Triple
T8449240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rob Roy |
E199759
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Shirley Henderson |
E256826
|
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: Shirley Henderson | Statement: [Rob Roy, castMember, Shirley Henderson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shirley Henderson Context triple: [Rob Roy, castMember, Shirley Henderson]
-
A.
Shirley Henderson
chosen
Shirley Henderson is a Scottish actress known for her distinctive voice and roles in films such as the Bridget Jones series and the Harry Potter franchise.
-
B.
Sheila Hancock
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.
-
C.
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."
-
D.
Betsy Aidem
Betsy Aidem is an American actress known for her work in film, television, and theater.
-
E.
Lesley Garrett
Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe445b7988190b53ae45070c70d1d |
completed | March 31, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb52978481909c60e43abd496746 |
completed | April 2, 2026, 6:54 p.m. |
Created at: March 30, 2026, 6:09 p.m.