Triple
T20155447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Hunter |
E491550
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Robyn Nevin |
—
|
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: Robyn Nevin | Statement: [Bill Hunter, spouse, Robyn Nevin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robyn Nevin Context triple: [Bill Hunter, spouse, Robyn Nevin]
-
A.
Robyn Nevin
chosen
Robyn Nevin is a prominent Australian actress and theatre director known for her extensive work on stage, film, and television, as well as her leadership roles in major Australian theatre companies.
-
B.
Tania Tapsell
Tania Tapsell is a New Zealand politician and local government leader known for serving as the mayor of Rotorua and for her prominence as a young Māori woman in public office.
-
C.
Claire Jackman
Claire Jackman is a fictional character portrayed by actress Gina Bellman, known from her work in British television and film.
-
D.
Jennifer Hennessy
Jennifer Hennessy is a British actress known for her work in television dramas and comedies, including appearances in series such as Doctor Who and The Office.
-
E.
Kerry Bishé
Kerry Bishé is a New Zealand–born American actress best known for her roles in the film "Argo" and the television series "Halt and Catch Fire."
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667df7ac081908816d2d29e7c6513 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.