Triple
T16276730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liebowitz |
E395147
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Ronald D. Liebowitz |
E92521
|
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: Ronald D. Liebowitz | Statement: [Liebowitz, hasNotableBearer, Ronald D. Liebowitz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ronald D. Liebowitz Context triple: [Liebowitz, hasNotableBearer, Ronald D. Liebowitz]
-
A.
Ronald D. Liebowitz
chosen
Ronald D. Liebowitz is an American academic leader and former Middlebury College president who serves as the president of Brandeis University.
-
B.
Robert D. Wachs
Robert D. Wachs was a film and television producer best known for his work on the hit comedy "Coming to America."
-
C.
Richard P. Levine
Richard P. Levine is a film producer best known for his work on the World War II epic "A Bridge Too Far."
-
D.
Robert D. Siegel
Robert D. Siegel is an American screenwriter and director best known for writing character-driven dramas such as the film "The Wrestler."
-
E.
Robert Wachs
Robert Wachs is a film producer best known for his work on the comedy film "Delirious."
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460ea5a4819099f779288a754c8d |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017c2b9688190b96d62d83a03f158 |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:05 a.m.