Triple
T14147232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Gwynne |
E350582
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sarah Gwynne |
E350582
|
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: Sarah Gwynne | Statement: [Sarah Gwynne, name, Sarah Gwynne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Gwynne Context triple: [Sarah Gwynne, name, Sarah Gwynne]
-
A.
Sarah Gwynne
chosen
Sarah Gwynne was an 18th-century Welsh woman best known as the wife of Methodist hymn-writer and co-founder Charles Wesley, and a supporter of the early Methodist movement.
-
B.
Sarah Greer
Sarah Greer is a British academic and higher education leader who serves as Vice-Chancellor of the University of Winchester.
-
C.
Susanna Moore
Susanna Moore is an American novelist and screenwriter best known for her psychologically intense literary fiction, including the novel "In the Cut."
-
D.
Kathryn Prescott
Kathryn Prescott is an English actress best known for her roles in the TV series "Skins" and the film "A Dog’s Journey."
-
E.
Lucinda McCullough
Lucinda McCullough was the wife of renowned American bridge engineer Conde McCullough, associated with his personal and family life during his career in Oregon.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de612266248190a8591b646fe30ae6 |
completed | April 14, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7e86820819099d6e3d3d4229f0d |
completed | May 7, 2026, 8:36 p.m. |
Created at: April 10, 2026, 12:54 a.m.