Triple
T13512596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Bates |
E322674
|
entity |
| Predicate | loyalTo |
P1201
|
FINISHED |
| Object | John Bates |
E322673
|
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 Bates | Statement: [Anna Bates, loyalTo, John Bates]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Bates Context triple: [Anna Bates, loyalTo, John Bates]
-
A.
John Bates
chosen
John Bates is a central valet and later estate manager in the British period drama series "Downton Abbey," known for his loyalty, quiet strength, and complex personal struggles.
-
B.
John Bates
John Bates is a fictional character who appears in D. H. Lawrence’s short story "Odour of Chrysanthemums," contributing to its exploration of working-class life and emotional estrangement.
-
C.
Russell Sage
Russell Sage was a 19th-century American financier, railroad executive, and politician known for his immense wealth and later philanthropic legacy.
-
D.
Henry Wells
Henry Wells was a 19th-century American businessman and express pioneer who co-founded both American Express and Wells Fargo.
-
E.
Roger W. Babson
Roger W. Babson was an American entrepreneur, economist, and business theorist known for his influential market forecasts and for establishing educational and financial institutions.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf87ca288190a147fbdb2f90985f |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f8239c481909faf5a9c403b55f2 |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 9:44 p.m.