Triple
T10836759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Night School |
E255778
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object | Matthew Kellard |
E891645
|
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: Matthew Kellard | Statement: [Night School, storyBy, Matthew Kellard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Kellard Context triple: [Night School, storyBy, Matthew Kellard]
-
A.
Matthew Kellard
chosen
Matthew Kellard is a screenwriter known for his work on the film "Night School."
-
B.
Mitchell Kennerley
Mitchell Kennerley was an early 20th-century British-born American publisher and bookseller known for his influential role in modernist literature and fine press publishing.
-
C.
Matthew Skemp
Matthew Skemp is a musician best known as a member of the experimental indie rock band Volcano Choir.
-
D.
Matthew Benham
Matthew Benham is an English professional gambler and businessman best known for using data-driven, analytics-based methods to transform Brentford F.C. from the lower leagues into a successful, Premier League club.
-
E.
Matthew Margeson
Matthew Margeson is an American film composer known for his work on action and genre films, including collaborations on scores for 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d746ff70148190b844ab92d796af6c |
completed | April 9, 2026, 6:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2166293808190b7ed1620dfc8a158 |
completed | April 17, 2026, 11:15 a.m. |
Created at: April 8, 2026, 9:19 p.m.