Triple
T15026387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lethal Weapon 3 |
E378226
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Rene Russo |
E207120
|
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: Rene Russo | Statement: [Lethal Weapon 3, starring, Rene Russo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rene Russo Context triple: [Lethal Weapon 3, starring, Rene Russo]
-
A.
Rene Russo
chosen
Rene Russo is an American actress and former model known for her roles in films such as "Lethal Weapon 3," "Outbreak," and "Nightcrawler."
-
B.
Lee Russo
Lee Russo is known as the spouse of American television host and film critic Ben Mankiewicz.
-
C.
Laura Kugler
Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
-
D.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
-
E.
Diane Lane
Diane Lane is an American actress acclaimed for her versatile performances in film and television, with a career spanning from childhood roles to major Hollywood productions.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7dfcb508190aec8cd667e27a8ea |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfd954548190b3f7c60d95403f3e |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 2:58 a.m.