Triple
T12358285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Very Harold & Kumar 3D Christmas |
E294666
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | William Ross |
E484422
|
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: William Ross | Statement: [A Very Harold & Kumar 3D Christmas, musicBy, William Ross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Ross Context triple: [A Very Harold & Kumar 3D Christmas, musicBy, William Ross]
-
A.
William Ross
chosen
William Ross is an American composer, orchestrator, and conductor known for his work on numerous film scores and collaborations with major Hollywood productions.
-
B.
Frank Ross
Frank Ross was an American film producer known for his work on major mid-20th-century Hollywood productions.
-
C.
John Graham
John Graham is an Australian Labor Party politician who serves as a senior minister in the New South Wales government under Premier Chris Minns.
-
D.
James Gillespie
James Gillespie was a 19th-century Texas figure, likely a politician or military leader, honored as the namesake of Gillespie County.
-
E.
William Vans Murray
William Vans Murray was an American diplomat and politician best known for helping negotiate peace with France during the Quasi-War at the turn of the 19th century.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8e64dc81908c2242c68cd1b86e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f66854281c8190bd8d21d501cddb89 |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:54 p.m.