Triple
T15169582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sleepers |
E362448
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | John Williams |
E20414
|
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: John Williams | Statement: [Sleepers, musicBy, John Williams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Williams Context triple: [Sleepers, musicBy, John Williams]
-
A.
John Williams
John Williams was a colonial New England Puritan minister best known for his captivity narrative recounting his abduction during the 1704 Deerfield raid.
-
B.
John Williams
John Williams was a British actor known for his character roles in mid-20th-century films and television, including classic courtroom dramas and comedies.
-
C.
John Williams
chosen
John Williams is an acclaimed American composer and conductor best known for his iconic film scores for franchises such as Star Wars, Indiana Jones, Harry Potter, and many others.
-
D.
John Williams
John Williams was a 19th-century British missionary renowned for his extensive evangelizing and church-building work in the South Pacific, particularly in Polynesia.
-
E.
John Debney
John Debney is an American film composer known for scoring a wide range of movies and television shows, including major studio productions and acclaimed dramas.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0064dba588190a4341775b472a6d3 |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5e664b081909ef4b6a76976847f |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:08 a.m.