Triple
T11050216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toy Soldiers |
E261224
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Andrew Divoff |
E751562
|
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: Andrew Divoff | Statement: [Toy Soldiers, starring, Andrew Divoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrew Divoff Context triple: [Toy Soldiers, starring, Andrew Divoff]
-
A.
Andrew Divoff
chosen
Andrew Divoff is a Venezuelan-born character actor best known for playing intense villains in horror and action films, including the Wishmaster series.
-
B.
Andrew Rabinovich
Andrew Rabinovich is a computer scientist and researcher known for his contributions to computer vision and deep learning, including influential work at Google.
-
C.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
D.
Steven Fierberg
Steven Fierberg is an American cinematographer known for his work on feature films and television series, including the romantic drama "Love & Other Drugs."
-
E.
Michael Aronov
Michael Aronov is an American actor known for his work in film, television, and theater, including a role in the historical drama "Operation Finale."
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79868c78881908c8e3672c05ae7ec |
completed | April 9, 2026, 12:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b78e7ec819093e5e631197ed295 |
completed | May 2, 2026, 7:07 p.m. |
Created at: April 8, 2026, 9:26 p.m.