Triple
T10201083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Book |
E238881
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Jim Burke |
E310593
|
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: Jim Burke | Statement: [Green Book, producer, Jim Burke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Burke Context triple: [Green Book, producer, Jim Burke]
-
A.
Jim Burke
chosen
Jim Burke is an American film producer known for his work on acclaimed movies such as "The Descendants" and "Green Book."
-
B.
Dave Burke
Dave Burke is a central character in the 1959 film noir "Odds Against Tomorrow," depicted as a former police officer who masterminds a high-stakes bank heist.
-
C.
Phil Burke
Phil Burke is a Canadian actor best known for his role as Mickey McGinnes on the television drama series "Hell on Wheels."
-
D.
Kevin Burke
Kevin Burke is an acclaimed Irish fiddler best known for his influential work in traditional Irish music and collaborations with prominent folk groups and artists.
-
E.
Matt Burke
Matt Burke is a retired English teacher and key supporting character in Stephen King’s novel "’Salem’s Lot," who helps protagonist Ben Mears confront the town’s growing vampire threat.
- 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_69ca84e1ea088190b38162e43d4cfa8f |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdee40cb7481908a1bf4d5636eb8ef |
completed | April 2, 2026, 4:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f6e73a2881908563e9e6a02df944 |
completed | April 9, 2026, 12:46 a.m. |
Created at: March 30, 2026, 9:14 p.m.