Triple
T21042178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Life with John Thaw |
E518352
|
entity |
| Predicate | hasBiographicalSubject |
P6570
|
FINISHED |
| Object | Sheila Hancock |
—
|
NE NERFINISHED |
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: Sheila Hancock | Statement: [My Life with John Thaw, hasBiographicalSubject, Sheila Hancock]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sheila Hancock Context triple: [My Life with John Thaw, hasBiographicalSubject, Sheila Hancock]
-
A.
Sheila Hancock
chosen
Sheila Hancock is a British actress and author renowned for her extensive work in theatre, television, and film, as well as her appearances as a television presenter and panelist.
-
B.
Betsy Aidem
Betsy Aidem is an American actress known for her work in film, television, and theater.
-
C.
Lesley Garrett
Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
-
D.
Catherine Craig
Catherine Craig was an American film actress active in the 1940s, known for her supporting roles in Hollywood productions.
-
E.
Helen Baxendale
Helen Baxendale is a British actress best known for her roles in the TV series "Cold Feet," "Friends," and various other film and television dramas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf0b27881909d1c5b58be387a74 |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:15 p.m.