Triple
T15078043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzan Farmer |
E380058
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Suzan Farmer |
E380058
|
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: Suzan Farmer | Statement: [Suzan Farmer, name, Suzan Farmer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzan Farmer Context triple: [Suzan Farmer, name, Suzan Farmer]
-
A.
Suzan Farmer
chosen
Suzan Farmer was a British actress best known for her roles in 1960s Hammer horror films and various British television series.
-
B.
Sue Johnston
Sue Johnston is an English actress best known for her roles in television dramas such as "Brookside," "The Royle Family," and "Waking the Dead."
-
C.
Rita Farmer
Rita Farmer was the sister of American actress Frances Farmer, known primarily in relation to her sibling’s troubled Hollywood career and life story.
-
D.
Susan Duerden
Susan Duerden is a British actress known for her voice and screen roles in film, television, and audio productions.
-
E.
Marilee Fiebig
Marilee Fiebig is an American attorney and immigration lawyer who has also worked as a fashion and entertainment industry executive.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dff7fe5a208190823900b25e298dab |
completed | April 15, 2026, 8:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5d4f6a48190aeb42341b0c395a7 |
completed | May 9, 2026, 3:11 a.m. |
Created at: April 10, 2026, 3:03 a.m.