
Haim Oron
chosen
Haim Oron is an Israeli politician and peace activist who led the left-wing Meretz party and served for many years as a member of the Knesset.
-
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.
Itamar Ben-Avi
Itamar Ben-Avi was an early 20th-century Jewish journalist and language activist, best known as the first native speaker of Modern Hebrew and a prominent promoter of its everyday use.
-
D.
Haim Shiran
Haim Shiran is a cinematographer best known for his work on the documentary film "Brave Miss World."
-
E.
Oren Aviv
Oren Aviv is an American film executive and producer known for his work on major Hollywood projects, including contributing to the story for the action-adventure film "National Treasure."
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff202088190ae971879348e2294 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 10:25 a.m.