Triple
T10387594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deacon |
E244803
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Deacon (Waterworld film character) |
E244803
|
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: Deacon (Waterworld film character) | Statement: [Deacon, basedOn, Deacon (Waterworld film character)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deacon (Waterworld film character) Context triple: [Deacon, basedOn, Deacon (Waterworld film character)]
-
A.
Jack Deerson
Jack Deerson is a cinematographer best known for his work on the 1971 road movie "Two-Lane Blacktop."
-
B.
Deacon
Deacon is a small rural community located within the township of Bonnechere Valley in eastern Ontario, Canada.
-
C.
Deacon
chosen
Deacon is the main villain and ruthless leader of the Smokers in the WaterWorld: A Live Sea War Spectacular stunt show.
-
D.
Deacon
Deacon is the nickname of Hall of Fame NFL defensive end Deacon Jones, renowned as one of the greatest pass rushers in football history.
-
E.
Jed Water
Jed Water is a river in the Scottish Borders that flows past the historic town of Jedburgh and its medieval abbey.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9a59d688190b1da1ea0ed48fafa |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795aa3afc8190aed1ca11556ae34f |
completed | April 9, 2026, 12:03 p.m. |
Created at: April 6, 2026, 12:05 p.m.