Triple
T13238539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romeo Is Bleeding |
E315217
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Roy Scheider |
E21481
|
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: Roy Scheider | Statement: [Romeo Is Bleeding, starring, Roy Scheider]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roy Scheider Context triple: [Romeo Is Bleeding, starring, Roy Scheider]
-
A.
Roy Scheider
chosen
Roy Scheider was an American actor best known for his intense, everyman performances in films such as "The French Connection," "All That Jazz," and "Jaws."
-
B.
James Caan
James Caan was an American actor best known for his intense, charismatic performances in films such as "The Godfather," "Misery," and "Brian's Song."
-
C.
Jo O'Meara
Jo O'Meara is an English singer and songwriter best known as one of the lead vocalists of the pop group S Club 7.
-
D.
Ned Beatty
Ned Beatty was an acclaimed American character actor known for his powerful supporting roles in films such as "Deliverance," "Network," and "Superman."
-
E.
Richard Dreyfuss
Richard Dreyfuss is an American actor known for his energetic, often neurotic characters in films of the 1970s and 1980s, including an Academy Award–winning performance in "The Goodbye Girl."
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5850ac8190849a51da39efe5be |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a38d09881909e8e3c32e9b1746e |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:23 p.m.