Triple
T21446763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amadeus |
E529096
|
entity |
| Predicate | broadwayProducer |
P33162
|
FINISHED |
| Object | Harry Rigby |
—
|
NE NERFINISHED |
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: Harry Rigby | Statement: [Amadeus, broadwayProducer, Harry Rigby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harry Rigby Context triple: [Amadeus, broadwayProducer, Harry Rigby]
-
A.
Harry Rigby
chosen
Harry Rigby was an American theatrical producer best known for his work on Broadway musical revivals in the mid-20th century.
-
B.
Arthur Rigby
Arthur Rigby was a British actor best known for his long-running role as a police sergeant in the classic BBC television series "Dixon of Dock Green."
-
C.
Harry Gribbon
Harry Gribbon was an American vaudeville and film comedian best known for his slapstick roles in silent and early sound comedies.
-
D.
Harry Rowlands
Harry Rowlands is known primarily as the husband of June Rowlands, the first female mayor of Toronto.
-
E.
Harry Tibbett
Harry Tibbett is a fictional British police inspector and the protagonist of a series of classic detective novels by Patricia Moyes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9cea7bc81909ee3e1cdeda1fe7e |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:06 p.m.