Triple
T10526293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Superman IV: The Quest for Peace |
E248313
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Yoram Globus |
E296900
|
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: Yoram Globus | Statement: [Superman IV: The Quest for Peace, producer, Yoram Globus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yoram Globus Context triple: [Superman IV: The Quest for Peace, producer, Yoram Globus]
-
A.
Yoram Globus
chosen
Yoram Globus is an Israeli film producer best known for his leadership of Cannon Films in the 1980s, during which he oversaw numerous action and genre movies.
-
B.
Jeff Levine
Jeff Levine is a film producer best known for his work on the horror drama "Shadow of the Vampire."
-
C.
Gabe Kaplan
Gabe Kaplan is an American comedian, actor, and professional poker player best known for starring as Gabe Kotter on the 1970s sitcom "Welcome Back, Kotter."
-
D.
Larry Hillblom
Larry Hillblom was an American businessman and entrepreneur best known as a co-founder of the global logistics and courier company DHL.
-
E.
Nir Friedman
Nir Friedman is a computer scientist and computational biologist known for his influential work on probabilistic graphical models and their applications to biological data.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f4bbe88190bce7789a56c85671 |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90e26c4908190b77d73c11bee6119 |
completed | April 10, 2026, 2:50 p.m. |
Created at: April 6, 2026, 12:29 p.m.