Triple
T10304193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Model Checking |
E241708
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Orna Grumberg |
E258561
|
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: Orna Grumberg | Statement: [Model Checking, author, Orna Grumberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orna Grumberg Context triple: [Model Checking, author, Orna Grumberg]
-
A.
Orna Grumberg
chosen
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
B.
Orly Goldwasser
Orly Goldwasser is an Israeli Egyptologist and epigrapher known for her influential research on early alphabetic writing and the interpretation of ancient inscriptions.
-
C.
Gila Almagor
Gila Almagor is a renowned Israeli actress, author, and film producer often referred to as the "first lady of Israeli cinema and theatre."
-
D.
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
E.
Einat Kalisch-Rotem
Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
- 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_69d381ac38808190a8ca7457c85b625b |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d308f034819098da69b963eb8a02 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d58416081909a010e905d70e934 |
completed | April 9, 2026, 3:30 a.m. |
Created at: April 6, 2026, 11:45 a.m.