Triple
T14699543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Brodie |
E345256
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Jean Brodie |
E863419
|
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: Jean Brodie | Statement: [Jean Brodie, fullName, Jean Brodie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Brodie Context triple: [Jean Brodie, fullName, Jean Brodie]
-
A.
Jean Brodie
chosen
Jean Brodie is a charismatic and unorthodox schoolteacher in 1930s Edinburgh whose strong influence over her select group of students drives the central drama of the story.
-
B.
John Brodie
John Brodie was a British civil engineer best known for his major infrastructure projects in Liverpool, including pioneering road tunnels and urban planning innovations.
-
C.
John Brodie
John Brodie is a former San Francisco 49ers quarterback who became a prominent American football television commentator.
-
D.
Malcolm Brodie
Malcolm Brodie is a Canadian municipal politician who has served for many years as the mayor of Richmond, British Columbia.
-
E.
Mr Ramsay
Mr Ramsay is the intellectually driven yet emotionally distant philosopher and family patriarch at the center of Virginia Woolf’s novel "To the Lighthouse."
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb604f88081908a677175045496d0 |
completed | April 14, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde191ef6081908434db8d89ad38cb |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:28 a.m.