Triple
T3748624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E. Allen Emerson |
E81270
|
entity |
| Predicate | doctoralStudent |
P167
|
FINISHED |
| Object | Orna Kupferman |
E260558
|
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 Kupferman | Statement: [E. Allen Emerson, doctoralStudent, Orna Kupferman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orna Kupferman Context triple: [E. Allen Emerson, doctoralStudent, Orna Kupferman]
-
A.
Orna Kupferman
chosen
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
B.
Daphna Kastner
Daphna Kastner is a Canadian actress, screenwriter, and director known for her work in independent films.
-
C.
Orna Grumberg
Orna Grumberg is a prominent computer scientist known for her contributions to formal verification and model checking.
-
D.
Basya Cohen
Basya Cohen, better known as Betty Comden, was an American lyricist, screenwriter, and performer famed for her influential work on classic Broadway musicals and Hollywood films.
-
E.
Ayelet Zurer
Ayelet Zurer is an Israeli actress known internationally for her roles in films such as "Angels & Demons," "Munich," and "Man of Steel."
- 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_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb6bf95c81909796fbc84995ae05 |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db2f5e9881908c10feafbb569f48 |
completed | March 14, 2026, 3:51 a.m. |
Created at: March 8, 2026, 3:35 p.m.