Triple
T15576559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Mercury |
E374383
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Frances Barber |
E1076242
|
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: Frances Barber | Statement: [Red Mercury, castMember, Frances Barber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frances Barber Context triple: [Red Mercury, castMember, Frances Barber]
-
A.
Frances Barber
chosen
Frances Barber is an English actress known for her extensive work in film, television, and theatre, including roles in productions such as "Film Stars Don’t Die in Liverpool."
-
B.
Wendy Hiller
Wendy Hiller was an acclaimed English stage and film actress known for her nuanced, often understated performances in classics such as "Pygmalion" and "Separate Tables."
-
C.
Lesley Garrett
Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
-
D.
Ann Pugh
Ann Pugh is known as the spouse of British Liberal Democrat politician John David Pugh, who served as Member of Parliament for Southport.
-
E.
Betsy Aidem
Betsy Aidem is an American actress known for her work in film, television, and theater.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e22c89081909b1ec0cd36a1ef45 |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001799fbac8190b75a48a8c63e3381 |
completed | May 10, 2026, 5:28 a.m. |
Created at: April 10, 2026, 4:10 a.m.