Triple
T13744427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop |
E330175
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | John Bishop |
E422394
|
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 Bishop | Statement: [Bishop, hasNotableBearer, John Bishop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Bishop Context triple: [Bishop, hasNotableBearer, John Bishop]
-
A.
John Bishop
chosen
John Bishop is an English stand-up comedian, actor, and television presenter known for his energetic storytelling style and appearances on British panel shows and dramas.
-
B.
John Bishop
John Bishop is the son of Maurice Bishop, the revolutionary leader and former Prime Minister of Grenada.
-
C.
Mark Curtis
Mark Curtis is a British historian and author known for his critical works on UK foreign policy and Western interventionism.
-
D.
John Brydon
John Brydon was a British architect of the late 19th and early 20th centuries, known for designing prominent public buildings in London.
-
E.
John Grover
John Grover is a film editor best known for his work on action and thriller movies, including the James Bond film series.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b06b7d1481909a318086d76d3af0 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:08 p.m.