Triple
T12889686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleanor Bron |
E308324
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Eleanor Bron |
E308324
|
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: Eleanor Bron | Statement: [Eleanor Bron, name, Eleanor Bron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eleanor Bron Context triple: [Eleanor Bron, name, Eleanor Bron]
-
A.
Eleanor Bron
chosen
Eleanor Bron is a British actress and writer known for her distinctive, often imperious screen presence in film, television, and theatre.
-
B.
Edith Lesley
Edith Lesley was an American educator and founder of the teacher-training institution that evolved into Lesley University in Cambridge, Massachusetts.
-
C.
Rita Tushingham
Rita Tushingham is an English actress known for her distinctive, wide-eyed look and acclaimed performances in 1960s British cinema, including key roles in films of the British New Wave.
-
D.
Lesley Garrett
Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
-
E.
Anna Chancellor
Anna Chancellor is a British actress known for her work in film, television, and theatre, including notable roles in productions such as "Four Weddings and a Funeral" and various acclaimed TV dramas.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714581988190afc720ffd7797860 |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7265af6cc81908837c80797a8e704 |
completed | May 3, 2026, 10:41 a.m. |
Created at: April 9, 2026, 5:39 p.m.